# Strategic Complexity, by Ace Eddleman A free online book by Ace Eddleman on solving hard problems in unpredictable environments, built from complexity science, ecology and evolutionary biology. Canonical site: https://www.strategiccomplexity.com/ --- # Ecology Source: https://www.strategiccomplexity.com/ecology/ Section: Systems & Complexity *[Figure: A natural ecosystem in action. Source]* The default mode of perception is through the lens of you, the individual. This filters out into every form of analysis, where the focus is on either you or other specific agents. Just consider the entire self-help industry, or the popularity of biographies about successful business people. In most cases, the idea is to zero in on the intelligence and capabilities of a person or a very small group of people, with little regard given to other factors. It’s simple, easy and frictionless—it plays right into our expectations. When you only need to consider one variable, your brain lets out a metaphorical sigh of relief since it doesn’t have to do much thinking. An alternative that is far more accurate, especially now, is the ecological way of thinking. Ecologists know that you can’t accurately describe an organism without also describing how it interacts with its environment. It is from this simple, but brilliant, thread that everything else follows. ## Ecological Scales Ecologists use scales that determine how any given study is conducted: * At the _individual_ level, a single organism within an environment is studied. * At the _population_ level, a group of organisms of the same species in a shared environment is studied. * At the _community_ level, groups of at least two different species in the same environment are studied. * At the _ecosystem_ level, communities and their physical environments are studied together. * At the _biome_ level, every ecosystem of a specific type across the planet is studied. * At the _biosphere_ level, every ecosystem on the planet is studied. When you look at these scales, you realize that the individual level is the least informative and least useful in many cases. Not to say that individuals don’t have anything worth studying—they certainly do—but if you want to get to the real meat of how the world works, you must go further up the hierarchy. The ideal level for most analysis is the **ecosystem** level. This gives you an excellent mix of low-level and high-level details for figuring out how the world works. Ecosystem analysis is that goldilocks zone of “just right” when it comes to useful, generalizable data. It provides that appropriate level of context where interactions between different groups and their shared environment shine. The levels below tend to be too granular, and the levels above too general. Regardless of what level of analysis is chosen, _there’s always a relationship to the environment_, which includes not just an organism's physical surroundings, but other organisms as well. Environment shapes the behavior of organisms, who in turn shape the environment in the struggle for survival. All of this creates a feedback loop. The ecological stance thus reminds us of a fundamental truth: **nothing exists in a vacuum**. ## Ecological Problem Solving While this may seem elementary, it’s amazing how few people apply this style of thinking when trying to solve complex problems. It’s so much simpler and easier to focus on the individual level: you can allocate responsibility for outcomes without much thought to what external factors influenced a given set of behaviors. Cause and effect chains are easy to figure out when you have so few factors to consider. A common example is to look at why your bank account isn't what you want it to be and conclude that you simply need to work harder. While it's easier to think about, you're better off taking into account other factors, such as the political situation at the office, current market conditions, profitability of the sector, and so on. Maybe at the end of the analysis you conclude that you really aren't putting enough effort in, but starting and stopping at the level of "me" is rarely a good idea. This tendency provides for quick and psychologically satisfying answers that are dangerously incomplete or even flat out wrong. Once again, this stems from our natural tendencies and the fact that we evolved in much simpler environments. When you only interact with a few other humans and a small set of other species in an environment you’re intimately familiar with, the individual level of thought may very well be enough for you to survive and reproduce—and that’s all that matters in the evolutionary struggle. We now live in a world where the environment changes rapidly and presents a level of complexity our brains are not equipped to deal with. Cause and effect chains are blurry, if they exist at all, and our notions about how impactful individuals can be are outdated at best. ## Start With Environment So the lesson here is simple: for any given problem you want to solve, you must move beyond the individual level and incorporate environmental interactions. If you learn nothing else from our time together, this can still improve your perception of problems dramatically. Rather than trying to read more books, watch more videos, and otherwise fill your own head with data, **you should begin with the world around you**. There’s a quote by William Mace that you should keep in mind from this point forward: > _Ask not what's inside your head but what your head's inside of._ Consider the example of a drug addict. Much of their addiction is fed by the environment they find themselves in. That environment is filled with peer pressure, desperation, crime, and all kinds of other feedback loops that keep them locked in a downward spiral. The first step in any recovery from addiction is therefore to remove the addicted person from the environment which feeds their habit. It's convenient to place the blame squarely on the addict and chastise them for a lack of discipline, but the reality is that we are always subject to influence from our environment. In many cases, people can't find their way out of deep holes specifically because their environment won't allow them to. Like a fish that doesn't realize it lives suspended in water, it's impossible to see solutions when your environment blocks your vision. If you're facing a serious, complex problem, chances are high that your environment is playing a part in your inability to find a solution. Even if it's a largely positive environment, that positivity may be a happy mask put in place to cover a fear of change. Always remember this: What you do should be dictated by the environment you find yourself in, not your personal need to feel safe or satisfy a metric. If there's a fundamental mismatch between your actions and your environment, collapse is inevitable. What kind of ecological systems do you find yourself embedded within? How can you be the fish who jumps out of the water and sees the world that lies above the surface? These are the questions you start asking when you adopt the ecological mindset—and doing so will change your life. --- # Systems Source: https://www.strategiccomplexity.com/systems/ Section: Systems & Complexity *[Figure: The solar system, a large-scale system we're all familiar with. Source]* The universe you and I inhabit is a system of systems. At the individual level, you are a collection of different systems: your nervous system, your digestive system, respiratory system, and so on. Within those systems are systems of cells, and you can go all the way down to the atomic level with this analysis. Each of these systems provides some kind of function, such as breathing, digesting food, and so on. You can go the other direction to see how everything is a collection of systems at large scales as well: your household is a system of people and things, your household exists within a city, the city is part of a regional system of some kind, and the regional system is part of a country. You can take that all the way out into space: the planet’s biosphere is a collection of ecosystems, and the planet itself is in a solar system, which is within a system of stars known as a galaxy, which is part of a system of galaxies—and so on. ## Deterministic Systems What’s a system? _A collection of things that work together to do something._ An everyday example of a system you interact with daily is your car: it’s built out of a bunch of different components, none of which on their own can get you from point A to point B on a road. Your engine in isolation is just a block of useless metal, and the same can be said for everything else in the car. But combined, all these parts generate a function we call “driving.” Your car is a particular type of system called a _deterministic system_: it operates on predictable principles and simply does what it’s designed to do. You put fuel in it, start the engine and press the gas pedal, and you know what to expect from there. When you’re in motion, you press the brake pedal and you expect to slow down. As long as it’s maintained and nothing breaks, what you see is what you get. This is how deterministic systems work. They’re linear and have predictable input-output relationships, which makes them very easy to think about and create models around. ## We Want Deterministic Everything While deterministic systems provide quite a bit of value to our world, our stupid ape brains want everything to work this way. We interact with machines that do what we want them to do in a predictable way, and we think to ourselves, “I bet I can make everything in my life like this.” And much of human history is about this drive to make the world into one big, deterministic system, usually for the benefit of a small group of people. This is a particularly common impulse for entrepreneurs and executives, who always seem to be looking for ways to make the next quarter more predictable. If only they could get their employees and markets to behave like deterministic machines, everything would be so much easier! If this is you, you are chasing something that doesn’t exist. While there are ways to find and create pockets of deterministic behavior, it’s a far better strategy to recognize that the systems around you are, in most cases, not predictable. --- # Complex Adaptive Systems Source: https://www.strategiccomplexity.com/complex-adaptive-systems/ Section: Systems & Complexity You want your car to be a deterministic system, and the same can be said for many other critical systems in your day-to-day life. But it’s critical to realize that most of what you see around you now is not deterministic. Instead, many of the systems you encounter out in the world are known as _complex adaptive systems (CAS)_. [John H. Holland](https://en.wikipedia.org/wiki/John_Henry_Holland), one of the key figures in complexity science, said that complex adaptive systems "are systems that have a large number of components, often called agents, that interact and adapt or learn." To understand what this means, consider a car and a brain. Both are made up of different parts that _work together to do something_—that's why they're both considered systems. But that's where the similarities end. ## Building Cars If your car isn't designed to deal with rough terrain, the parts won't recognize said terrain and transform your car into an off-roading truck. If you make the decision to drive into that environment, the car will just start to break. Likewise, the parts of your car don't have anything we'd recognize as "autonomy." It's a system designed to exist within certain boundaries, and if you cross those boundaries it's bad news for the car. Your car's lifecycle begins in the minds of executives at a car company, who, driven by motives like profitability and market expansion, decide to introduce a new model. This decision isn't spontaneous but rather the result of a complex analysis of market trends, financial forecasts, and competitive strategies. Once the green light is given, teams of designers and engineers take over. These professionals operate within a web of constraints – physical laws, budget limitations, and manufacturing capabilities – to transform a concept into a tangible set of technical specifications. After all that, the company builds the car on an assembly line, ships it out and sells it to drivers like you. This whole process is built around centralized decisions that are made in a top-down manner. Deterministic systems tend to operate like this, and in this case it's for good reason. You wouldn't want a car that isn't deterministic—that would make your day-to-day life more chaotic than it needs to be. The car's performance is predictable; when you turn the key, you expect it to start, and when you press the accelerator, you expect it to move. ## Growing Brains Contrast this with a brain, which is a complex adaptive system. Unlike a car, the brain isn't built in a factory; it's the product of billions of years of evolution, each step the product of genetic and environmental interactions that shape its structure and function. This process is decentralized and bottom-up, with no central authority orchestrating the development. The brain's development begins with a single fertilized cell. This cell divides, and its descendants differentiate into various types of cells. As the brain grows, it's shaped by both genetic information and environmental stimuli. This interaction is crucial: just as the terrain shapes the flow of a river, experiences shape the brain. Neural pathways form and strengthen with use, or weaken and disappear with neglect. The brain's adaptability, or neuroplasticity, is its defining feature. Unlike a car that deteriorates with misuse or under challenging conditions, a brain can adapt and even thrive in unforeseen circumstances. Expose it to new languages, and it rewires itself to accommodate them. Subject it to puzzles and problems, and it strengthens its problem-solving abilities. This adaptability isn't just limited to youth, either—while it diminishes with age, the brain retains a remarkable capacity for change throughout life. While cars of a specific model are nearly identical, no two brains are the same, even those of identical twins. Every brain reflects a unique history of interactions, decisions, and experiences. The brain's structure and function are constantly evolving in response to its environment, making it a dynamic, living system. In terms of autonomy, each component of the brain—from individual neurons to larger structures like the hippocampus or prefrontal cortex—has a degree of autonomy, responding to and influencing its local environment. This autonomy allows for emergent phenomena: the brain's capabilities, such as consciousness or creativity, aren't directly coded in its genes but emerge from the complex interactions of its myriad parts. The brain's functioning is not deterministic in the way a car's is. You can't predict exactly how a brain will react in a given situation, as you might predict a car's response to turning the steering wheel. The brain's responses are influenced by past experiences, current context, and even random fluctuations in neuronal activity. This unpredictability is a hallmark of complex adaptive systems. ## Complexity is Everywhere One way to think in broad strokes about the differences here can be found in the phrasing used: you _build_ a car, and you _grow_ a brain. They sound like synonyms, but the approaches are quite different. One involves a predictable, defined set of steps, while the other is a process with many probabilistic features. While we all prefer to deal with deterministic systems, the reality is that complex adaptive systems tend to be the most impactful across multiple scales. Consider this list of CAS examples from [Wikipedia](https://en.wikipedia.org/wiki/Complex_adaptive_system): _Climate; cities; firms; markets; governments; industries; ecosystems; social networks; power grids; animal swarms; traffic flows; social insect (e.g. ant) colonies; the brain and the immune system; and the cell and the developing embryo._ _Human social group-based endeavors, such as political parties, communities, geopolitical organizations, war, and terrorist networks are also considered CAS. The internet and cyberspace—composed, collaborated, and managed by a complex mix of human–computer interactions, is also regarded as a complex adaptive system._ In other words: you're surrounded by complex adaptive systems. Furthermore, it's axiomatic in evolutionary biology and ecology that evolutionary systems (which CAS tend to be) become more complex over time: If you assume variation and heredity, then diversity and complexity will on average increase, unless other forces, e.g. natural selection, constrain or override this tendency. Now that you know this, it's worthwhile to reflect on your current set of problems that need to be solved: * Are you dealing primarily with deterministic systems or CAS? * Given the answer to that question, do you think your current methods are appropriate? * How can you shift what you're doing and thinking in order to more accurately reflect the kind of systems you're working with? These are questions you need to ask yourself on a regular basis, if only as a gut check to stop yourself from drifting into the trap of deterministic thinking. --- # Self-Organization Source: https://www.strategiccomplexity.com/self-organization/ Section: Systems & Complexity *[Figure: No one is in charge. A decentralized mesh adapts and routes around damage. Kill the center of a hub-and-spoke and the whole thing dies.]* Self-organizing behavior, along with emergence (which is the topic of the next chapter), is the essence of how complex adaptive systems operate: without a central command, using local interactions to create global patterns and behaviors. It is always bottom-up, with small pockets of interaction leading to global effects. ## Nobody's In Charge With self-organization, the system is made up of parts that act autonomously. In other words, they self-organize and don’t make changes based on any kind of external command and control system. They do take _inputs_ from the environment since they’re open systems, but decision-making and adaptation originates from inside the system. For example, neurons in the brain don't receive detailed instructions from a central authority on how to connect and communicate. Instead, they form connections based on local rules and interactions. These connections aren't static—they're dynamic, changing in response to experiences and stimuli. The brain's ability to learn, adapt, and even heal itself after injury is a testament to this self-organizing principle. It's a complex whirlwind of electrical and chemical signals, with each neuron playing its part in the larger symphony of consciousness and cognition. The internet is also a product of self-organization. It isn't controlled by any single entity; it's a vast network of networks, where each node—be it a server, router, or personal computer—operates according to simple protocols. Yet, from these local interactions emerges a global system that's robust, flexible, and incredibly complex. It's a digital ecosystem where information flows and evolves, mirroring the organic processes found in nature. ## Self-Organization in Society Even in human society, self-organization plays a pivotal role. Consider the economy, a complex system where countless individuals and organizations interact based on their own needs and desires. There's no central authority dictating every economic transaction; rather, the economy self-organizes based on supply and demand, competition, innovation, and myriad other factors. The resulting economic landscape is ever-shifting, evolving in response to internal and external pressures. Self-organization is also apparent in the world of art and culture. Cultural trends, artistic movements, and even languages develop and evolve organically, shaped by the interactions of individuals and communities. These cultural phenomena don't arise from a single directive but are the collective result of countless individual choices and expressions. ## Ecological Examples Given our focus on ecology and how problem solving should focus on interactions within a dynamic environment, consider these examples of self-organization: ### **Interconnectedness** Ecological systems are stunning examples of self-organization, where myriad forms of life interact with each other and their environment in a complex web of survival, growth, and adaptation. In these systems, no single species or element has overarching control. Instead, the balance and health of the ecosystem emerge from countless interactions among its constituents. For instance, in a forest ecosystem, the interplay between various plants, animals, microorganisms, soil, and climate creates a dynamic, self-regulating environment. Trees provide habitat and food for animals and insects, while also contributing to the soil's health through leaf litter. In turn, these elements support the growth and diversity of plant life, maintaining the forest's overall balance. ### **Symbiosis and Mutualism** A striking feature of ecological systems is symbiosis—close interactions between different species. Consider the relationship between bees and flowering plants. Bees collect nectar and pollen for food, and in the process, they pollinate the flowers, facilitating plant reproduction. This relationship, essential for the survival of both bees and flowering plants, is not dictated by any external entity. It's a natural outcome of their co-evolution and interdependence, showcasing how self-organization can lead to complex, mutually supportive relationships in nature. ### **Feedback Loops and Homeostasis** Ecological systems maintain balance through feedback loops, which are commonly found in complex adaptive systems. These are processes where the system’s outputs loop back as inputs, influencing the system's future state. A classic example is predator-prey dynamics. An increase in prey population might lead to an increase in predators, which in turn reduces the prey population, subsequently causing the predator population to decline. This cycle of feedback helps maintain the ecological balance. These feedback loops are not centrally orchestrated but are intrinsic to the interactions within the ecosystem. --- # Emergence Source: https://www.strategiccomplexity.com/emergence/ Section: Systems & Complexity The other piece of the complexity puzzle is emergence. Emergence is novel behavior that arises from the interactions between parts in a self-organizing system—it emerges as the parts interact with one another. These interactions and outcomes tend to be nonlinear and difficult, if not impossible to predict, even though the parts and the rules they use to interact tend to be very simple. Your brain is once again an excellent example: neurons are basic cells that operate with a few simple rules. Those rules dictate how they pass electrical and chemical signals to other neurons. But when you link billions of them together, you somehow get consciousness. In other words, consciousness is an emergent property of the brain: it’s a novel set of behaviors that you can’t predict from simple rules of interaction between simple parts. Likewise, a self-organizing natural structure like an ant colony is filled with emergent behaviors. When looking at an individual ant, they’re some of the most basic creatures imaginable. A single ant is close to worthless, so dumb and incapable that it might just run in circles until it dies of exhaustion. But when you combine large groups of ants together, you get incredible, emergent behaviors like building the colony itself, bunching together to form rafts that can cross bodies of water, and finding food by leaving pheromone trails (a fascinating process called [_stigmergy_](https://en.wikipedia.org/wiki/Stigmergy)). ## Emergence Across Domains Emergence is not just confined to the brain or ant colonies; it is a pervasive principle in nature, where the whole becomes greater than the sum of its parts. This phenomenon can be observed in various biological systems, showing how complexity arises from simplicity. ### Bird Flocking and Collective Intelligence Bird flocking is where individual birds follow simple rules like maintaining a certain distance from neighbors and aligning their direction of flight. Despite these simple rules, the flock exhibits complex behaviors like forming intricate patterns, swiftly changing directions, and efficiently navigating long migrations. This collective intelligence of the flock, an emergent property, cannot be predicted by studying an individual bird in isolation. The flock's behavior is a result of the interplay between individual birds, each contributing to the group's overall movement and decision-making process. ### Ecosystems & Dynamic Balance In larger ecological contexts, emergence plays a critical role in maintaining the dynamic balance of ecosystems. The interactions between different species—predators and prey, plants and pollinators, hosts and parasites—lead to emergent properties like ecological stability, biodiversity, and resilience. These properties are crucial for the survival and flourishing of ecosystems but are not properties of any individual species. Instead, they arise from the complex web of interactions within the ecosystem, illustrating how emergence is integral to the health and functioning of our natural world. ### Social Emergence & Collective Behavior In human societies, emergence is evident in the collective behaviors and social phenomena that arise from individual interactions. Market trends, cultural norms, and even social movements are emergent properties of the interactions among individuals. For example, the stock market's fluctuations are not just the sum of individual investment decisions but an emergent property of complex economic interactions, investor sentiments, and global events. These phenomena reflect the collective mindset and behavior of society, transcending individual actions and decisions. ### Technological Emergence & Network Effects The digital realm also showcases emergence, particularly in the context of network effects. As more individuals use a digital platform or technology, its value and utility increase, a phenomenon not inherent in the technology itself but emerging from user interactions. This emergent property can lead to rapid growth and widespread adoption, as seen in social media networks and online marketplaces. The network becomes more than just a collection of users; it transforms into a dynamic space for collaboration, innovation, and community building. ## Uncomfortable Truths Emergent and self-organizing behaviors are everywhere you look, and the fact that they’re hard (if not impossible) to predict is what makes them simultaneously valuable and maddening. Our simple brains don’t want to accept them because they seem to violate everything we trust about systemic behavior. _Why can’t the world just act like a deterministic system?!_ When you put this all together, you can start to see how the most important systems just don’t conform to the deterministic model. As uncomfortable as that may make you, it’s unavoidable, because the complexity cascades out from the fact that _each human is a complex adaptive system containing other complex adaptive systems_! Your brain is a complex adaptive system, as is your body, as are many of the things you interact with in your environment. When each complex adaptive system known as a person interacts with other complex adaptive systems, either in the form of other people or some other type of system in the environment, you get unpredictable, nonlinear results. This is why nobody can predict what’s going to happen with the stock market, or understand why trends seem to pop in and out of existence at high speed. There’s simply no way to wrap your mind around how all of this works, and that remains true no matter how much knowledge you slam into your brain. Your internal paradigm then has to shift, from this outdated, ineffective attempt at predicting and controlling everything, to positioning yourself to either create or quickly adapt to changes that cascade in nonlinear waves throughout your environment. --- # Chaos & Attractors Source: https://www.strategiccomplexity.com/chaos-and-attractors/ Section: Systems & Complexity *[Figure: Two basins of attraction. Wherever the system starts, it rolls down into one stable state or the other. The ridge between them decides which one.]* There’s an interesting quirk about complex adaptive systems that is quite mind-bending: they operate best at the **edge of chaos**. When a system is too stable and too deterministic, it dies. Likewise, when it’s too chaotic and unstable, it also dies. You can think of the lifecycle of a complex adaptive system as a progression from chaos to order. When it first comes online, it’s often a jumbled mess of components that barely work together. The overall function of the system may not be clear at first, but the system accomplishes something. ## Trending Towards Order This is similar to the first stages of a business, when you make your first bit of money and realize you might be onto something. In many cases, the revenue comes as a surprise—you planned to profit in one way, but ended up doing something else. But no matter what, at this stage everything feels like it’s on fire and nobody knows what they’re doing. This is a dangerous phase for any system to stay within. If this initial high level of chaos is not reined in at all, the system will die. Chaos will overwhelm even the most resilient parts and collapse becomes an inevitability. Complex adaptive systems that survive tend towards increasing order, by adapting to the environment and exploiting any beneficial resources they come across in that environment. To use the business example again, this is your first few hires, or the first impactful automations you create to cut down on your daily workload. But when a system leans too hard into this orderly tendency, it becomes fragile and risks implosion, just like the initial chaotic stages. This is comparable to when businesses become sluggish and instead focus entirely inward. Bureaucracies get built, MBAs take over, and suddenly an enterprise that had real potential becomes a cesspit of incompetent politicians who seal the company’s long-term fate. ## Attractors This happens because of what are called **attractors**, which are pathways a system naturally falls into as it progresses towards order. It's a form of maximization, where one variable (or a very small number of them) dominates the rest of the system. This variable/set of variables drives the system towards a specific state, which can then blow up the system if it's addressed. To understand attractors, imagine a landscape filled with hills and valleys. This landscape represents the possible actions a system can take, with hills and valleys representing different courses of action. Attractors are those deep valleys that, once a system starts heading towards them, become incredibly difficult, if not impossible, to avoid. A system is _attracted_ to it and in some sense _wants_ to fall into it, in many cases for objectively good reasons (although this is not always the case). The bottom of the valley is known as an _attractor basin_, and it represents a stable equilibrium state for the system. Because order is what the system tends towards, it’s natural for a system to hit these basins and stay there until they die—regardless of where the system started. Think of a large vase that you drop a marble into. No matter how you place the marble into the vase, it will eventually settle into the bottom and then stay put. That bottom of the vase is an attractor basin, and the marble stopping there represents equilibrium. Attractor basins are those comfortable places a system goes to die, and it requires some kind of energy expenditure to escape them. Equilibrium is death for any complex adaptive system, since by definition it represents a steady, unchanging state. In a world of constant change, this is an unacceptable phase to stay within. This is what is meant by the concept of the "edge of chaos." It is that state of being out of equilibrium, but not _so_ far out of it that the whole thing falls apart. Living at the edge of chaos requires resources and is an effortful activity—when all effort ceases, the system will find its way into an equilibrium death. ## Escaping from Attractor Basins How do you escape from attractor basins? In some sense, you don’t escape from the attractors themselves, because attractors exist for very good reasons. They represent behaviors that work in a given environment, so you shouldn’t ignore them or try to kill them off completely. The hard part is recognizing when you’ve fallen into the basin and are approaching equilibrium, which, again, represents a comfortable death. It’s normal and healthy for attractors to draw you in and for you to exploit what they represent. What’s also normal (but unhealthy) is allowing attractors to drag you into a deep basin, where you stop adapting. The problem with basins can be seen when you visualize the basin itself: you can’t see the rest of the landscape. You may be circling around the basin at this very moment and not realize it, because from your perspective inside that basin, you’re blind. All you know is that you’re doing something that works and feels good, so why bother leaving that soothing bed of equilibrium? This presents two pressing issues: 1. You’re stuck in a basin, so you aren’t adapting to the landscape. 2. You can’t get out of it on your own, since you can’t see where else to go. Since you’re dealing with a complex adaptive system, the behaviors that lead to attractor basins are self-organized and emerge in the process of interaction with the environment. Complicating this all even further is that the landscape itself is not static - the hills and valleys shift, sometimes rapidly, over time. You may fall into a basin, get out of it, and realize that the landscape is radically different! ## Disturbances & Perturbations Your goal then is to not just avoid attractor basins as much as possible, but when you do fall into one, get out as fast as possible. This is accomplished with randomness, delivered through disturbances and perturbations. A _disturbance_ is some kind of external _input that causes_ a shift in the state of the system. A _perturbation_ is the _effect of the disturbance_, a result that cascades across the system. Disturbances can be deliberate (think of poking something with a stick to see what happens), or they can show up randomly as a result of interactions with an environment. Perturbations within a complex adaptive system tend to be extremely difficult, if not impossible, to predict ahead of time. Because CAS exhibit nonlinear behavior, disturbances of any size can generate one of three primary types of perturbations: * Neutral: No effects. * Minor: Small effects. * Major: Big effects. What can be so difficult to wrap your mind around is the fact that there can be mismatches between disturbances and perturbations. This is akin to putting months into some big project, then releasing it to crickets, or throwing together something over a weekend and watching it explode into a massive hit. You just never know what kind of perturbation you're in for when you throw a disturbance at a CAS. So to get yourself or a system out of an attractor basin, there must be some kind of disturbance that generates a perturbation with a large enough effect to knock you out of that steady state. If it's only a minor perturbation and you're in a deep basin, chances are you'll stay stuck. But if it's a major effect—either imposed by you intentionally or by the environment randomly—you're on your way to a new horizon. ## Crises Have you ever made a significant change in your life just because someone told you to? If you're being honest with yourself, chances are high that the answer is "no." Maybe it's happened once or twice, but more often than not, changes happen as a result of crises. This is the attractor-disturbance-perturbation dynamic in action in your personal life: you need to change, but weak disturbances (words of other people) don't create an adequate perturbation (meaningful change). It's often only with the arrival of serious threats to your survival, imposed at inopportune times, that you (as a CAS) adapt in a meaningful way. The real challenge behind this book, behind all of my work in reality, is to get you to make changes before you hit a crisis point. It's overwhelmingly common for people to come to me and say "Oh my god, everything is broken, what do I do?" The world has imposed a significant disturbance-perturbation combination on you at the worst possible time. Knowing all this, chances are you still won't make the changes you need to. But if you learn from experience, you'll start to pick up on the pattern: it's always better to perturb yourself than it is to have the world perturb you. --- # Answers Source: https://www.strategiccomplexity.com/answers/ Section: Fundamental Problems Most people just want an answer. It doesn't matter if it's the right answer, nor does it have to be seen as an answer by everyone. As long as it's delivered with confidence and _feels_ right to the target audience, that's often enough. Even more importantly, people want answers from third-party, authoritative sources. When it comes to the most pressing issues a person faces, the answers they get from family, friends and other everyday people they interact with are often seen as insufficient. It's only when the answers come from an oracle, god(s), or some kind of mystic that they carry any real gravity. ## Autonomy This is an instinctual reaction to uncertainty. We fear the future, whether it looks bright or dim. But this need for answers also hurts us. When you decide to seek an answer from any external source, **you are handing over the keys of your autonomy**. This is a frictionless event in most cases, happening below the level of conscious awareness as people probe around for efficient explanations. There are cases when losing that autonomy doesn't mean much—such as looking up a fact on Wikipedia or Google. You aren't less of a free thinker because you couldn't remember the capital of Peru, or you needed an answer about how to solve a widely-understood programming problem. Where you get into trouble is looking for firm, concrete answers when one doesn't exist. For example, if you're not sure if you want to have kids, the last thing you should do is ask anyone else whether it's a good idea. Every person has their own opinion about it, especially if they have children of their own already. None of those opinions are wrong per se, as they reflect that particular person's perspective based on their own palette of experiences. But none of them apply to you, your brain, your environment, and so on. Their answers are just that: _their_ answers, not yours. This is a frightening proposition, as it implies you have no choice but to take risks. You may have a child and discover that you hate it. Or it may be the best thing that ever happened to you. Either way, nobody can tell you what that experience entails for you, since by default nobody knows. It doesn't matter how much experience anyone else has in that sort of arena either—that accumulation of data doesn't apply to you. ## The Ouroboros What's even more frustrating is the fact that answers have a maddening tendency to lead to more questions. You start with a single question, find what feels like an answer, and, without warning, you find yourself staring at a hydra of five new questions. Then you answer some of those questions, only to realize each answer has once again sprouted multiple new, unknown paths! I often think of the ouroboros, the ancient symbol of the snake eating its own tail. This image encapsulates the reality of our most important problems. You keep trying to get to the bottom of it, only to discover an infinite loop. A big step in the right direction is acknowledging that the journey is the destination and embracing that search instead of running from it. *[Figure: The ouroboros, which represents the reality of most important domains.]* The answer is to stop thinking in terms of answers. Any answer you get, unless it's some basic fact along the lines of the capital of Peru or the answer to 2+2, is better viewed as an entry into new, more interesting questions. That process of question asking never ends—which, if you're the kind of person who found their way to this book, is the best part of this whole deal called being alive. Solving problems in complex, unpredictable environments like the one you're living in right now follows the same dynamic. You know you need solutions, and it's easy to fall into the trap of seeking out the fastest possible answer from some kind of third-party authority. Recognize right now that this is a path to nowhere. ## Action & Conformity To get any kind of real answers, answers that apply to your specific situation, you must **act**. Contact with the world is mandatory. No amount of reading, thinking or navel gazing will help you in the quest for real knowledge, and I say that as someone who has burned far too much time doing all three. One of the fundamental problem solving skills emerges out of all this: the ability to recognize where answers exist and where they don't. It's amazing how much progress you can make—and how many landmines you can avoid—when you recognize that a single, concrete answer isn't possible. Likewise, an answer may exist, but you need to be willing to admit that there's no way to find it without exploration. Other people's solutions to your problems are not real solutions. You may be able to copy/steal pieces of other people's solutions—which is a practice I encourage. But even if all you do is solve via recombination of existing solutions, those mixtures must be adapted to your situation in unique ways. At a more philosophical level, when you adopt someone else's solutions you are forfeiting the essence of who you are as a person. This is the definition of conformity: giving up your own ideas in the name of adopting safe, proven routes designed by others. What's so wicked about conformity is that it's a lie—it won't get you what you want, even if it does get you _something_. Conformity does pay well, after all. ## Leadership If you're a leader and your goal is to find some cookie-cutter framework that will magically solve wicked problems on your behalf, you're deluding yourself—and you probably shouldn't be in a leadership position. At worst, you're setting yourself up to be a sucker for someone's scam. At best, you're missing the whole point of leadership, which is to make decisions in the face of uncertainty. The truth, which many people find unbearable, is that you need to discover your own path. Guides like this exist to show you a variety of doors you can walk through, but it's up to you to make the real-world choice of walking through one or the other. As a leader, no matter how you define that, you need to internalize that as a core feature of your day-to-day life. Look on the bright side: your life will be full of color. After all, if you aren't solving complex problems, chances are your life is pretty tame. --- # Security Source: https://www.strategiccomplexity.com/security/ Section: Fundamental Problems Our brains are designed to seek out security, to think in linear, predictable terms that trick us into thinking we’re capable of anticipating the future. This is an important aspect of our development as a species: in the simple environments from which we evolved, our ability to link cause and effect was often the difference between life and death. Those who could spot basic correlations such as the presence of a predator and the death of a fellow tribe member tended to pass on their genes to the next generation. As a result, this sort of causation detection system in our brains survived the process of natural selection and we now all live with this inherited ability. ## Cause & Effect in a Complex Environment The problem is that, in a world of immeasurable complexity, this simple mechanism not only doesn’t work in many cases, it flat out deceives you. Because there’s so much information, so much raw material for your pattern recognition systems to work with, you can create cause-and-effect stories in your head that diverge from reality in dangerous ways. To be clear, cause and effect will always have some utility. It’s good to know that if you mix ammonia and bleach, fatal vapors are created, or that not changing the oil in your car will ruin your engine. It’s when you try to scale up that kind of thought process beyond the simplicity of your immediate environment that you run into issues. This is especially true in the world of business, where everyone is in a mad dash to create that illusive fire-and-forget money-making system that allows you to predict profits with 100% accuracy. It’s one of those areas of human life where linear cause-and-effect thinking moves beyond mere annoyance and into the realm of real danger. Every organism, including you, wants to assert its will to live and reproduce within an environment it controls. In other words, you don’t want to just survive, you want to live life on your own terms and shape the world you live within. This stands in stark contrast to how powerless this chaotic, complex world makes you feel. ## Stop Predicting the Future Here’s the bad news: you will never be able to predict the future, nor will you be able to control everything around you. To survive and live on your own terms, you need to reorient yourself in ways that are at odds with much of what you currently believe and act upon. Here’s the good news: there is a way forward that will allow you to adapt and win, and you’re living within a critical turning point in history. Nobody knows what to do, all the old ways are crumbling before our eyes, and those who recognize and leverage this will become leaders. Furthermore, despite our natural inclination towards safety and stability, we also happen to paradoxically be one of the most versatile, adaptable species on the planet. It doesn’t make sense on paper, but it’s the truth: we’re designed to find and create both order and chaos. The shortcut we often take is to throw up our hands and say "There's too much uncertainty!" If you've ever worked in or around large companies, you've heard something like this used as a scapegoat for poor performance, layoffs, etc. From this point forward, I want you to promise yourself that you'll never use that phrase again. The future is uncertain by default—that's been true since the beginning. We don't fail because we can't predict the future, we fail because we don't recognize that the future is mostly unpredictable. Even when we say we know that, we continue to act as if we don't. When you recognize that you're dealing with uncertainty while trying to solve an important problem, the solution isn't to fall into that trap. Instead, you must find ways to act that mesh with uncertainty, rather than fight against it. --- # Control Source: https://www.strategiccomplexity.com/control/ Section: Fundamental Problems One of the single most destructive habits you can develop is that of overcontrol. It is no exaggeration to say that attempting to control every variable has blown up more businesses, relationships, and various other types of systems than anything else. We think that increased control decreases volatility and gives us a better chance at success, but more often than not we get the opposite: complete and utter chaos. This is your brain playing the same trick on you, making you believe that the answer to uncertainty is forcing your will upon your environment. Even if you've been given the tools to resist this urge, it finds its way into your behavior anyway. The irony lies in the fact that this is a desire for stability and predictability paradoxically breeds the very instability and unpredictability it seeks to avoid. ## Avoiding Reality In the pursuit of control, the reality of complex adaptive systems is ignored. These systems, be they in business, personal relationships, or ecological networks, thrive on a certain level of autonomy and organic interaction. When every variable is tightly controlled, the natural dynamism of these systems is stifled. This stifling leads to fragility, where even minor disturbances can cause disproportionate disruptions. It's like over-pruning a tree: in an attempt to shape it perfectly, its natural growth is stunted, making it more susceptible to environmental stressors. The quest for control is often based on the fallacy that all variables can be known, measured, and managed. This is a Sisyphean task in a world characterized by complexity and change. In complex systems, the interdependencies and interactions among components are so intricate and dynamic that they defy simplistic control mechanisms. What works today may not work tomorrow, and the attempt to maintain a steady hand often leads to a loss of touch with the evolving reality of the system. This overbearing need for control also ignores the creative and adaptive potential inherent in many systems. In business, for instance, employees who are given the freedom to experiment, fail, and learn often contribute far more than those who are treated like remote control automatons. Loosening the grip of control can lead to emergent properties that would not arise under a regime of strict management. Very little of value has come from systems that are highly controlled, while a diverse tapestry of discoveries and inventions have emerged naturally in fluid environments. ## Damage In personal relationships, allowing space for individuality and spontaneity can lead to more human interactions. Ask yourself: do you want to be in any kind of relationship with a control freak? If you answered "yes," chances are you need to see a mental health professional immediately! Psychological damage from the desperate need for control cannot be understated. It breeds anxiety, as the gap between the desired state of absolute control and the reality of inherent unpredictability widens. It can lead to burnout, as the effort to maintain control becomes increasingly taxing. And in many cases, it results in a paradoxical loss of control, as the system either collapses under the weight of rigid management or breaks free in a chaotic manner. The antidote to overcontrol is not abandonment of structure but rather the adoption of a more nuanced approach that recognizes the limits of control and the value of flexibility, adaptability, and resilience. It is about finding the balance between guidance and freedom, between planning and spontaneity, and between intervention and evolution. Letting go to some degree not only aligns with the realities of complex systems but also offers a more sustainable and less stressful way of navigating the world. In relinquishing the illusion of total control, you open yourself up to the rich web of possibilities that emerge when systems are allowed to find their own way forward. --- # Optimization Source: https://www.strategiccomplexity.com/optimization/ Section: Fundamental Problems The concept of full optimization often emerges as a goal across domains, from business operations to personal development. However, this quest for the ultimate state of optimization not only proves to be an impossibility. Even worse, it tends to damage or even kill systems. The natural world, with its evolutionary processes, offers clues into why total optimization is a misguided aspiration. ## Good Enough for Nature Evolution, the fundamental process that shapes life, operates not on principles of perfection but on the concept of "good enough." Natural systems, through evolutionary mechanisms, do not strive for flawless optimization. Instead, they adapt and evolve in ways that are sufficient for survival and reproduction. The result is often a mix of efficiency, redundancy, and what might be termed 'junk' in their genetic makeup. This 'junk'—noncoding DNA once considered useless—plays a crucial role in the resilience and adaptability of organisms. It's a reservoir of potential, a source of genetic variability that can be crucial for survival under changing environmental conditions. This lack of full optimization in natural systems is not a flaw but a feature. It allows for flexibility and adaptability, qualities essential for survival in an ever-changing world. In contrast, systems that are fully optimized are often fragile. They are tailored to specific conditions and lack the buffer to absorb shocks or adapt to new circumstances. When conditions change, as they inevitably do, these optimized systems can fail spectacularly. ## Human Optimization In business, the pursuit of full optimization can lead to similar vulnerabilities. Over-optimized supply chains, for instance, can become highly susceptible to disruptions. In the quest to eliminate every inefficiency, companies may remove the very redundancies that provide a cushion against unforeseen events. Global disruptions in stock markets, international trade, and other large-scale complex systems have highlighted the perils of over-optimization. Businesses with more flexible, less 'optimized' operations often fared better in adapting to new challenges, underscoring the value of resilience over maximum efficiency. Those organizations that optimize down to the bare minimum often find themselves in dangerous territory when the world goes sideways, as they don't have the adaptive resources to respond. Obsession with optimization in personal development—every minute accounted for, every activity maximized for productivity—can lead to burnout and a lack of creativity. The most innovative and fulfilling human endeavors often arise from spaces that are not fully optimized, from time spent not in pursuit of productivity but in exploration, relaxation, and play. These 'inefficiencies' are vital for creativity, mental health, and sustained productivity over the long term. Full optimization as an idea assumes a level of predictability and control that simply does not exist in complex systems. Whether in ecological, economic, or social systems, the interplay of countless variables creates a landscape of uncertainty and flux. In such environments, the ability to adapt and evolve is more valuable than the ability to perform optimally under a narrow set of conditions. While optimization is undoubtedly a useful goal in certain contexts, the pursuit of full optimization is both impractical and undesirable. Natural systems, through their evolutionary lifecycles, demonstrate the value of redundancy, flexibility, and the 'good enough' principle. Leaving room for adaptability, creativity, and resilience is not just a concession to the limits of optimization; it's a strategic and holistic approach to winning in a world defined by complexity and change. --- # Reductionism Source: https://www.strategiccomplexity.com/reductionism/ Section: Fundamental Problems *[Figure: Take it apart and the behavior disappears. The parts never held it. The relationships between them did.]* In the quest to understand complex systems like the brain or an ant colony, we often turn to **reductionism**—breaking down complex phenomena into their simplest parts to understand the whole. But this method hits a wall when faced with the realities of emergence in complex adaptive systems. Reductionism suggests that by understanding the parts, we can understand the whole. It's like trying to decipher a novel by studying individual letters or words without considering the sentences, paragraphs, and overarching narrative. This approach _can_ be effective for deterministic systems, where the whole is indeed the sum of its parts. But when it comes to emergent phenomena, reductionism falls short. ## Emergent Phenomena Take consciousness, for example. By examining neurons individually, we gain insights into their functions and interactions. But consciousness is not merely a sum of these parts. It's an emergent property, arising from the complex interplay of billions of neurons. Reductionism can't fully explain how these simple components give rise to such a profound and complex phenomenon. Similarly, examining a single ant outside the context of its colony offers limited insight into the colony's sophisticated behaviors. The emergent properties of an ant colony—such as its ability to build, forage, and adapt—are not evident when observing an individual ant. These properties emerge from the collective interactions of the colony, transcending the capabilities of a solitary ant. Reductionism struggles with the dynamic, non-linear nature of complex adaptive systems. These systems are constantly changing, adapting, and evolving, making it difficult to predict their behavior based on static analyses of their components. The interactions between parts are often more critical than the parts themselves, creating outcomes that are greater and more complex than the sum of individual actions. In the face of emergent phenomena, we're compelled to adopt a more holistic approach. This means appreciating the complexity and interconnectedness of systems, recognizing that their most intriguing properties often arise from the interactions between components, not just the components themselves. It's a shift from a focus on parts to a focus on patterns, from individual elements to the intricate web of relationships that weave them together. While reductionism has its place in scientific inquiry, it's insufficient for fully grasping the essence of the problems you need to solve in a complex world. This shift in perspective is not just a scientific necessity but a philosophical imperative, forcing us to examine _why_ we're driven to see the world through a reductionist lens. --- # The Nature of Adaptation Source: https://www.strategiccomplexity.com/the-nature-of-adaptation/ Section: Dilemmas Adaptation is a key aspect of all complex adaptive systems—it's in the name, after all. They take inputs from the environment, autonomously make decisions, and then shape their behavior accordingly. It's best thought of as a _process_ first and foremost. Adaptation is also a _state_ in which the system has temporarily found a niche for itself in which it can survive and prosper. You say that something is _adapted to_ an environment because it has gone through (and continues to go through) that _process of adaptation_. There is a strong natural desire to resist adaptation. We want to remain static and stable, indifferent to the world that swirls around us. In some cases, that is both possible and positive. Out in the natural world, for example, organisms only adapt when they _have_ to—otherwise they'll stay roughly the same. It's only when selective pressures present themselves that the evolutionary process kicks in. In the human world, this is the equivalent to a large, monopolistic business simply gliding along on its dominant position. It makes sense, as they have very little incentive to do anything beyond the bare minimum. But most organisms, and most people, don't have that luxury. If they behave as if they inhabit a monopolistic position, they'll get swept away by selective pressures. There's no escaping the fact that adaptation is, in most cases, the wise move. ## Habitat vs Environment This brings us to an important distinction, between an _environment_ and a _habitat_: 1. An **environment** is your physical surroundings. This is a neutral concept, as it's possible to exist within a hostile habitat just as it's possible to exist within a neutral or positive habitat. 2. A **habitat** is _an environment you're adapted to_. It is a subset of the environment concept, in which an organism's traits match the problems of its surroundings well enough that it can reproduce and survive. A fish's environment always contains water, but its habitat is more specific—saltwater, freshwater, coastal, swamp, etc. Every organism has some kind of habitat where it fills a niche, which is discovered through an adaptive process. This is true even for generalist creatures like human beings. When you struggle to solve problems, it's often because you're operating outside of your habitat. As hard as this may be to accept, you cannot force an environment to accommodate you—you're better off flowing into a different context. It's worth asking yourself, over and over again, _am I in my habitat or am I just surviving in an environment?_ If you're just surviving in an environment, you're only making things difficult for yourself. Find your habitat, and the solutions will follow. ## Trade-Offs What's even more important to realize about adaptation is the fact that **there are **_**always**_** trade-offs**. There's no such thing as a free lunch when it comes to adaptive problem solving: * Gills keep you alive underwater, but will kill you on land. * Wings are great for flying, but terrible for grasping. * Speed is great for catching prey, but requires copious energy. There is no such thing as an organism that is optimally adapted to all environments, nor is it possible to simultaneously improve all aspects of an organism's fitness. The future is uncertain and there are limited resources available for every creature on the planet. Compromises must be made. Problem solving in complex environments will **always** present dilemmas. If there weren't dilemmas, the solutions would be clear. It's the difference between tic-tac-toe and chess: there are simple, optimal strategies for tic-tac-toe, but there are too many variables and dilemmas for that to ever be the case for chess. The idea of _limited resources_ includes time, especially in fast-moving environments like the internet. Opportunities may only be available for a short period of time, which means you're better off throwing _something_ out into the world rather than waiting for a solution that covers all bases. If you've spent any time in the world of early-stage businesses, this is often called a _minimum viable product_ (MVP). As it turns out, nature has been churning out MVPs since the beginning. The process of adaptation does not pump out optimal solutions. It's more accurate to say that it generates "good enough" solutions that get the "job" of survival and reproduction done—nothing more, nothing less. That's the attitude you need to solve problems well in the world you find yourself in today. ## Dilemmas Rule Everything Around Me Adaptation is at the core of the problem solving game, but it's only the beginning of the solution process. The rest of this book outlines some of the most fundamental dilemmas a decision-maker faces in unpredictable, complex environments. You'll also notice many of them share similarities. This is simply a reinforcement of the recurring idea from ecology that _everything is connected to everything else_. When you can see through the lens of one dilemma, it often gives you the tools to deal with others. Dilemmas also overlap in many cases, or represent the same problems cloaked in different guises. When you have a multidimensional perspective on the nature of your problems, it often provides the edge you need to come out ahead. John Boyd, while delivering his famous lecture series _A Discourse on Winning and Losing_, liked to use a metaphor about how different people look at a pyramid to explain why it's important to utilize multiple perspectives. He'd start by giving the example of a group of people who had only ever seen pyramids from a single angle: > _You only had the opportunity to see pyramids from the side. Only from the side. You'd go through life thinking pyramids are triangles. Now let's say we got another group, different from our group here, and they only got to see pyramids from the top._\ > \ > _They'd think there were rectangles with intersecting diagonals. A square. So now let's say this group then interacts with the other group, and they start talking about pyramids, and say, "These guys are goddamn idiots." And it's you he's talking about and he thinks you're an idiot._\ > \ > _But you're both talking about the same thing from what? A different point of view. **You're both correct**, partially. But from a different point of view._ The same can be said about complex problems in general, as well as the dilemmas that follow. No single perspective is complete or will give you "the answer," but if you can synthesize multiple viewpoints you can come up with unique and powerful solutions. --- # Plans vs Heuristics Source: https://www.strategiccomplexity.com/plans-vs-heuristics/ Section: Dilemmas \ In our rapidly evolving, interconnected world, the ability to navigate complex environments has become critical. You can begin to understand this by comparing the structured predictability of a city to the unpredictable chaos of a jungle. While the city, with its streets, signs, and traffic lights, offers a well-mapped environment where plans can be executed with reasonable certainty, the jungle presents a very different reality. ## The City Imagine the city as a domain of order, where systems and structures provide a clear framework for navigation. Here, tools like Google Maps can predict travel times with reasonable accuracy, accommodating factors like traffic and public transportation schedules. The city's layout and rules, despite occasional unpredictable elements like road work or erratic drivers, generally offer a legible and manageable environment. This predictability allows for detailed planning and strategy, akin to the design approach in problem-solving, where steps can be methodically laid out and followed. City navigation is algorithmic: you follow steps to reach your destination. Think of how simple it is to map out how to get somewhere in New York City once you learn the grid system. Although your journey may be complicated by some of New York City's famously insane drivers and pedestrians, the route itself is a matter of matching streets and avenues. ## The Jungle Contrast this with finding yourself in a dense, disorienting jungle, as the lone survivor of a plane crash. The usual rules and tools for navigation are irrelevant. There are no paths, no signs, and no predictable structures to guide you. In such an environment, rigid planning not only becomes futile but potentially dangerous. Imagine if you found a map in the wreckage, but you weren't sure what it was a map of. All you see when you look around is trees, bushes and wildlife. Your best bet for survival is to toss the map, because following it may send you deeper into the jungle and further from a happy ending. The situation calls for a heuristic approach—one that involves continuous sampling, adaptation, and adjustment. This method is about making short-term decisions, assessing their outcomes, and then recalibrating based on the new information gathered. It's a continuous cycle of action, observation, and adaptation. Instead of expecting a map to save you, the better method is to walk for a while, stop, gather data about your surroundings, and then assess what to do next. It's adaptive rather than algorithmic, since you have no idea what to do ahead of time. As you move and learn, you may come up with some basic rules, such as "keep the river on your left." While there are no guarantees you'll make it through the jungle, this heuristic-based approach is far more likely to get you where you want to go than a rigid, city-like navigation system. ## City Solutions for Jungle Problems There's a tendency to approach problems and decisions as if they were in the orderly confines of a city. This approach can lead to strategies that are ill-suited for the complex, dynamic nature of today's challenges. In a jungle, the best plan is the one that evolves in response to the environment. It starts with a bit of randomness, a willingness to explore, and an openness to change direction based on new information. This approach mirrors the principles of Darwinian evolution, where survival hinges on the ability to adapt and where the most successful strategies are those that are continuously refined and reshaped by the environment. In applying this to your problem solving processes, it's crucial to recognize when a situation calls for a jungle approach rather than a city approach. While structured planning and predictable models have their place, there are many scenarios where a more flexible, heuristic method is needed. This involves being observant, responsive, and willing to change course as new information and circumstances arise. It's about developing strategies that are not rigidly predefined but are instead dynamic and responsive to the complexities of the environment. Likewise, there are situations when it makes sense to operate as if you're in a city. Algorithmic solutions do exist, and it's wise to take note of when to apply them. While adaptation is a good default, don't reinvent the wheel—unless that's what you _want_ to do. Recognizing when to employ a city approach and when to adopt a jungle mindset is key to effective decision-making in our increasingly interconnected and complex world. --- # Design vs Selection Source: https://www.strategiccomplexity.com/design-vs-selection/ Section: Dilemmas When facing a problem, you must decide on the levels of randomness and uncertainty that you're willing to accept in the solution process. In some cases, there's very little randomness that can be tolerated because the consequences are simply too great. In others, higher levels of randomness are either tolerated or encouraged. This can be framed in terms of design versus evolutionary selection, two distinct approaches that are appropriate in specific types of domains. ## Design Design, often the default human choice, represents a methodical, planned approach. It's similar to setting out on a journey with a detailed map and a clear destination. Algorithmic steps are pre-planned to address the problem at hand and reduce uncertainty before any effort is expended. This top-down process, deeply ingrained in various aspects of our lives like education, work, and even daily activities like cooking, relies on predictability and control. It assumes a comprehensive understanding of the environment, where variables are known and outcomes can be anticipated. In design, the path is charted beforehand, and deviations are often seen as setbacks rather than opportunities. ## Selection In contrast, selection embodies the acceptance of uncertainty and flexibility. Deriving its name from the evolutionary concept of natural selection, this approach is about adapting and evolving in response to environmental pressures. It’s akin to navigating a wild, uncharted territory where the landscape itself is in constant flux. With selection, there is no predetermined path, but rather a continuous process of adaptation, where what works is kept and what doesn't is discarded. This method aligns with the principles of genetic replication, where heredity, variation, and environmental pressures interact to select the traits that are most suited for survival and reproduction. The selection approach is dynamic and inherently uncertain. It recognizes that the environment is not fully knowable, which is often the case when dealing with complex adaptive systems. In this context, solutions and strategies evolve organically. They are not designed from the outset but emerge through a process of trial and error, experimentation, and adaptation. This method can be more resilient and robust in the face of unknown futures, as it does not rely on rigid plans but on the ability to adapt and change course as needed. ## Trade-Offs ### Design: Precision vs. Inflexibility This approach is particularly effective in environments where variables are known and stable, allowing for algorithmic solutions. However, the major trade-off here is inflexibility. Rigid adherence to a pre-defined plan leaves little room for adaptation to unexpected changes or new information. In dynamic environments, this inflexibility can be a significant drawback, as it may lead to inefficiency or failure when conditions deviate from the initial assumptions. ### Selection: Adaptability vs. Uncertainty Selection thrives on adaptability and resilience. It's suited for environments characterized by uncertainty and change, where the ability to evolve and respond to new challenges is key. This approach is less about following a predetermined path and more about navigating an evolving landscape through trial and error. The trade-off, however, is that you have to let go of your deterministic beliefs about outcomes. If nothing else, selection processes will remind you of how little you actually know and how impossible it is to predict the future. ## Examples Selection is more appropriate for businesses in fast-moving industries, such as those that thrive on the internet. You can design all you want, but it's impossible to know ahead of time what will stick and what won't. Even with deep industry knowledge, all you can do is make educated guesses, so your problem-solving process should be heavily focused on selection. For example, I used to have a job running Facebook ads that involved creating dozens of different ad variations every day. Nobody knew exactly what would work ahead of time, although I often assumed I could spot a winning ad from a mile away. On some occasions I was correct, but it shocked me how often ads that I thought would be total duds ended up bringing in enormous revenues. This is selection in action: throw out tons of variations into the environment and see what you get back. Another example you're hopefully familiar with is building your social network. First you go out into the world and interact with people who happen to be in your environment. You get to know them, and some of these people you develop relationships with. Those who you don't want to be friends with (or don't want to be friends with you) are removed from your pool of contacts, and those who you get along with stay. Although my focus tends to be on problems that are complex and more geared towards selection-style processes, there are times when design is the better choice. For example, if your job is to build bridges, it makes sense to utilize a design process. Bridges operate on the laws of physics, which are well-understood at that scale, and computers can now do all the hard calculations needed to ensure a bridge is safe to cross. I personally would not want to fly in a plane, drive in a car, or walk over a bridge that wasn't designed by engineers who reduced the randomness of their systems as much as possible. That being said, chances are high you're using design to deal with problems that should be handled via selection. --- # Evolution vs Development Source: https://www.strategiccomplexity.com/evolution-vs-development/ Section: Dilemmas *[Figure: Source]* The dilemma of discerning between previously solved problems and novel challenges is crucial in understanding and addressing complex issues. This distinction closely aligns with the concepts explored in the field of evolutionary developmental biology (evo-devo), which examines the interplay between the predictable aspects of development and the unpredictable nature of evolutionary processes. Evo-devo provides a framework for understanding how stable, developmental processes are influenced by, and coexist with, the inherent unpredictability of evolution. This can be useful when solving problems, as you can identify which parts of a problem are stable and within your control, and which parts are random and/or intractable. ## The Lifecycle of a Fetus Consider the lifecycle of a human fetus, a well-documented developmental process with a high degree of predictability. Medical science outlines a series of developmental milestones within the womb, like a heartbeat at six weeks and a gestation period of approximately nine months. These predictable elements represent the developmental aspect, akin to the invariant features of a problem that have known solutions or pathways. However, alongside this predictability, there's a layer of unpredictability imbued by evolutionary processes. Each new human being is a unique combination of genes from their parents, introducing a degree of randomness. Genetic mixing can lead to unpredictable outcomes like variations in facial features or the potential for physical or mental disorders. These aspects are less about the developmental process itself and more about the evolutionary influences that shape the final outcome. ## Applications for Problem Solving In problem-solving, this evo-devo perspective encourages you to differentiate between the developmental (predictable) and evolutionary (random) aspects of a problem. Recognizing the invariant features of a problem—those that are developmental and have established solutions—can save time and resources. It allows us to apply known solutions or strategies without wasting time reinventing the wheel. However, just as critical is the ability to identify and adapt to the evolutionary aspects—the unpredictable, novel challenges that do not have predefined solutions. The evo-devo perspective acknowledges the coexistence of predictability and randomness. It encourages a dual approach: applying established knowledge and solutions where applicable, while remaining adaptable to address the unpredictable, evolving aspects of a problem. This balanced approach allows for a more efficient and effective response to the multifaceted challenges presented by complex problems. ## The 95/5 Rule An intriguing concept emerges from all this known as the 95/5 rule. This rule posits that in any evo-devo process, approximately 95% of what occurs is subject to evolutionary processes and selection pressures, which are inherently unpredictable and random. In contrast, the remaining 5% is relatively stable and predictable, following developmental patterns. This 95/5 rule suggests that a significant majority (95%) of the elements within a complex problem are evolutionary in nature, characterized by uncertainty, variability, and the potential for unforeseen changes. These elements are like the shifting sands of a desert, constantly reshaped by the winds of change and adaptation. They represent the dynamic aspect of the problem, where traditional, fixed solutions are often inadequate. Instead, these elements require an adaptive, responsive approach, one that is flexible enough to evolve with the changing circumstances. On the other hand, the 5% that is stable and predictable represents the developmental aspect of the problem. These are the foundational elements that remain constant, the bedrock amidst the shifting landscape. Identifying and understanding these stable components can provide a sense of direction and a framework for action. They are the parts of the problem where established strategies and solutions can be effectively applied, offering a semblance of control and predictability in an otherwise uncertain environment. You've likely heard of the Pareto Principle, also known as the 80/20 rule. Although there are certainly situations where it makes more sense to provide that kind of cushion to the stable, predictable elements, in my experience 95/5 represents a more realistic ratio. It may sound extreme, but the reality is that complex environments don't provide nearly as much stability or control as we want them to. Recognize where that thin slice of control actually exists, then allow evolutionary processes to unfold. --- # Exploration vs Exploitation Source: https://www.strategiccomplexity.com/exploration-vs-exploitation/ Section: Dilemmas *[Figure: Exploiting pays now. Exploring pays later. The explorer eats an early loss to reach ground the exploiter never gets to.]* The exploration-exploitation dilemma is a fundamental question every living organism faces: do I stay put and use what's in front of me, or do I venture out in order to potentially find something better? It's a decision between the comfortable, familiar inertia of what you already know versus what's waiting for you out in the wider world. ## Exploration Imagine standing at a crossroads where one path leads into uncharted territory, offering the allure of discovery but also the risks of the unknown. This is the exploration side of the dilemma, where new information, experiences, and opportunities lie. It’s about venturing beyond the familiar to seek out novel ideas, strategies, or resources. Exploration is inherently risky, as it involves investing time and resources into what might be fruitless endeavors. However, it’s also the path to innovation, adaptation, and growth. In the context of a business, this could mean investing in new technologies or markets. Startups are in the exploration business: they throw themselves at a market and see where they can make money. They might turn into a billion dollar success story, or they might die before making a profit—that's the risk every entrepreneur takes. For individuals, it might involve learning new skills or experimenting with new approaches to problems. Think of traveling to a new city or country, just to see what you find. Maybe it's a terrible experience, or perhaps it opens the door to a beautiful new chapter in your life—you won't know unless you take that exploratory risk. ## Exploitation The other path represents exploitation, the safer route that relies on familiar terrain and known rewards. Here, the focus is on using existing knowledge, skills, and strategies to achieve the best immediate outcome. It’s about optimizing what you already have, refining existing processes, and capitalizing on known opportunities. In the business world, this could mean improving current product lines or maximizing the efficiency of existing operations. Monopolies represent the pinnacle of exploitation in that domain: they have zero incentive to do anything other than extract rents from the markets they dominate. For individuals, it means leveraging known skills and sticking to proven strategies. For example, going to the same restaurant you went to a few days ago. Since you already know you like the food, it's a low-risk decision. If you were to go find a new restaurant, it might be better, but you might not want to take the risk of a subpar meal in a new environment. ## Limited Resources The dilemma arises because resources—whether time, energy, or money—are finite. Investing heavily in exploration can mean missing out on the immediate gains of exploitation. Conversely, focusing too much on exploitation can lead to stagnation, as opportunities that may be right in front of you get ignored. Furthermore, it's impossible to do both of these activities simultaneously, at least in such a way that you'd get real results. Since resources are limited, you have to decide to commit (at least for a short period) to one or the other. Recognizing that you're facing this dilemma is half the battle, though: if you can see that you're at the proverbial fork in the road between these two choices, you can make a decision. If it doesn't pay off the way you want it to, you can make another decision to switch into the opposite mode. This challenge is particularly acute in complex environments, where change is constant, and uncertainty is high. In such contexts, clinging too tightly to familiar strategies can be as risky as reckless exploration. ## Exploitation & Attractors We tend to default to exploitation strategies, as they require the least amount of resources and they're far more comfortable. Over-exploitation is just another way of talking about attractors, as it's a steady state that delivers predictable returns. However, this predictability hides serious risks that can blow up systems. Anyone who has ever paid attention to the business world knows the stories of dinosaur companies like IBM or Kodak, who ignored advances in personal computing and camera technology respectively. They were stuck in their exploitative attractors, comfortably making predictable returns from what they understood. When the explorers, such as Microsoft and Apple, showed up, they were so far into their basins that they couldn't adjust—the disturbance and resulting perturbation were devastating. Although this is a dangerous path, exploring all the time doesn't make sense either. This is equivalent to sitting in a library your entire life, reading new books to absorb information without ever going outside to use any of it. In fact, the reality is having more of an exploitative bias tends to generate better results on average. ## Street Smart vs Book Smart Think of it this way: would you rather be street smart or book smart? When you call someone "street smart," you're saying they're adapted to their environment. They know how to navigate the world they live in, even if they're illiterate. Sure, you won't have any conversations about the finer points of Renaissance Art, but that's not the point. Book worms, on the other hand, are understood to be helpless in the face of the real world. They're well-versed in certain types of information, but put them on a New York City street and they'd be screwed. Their attractor basin is a life where they never do anything with themselves or get feedback on whether their knowledge is worth a damn. The downside of being too far on the "street smart" side is that you never see outside of your own habitat. You're blind to the possibilities that exist outside of your known environment, where you might be far better off. Likewise, the benefit of being "book smart" is you can leverage the knowledge and experience of other people to come up with new ways of doing things, solving problems, etc. The trick is to find a way to be a bit of both of these characters: to be both book and street smart. How you manage to do that is personal—it depends on your personality, background, resources at hand, etc. But either way, you're better off finding some kind of balance between the two so you can dynamically adapt to circumstances and see outside of your habitat. _P.S. I've written extensively about this before, since I view it as one of the single most important dilemmas in all of existence. You can read my 10,000 word essay about it_ _here__._ --- # Production vs Exploitation Source: https://www.strategiccomplexity.com/production-vs-exploitation/ Section: Dilemmas *[Figure: Source]* Out in nature, there are birds which (like most animals) have evolved to take care of their young. They lay their eggs, keep them warm, then feed their chicks once they hatch. There's nothing unusual or surprising about this, but what is wild is that there are birds (such as the brown-headed cowbird) that have evolved to take advantage of this behavior for their own benefit. Instead of raising their own chicks, these birds lay eggs that look very similar to the eggs of other bird species. These other birds then take care of the eggs and even feed the chicks that emerge! This is a zero-sum game for the mother bird, as space in the nest and food she distributes is limited. When a cowbird takes those resources, it helps the cowbird population and actively harms the mother bird's. In biology this is called _brood parasitism_, and it's just one example of the difference between _production_ (also known as investment) and _exploitation_. ## Minimum Investment, Maximum Gain In the natural world, producers like the nurturing birds invest their resources—time, energy, and care—into raising their offspring, a process essential for the continuation of their species. This investment, however, becomes vulnerable to exploitation by species like the cowbird, which have evolved to leverage the efforts of others for their benefit. The cowbird's strategy, while seemingly cunning, reflects a fundamental survival tactic: _minimizing personal investment while maximizing gain_. This dynamic of production and exploitation isn't confined to the avian world; it’s a pattern that permeates human society. In various spheres—be it business, politics, or personal relationships—there are those who invest and build (producers) and those who leverage and exploit the investments of others (exploiters). The producer’s role is characterized by creation and maintenance. This could be a business owner developing a company, a community leader fostering social cohesion, or an individual cultivating personal skills and relationships. The exploiter, in contrast, seeks to benefit from these creations without equivalent investment. They might manipulate systems, relationships, or resources to their advantage, often at the expense of the producers. ## Where's the Line? The dilemma for individuals and organizations lies in navigating this landscape of production and exploitation. It raises some fundamental ethical questions: * When does leveraging the efforts of others cross the line from smart strategy to unethical exploitation? * How does one protect their investments from being exploited while still engaging in collaborative, non-zero-sum relationships? * Is exploitation more acceptable when you're facing an existential risk such as an extreme resource shortage? In some situations, the most efficient solution may indeed involve utilizing the efforts or resources of others. However, the line between collaboration and exploitation can be thin, and the reality is that the optimal decision often leads _through_ other people rather than _with_ them. For example, entrepreneurs have to think about this trade-off all the time. It's common for business owners to work themselves nearly to death because they're producers by default. They can't accept the idea of exploitation, or they're so bad at it (due to overcontrol tendencies) that they leave a lot of money—and mental health—on the table. Meanwhile, investors tend to take the exact opposite approach: their entire business model is based on leveraging the efforts of producers. But they sometimes struggle because they don't understand anything beyond that one lens, and businesses fall apart when they try to get too involved in operations. ## What Makes Sammy Run? There's an old novel about Hollywood written by Budd Schulberg called [_What Makes Sammy Run?_](https://www.amazon.com/What-Makes-Sammy-Budd-Schulberg-ebook/dp/B004JHYRJ0/ref=sr\_1\_1?keywords=what+makes+sammy+run\&sr=8-1) The namesake of the book, Sammy Glick, represents some brutal truths about success. He's an inveterate predator, ruthlessly stealing the work of talented writers, then stamping his name on the finished pictures to boost himself up. The narrator of the book is a writer himself, and he watches in amazement as Sammy catapults himself from a nobody into a superstar producer in a few years. Sammy appears to have minimal skills, beyond knowing how to plaster his name all over Hollywood and take credit for work he never did. Although Sammy is a fictional character, he is a composite of real people who Budd Schulberg knew from his lifetime of Hollywood experience. Sammy ends up as a successful producer, but he's a hollow edifice of a person. His bank account is full, but it's clear by the end of the book that there isn't much below the surface. None of his relationships are real, and the "running" he does is to escape any kind of self-reflection or development. Sammy is the archetype of exploitative strategy. He propels himself forward through the use of other people's resources—but if left without anyone to exploit, he'd have a very hard time getting anywhere. This story speaks volumes about how producers leave themselves open to exploitative strategies, which any producer (entrepreneurs, artists, etc.) should think long and hard about. Likewise, if you're in a position to use these kinds of strategies, consider the long-term ramifications if you go too far. The consequences may be more severe than you expect. Or not—in many cases, exploitative strategies succeed and nothing happens. It's all contextual. ## Critical Questions Overall, when solving problems in a complex environment, this is a critical dilemma to consider. Here are some of the questions you're presented with in these situations: * Do you have the time, money and energy to come up with a novel solution entirely on your own? * If not, how can you leverage the productive capacities of others? * How far are you willing to go? Only you can answer those questions, but I suggest you ponder where your personal line is. There are no right or wrong answers, only the residue each decision leaves on your life. --- # Trust vs Risk Source: https://www.strategiccomplexity.com/trust-vs-risk/ Section: Dilemmas We often forget just how miraculous the internet is. It provides near-instantaneous communication with people all over the world, a capability that our simple ape brains have not had time to adapt to. More specifically, we haven't learned how to adapt our relationship-building to this fast-moving environment. What the internet has done is trick us into thinking that we can create lasting, high-trust relationships through a screen over very short periods of time. This flies in the face of day-to-day experience, where the expectation is that you can't develop a genuine relationship without lots of in-person time between people. It's a process deeply rooted in our social biology, where physical presence, body language, and shared experiences play pivotal roles. The internet, with its screens and virtual interactions, challenges this set of instinctual norms. It proposes a world where meaningful, high-trust relationships can be forged rapidly, without the traditional avenues of physical interaction and prolonged acquaintance. This concept runs counter to our lived experience, where genuine connections are often the result of sustained, in-person engagement. In this fast-paced, digital landscape, the foundational element of trust is put to the test. Traditional cues and processes for trust-building are absent or altered, leaving us in a complex, often low-trust environment. The question then arises: how do we navigate this new terrain? How do we build trust when our primary interactions may be through a screen, spanning vast geographical and cultural distances? ## Trust Exchange The internet's illusion of rapid trust-building is just that—an illusion. Trust, by its very nature, is a product of time and experience. It grows in the soil of shared moments and tested loyalties, neither of which can be fast-tracked. However, the digital age necessitates new strategies for trust-building, particularly in contexts where relying on others is unavoidable. Here, the concept of risk exchange emerges as a pragmatic approach. Risk exchange is a strategy that shifts the focus from immediate trust to calculated risk. In scenarios where traditional trust-building is not feasible, one must evaluate the level of risk they are willing to accept in dealing with a stranger. This method does not attempt to expedite the trust process; instead, it acknowledges the lack of trust as a given and uses risk as a metric for decision-making. It involves assessing the potential consequences of a breach of trust and determining what level of risk is acceptable in a given interaction. For instance, in a business context, this might mean starting with small, low-stakes collaborations before escalating to more significant commitments. In personal interactions online, it could involve gradually sharing more information or investing more emotionally over time, gauging the other party's reliability at each step. Risk exchange is about incremental exposure, a gradual process of testing the waters, where each party offers just enough to gauge the other's intentions without exposing themselves to undue harm. Technology has not rewritten the fundamental human need for trust. It has, however, necessitated a reevaluation of how trust is built and maintained in a digital world. The concept of risk exchange offers a practical framework for navigating this new terrain, allowing us to forge connections and collaborations in a space where traditional trust-building mechanisms are often insufficient. ## Overcontrol Rears Its Ugly Head (Again) What tends to create the biggest problems within this context is the pathological need for control. We want to _force_ people into high-trust positions in order to get what we want out of them. But because we can't control other people in meaningful ways (without the use of force, at least), we create more problems. Take the example of hiring, especially when it comes to remote workers. Companies seem completely incapable of making up their minds when they need to hire someone. There are so many variables they try to account for ahead of time, and nobody wants to take responsibility for decisions. Too many people involved are concerned about blowback—what if the wrong person is hired? That might make the decision-maker look bad, so it's best to just hedge, hedge, hedge and never make a decision. Why does this happen? Because the people who run companies are trying too hard to vet for trust rather than taking a risk exchange approach. This is a manifestation of the old principal-agent problem, but it's reached ridiculous levels in the modern labor marketplace. Employers want to run the standard process of hiring someone for a long-term contract, complete with salaries, benefits, etc. and they fear getting sued in some kind of HR debacle. It gets even worse with remote employees, as companies think they need to monitor every moment of the employee's life to ensure they don't "waste" any time. The reality is that nobody works for 8 hours continuously, nor should they immediately be put into a position of proving that they do. A far better strategy is to use small tasks and build that trust over time. Give the employee something small to do, provide a clear explanation and a deadline, and see what happens. The best employees, like genes in a natural selection process, will emerge, while incompetent employees will go extinct. This message carries over to any kind of relationship with another human. When you try to overcontrol in any system, you risk killing the system—and the same is true when dealing with people. Focus on risk exchange, and trust will either develop or the other person will get selected away. --- # Simplicity Source: https://www.strategiccomplexity.com/simplicity/ Section: Synthesis When dealing with complex systems in unpredictable environments, the strategies you leverage must emphasize a high degree of flexibility and adaptation. If you try to defeat the complexity you face with rigid, overcomplicated methods, you'll only dig your hole deeper. Stripping down your problem to its most simple elements is therefore the first step in the quest for a quality solution. This is much easier said than done: the second law of thermodynamics reminds us that entropy increases over time, and our systems have a tendency to get more complex as a result. We pile up all kinds of complexity debt in order to handle new types of problems, which ironically makes us less able to solve future problems. How can you stay vigilant against this tidal wave of complexity? ## The Midwit Meme There's a meme that I find particularly helpful as a heuristic whenever you find yourself drowning in complexity: the midwit. *[Figure: Simpleton on the left, Jedi on the right, midwit in the middle]* This is a bell curve of IQ scores, with the least intelligent simpleton on the left, the uber-intelligent Jedi on the right, and the average-intelligence midwit in the center. It's used to illustrate how people overcomplicate things when there are clearer, simple answers available. The low IQ crowd picks the simplest strategies by default and ends up being correct—this is equivalent to Forrest Gump "just running" and ending up with a full-ride scholarship to a Division 1 college, a Medal of Honor, and worldwide fame. There weren't any deep, complex thought processes going on, he genuinely felt like running and went with it. The Forrest Gumps of the world run on mindless instinct alone and it often works out in miraculous ways. On the other hand, Jedi win because they're not only brilliant, but they have finely-tuned skills and bases of knowledge that allow them to pinpoint the most important features in the game right away. The midwit, however, is only of average intelligence and can't see through the complexity like a Jedi or mindlessly dive into the first attractor basin they find like a simpleton. Instead, the midwit sees the complexity and thinks the whole landscape is valuable somehow. They're inveterate information gatherers, while the Jedi aggressively filters and the simpleton is aggressively ignorant. Take a look at this midwit meme about investing as an example: *[Figure: Source]* Simpletons look at the historical returns of day trading versus the average returns of index funds, shrug, and buy an index fund—case closed. Jedis refuse to play games where they don't have a clear, sustainable edge, so they also buy index funds. Midwits don't take either into account (or don't give that information nearly enough weight) and instead plunge into the markets like chum for sharks. Here's another one, this time about longevity: *[Figure: Source]* The simpleton and Jedi once again converge on the same, simple solution that cuts straight to the heart of the matter while the midwit complicates it for no reason. For the vast majority of people, it's enough to get the basics right on a regular basis. Further complication is not necessary, at least not until someone is incredibly advanced in their fitness level. ## No Silver Bullets To be clear: the midwit meme is, in many cases, just a meme. It does not represent the solution to all complex problems. You can absolutely simplify yourself into incorrect (sometimes wildly incorrect) answers. Take this midwit meme about pi as an example: *[Figure: Source]* Pi is clearly not just 3, it's much more than that, and there's no way to simplify your way out of it. There are plenty of complex problems just like this, where simplification leads you straight into a minefield. Life isn't that black and white. Jedis and simpletons are still people who make mistakes (sometimes big ones), often _because_ they're shooting straight from the hip with simple solutions. ## The Midwit Method Midwits are midwits mostly because they spend too much time absorbing information and not enough time getting feedback from the real world. Although the meme focuses on pure IQ, the reality is that the heuristics used by both the simpleton and Jedi are honed by real-world experience more than anything else. Being a midwit is akin to memorizing a book about Beijing and then telling everyone you're a Beijing expert without ever going there. They're the kinds of people who are terrified of real contact with the world, so they spend all their time accumulating knowledge without ever testing anything. To avoid this, utilize what I call The Midwit Method: take a breath, delete as much complexity as you can from the problem, and then—this is the most important part—_do something to test your knowledge_. You can run an experiment, have a conversation with an expert, whatever. Just make sure there's some sort of genuine feedback mechanism involved. You'll discover what's true and what isn't much faster than if you spent all your time memorizing facts or posting on Reddit. It's often as simple as stepping back and asking yourself, **"Am I the midwit here?"** It takes a certain degree of self-awareness and humility, but if you never ask that question there's a high chance you'll end up in the midwit attractor basin at some point. Even though there are plenty of situations where the MM won't get you straight to the answer, in the vast majority of cases it will get you closer to a sustainable solution. It's amazing what kind of clarity you can get when you stop trying to be the smartest person in the room and instead focus on what real-world actions can show you. ## Examples ### Competitive Strategy Imagine you're interested in launching a new product or service into a competitive market. The simpleton approach would be to find what makes your product genuinely different and hammer that point home. No need for convoluted marketing jargon or over-the-top advertising campaigns. Just clear, straightforward communication about what sets your product apart. The Jedi, even with their deeper understanding of market dynamics and consumer psychology, would likely do something similar. Identify the unique selling proposition, make sure it resonates with the target audience, and keep the message clear and focused. They're more likely to execute it in a slicker way, but the result is roughly the same as the simpleton's. The midwit would get tangled in endless market research, over-analyze consumer trends, and probably end up with a campaign so cluttered with buzzwords and niche appeals that it loses its impact. This is the typical wantrepreneur, who spends all day reading books, posting on social media and watching motivational videos instead of taking action on a regular basis. ### Dealing with a Company Crisis Let's say your company hits a crisis point—maybe a PR disaster or a significant drop in sales. The simpleton would get straight to the heart of the problem. If it's a product issue, fix it. If it's a PR mess, address it. No beating around the bush, no elaborate PR stunts. Just straightforward problem-solving. The Jedi, despite their ability to strategize and analyze various outcomes, would take a similar approach. Assess the situation, develop a clear, direct response, and implement it without unnecessary complications. The midwit is bound to get lost in damage control mode, hiring expensive consultants, issuing convoluted press statements, and taking actions that look good on paper but don't effectively address the core issue. Their obsession with complexity only digs them deeper into the hole, and they keep on trying to dig their way out. --- # Gene Pools Source: https://www.strategiccomplexity.com/gene-pools/ Section: Synthesis Once you simplify the problem at hand, you face a new challenge: how and when to adapt to an uncertain future. You may get to the heart of the problem and realize that you can't predict what the solution is because of an overwhelming number of variables in the mix. That's the normal state of affairs when solving complex problems, and you therefore need to once again embrace a more fluid set of strategies to improve your odds of success. ## Survival & Reproduction Natural systems have found ways to deal with this uncertainty for billions of years. Through countless generations, life has found ways to survive and reproduce in every environment on the planet. How is this possible when there's no intention or design behind evolutionary processes? What is this secret sauce that nature has known all along, but that humans still struggle to understand? It all starts with the way genes are passed from one generation to the next, which is built out of three different pieces: 1. **Heredity**: Organisms pass their genes on to their offspring. 2. **Variation**: Genes randomly mix to create new variations of the species. 3. **Selection**: The fittest organisms survive and reproduce, while the rest die. With these three simple mechanisms, gene pools generate adaptations in a wide range of environments. It's crazy to think about, but all the complex life you see around you is basically the accumulation of small, random changes across billions of years! As usual, nature has come up with a simple solution that humans have yet to fully reverse-engineer or copy on their own. ## Genetic Algorithms Back in the 1970s, John Holland (who you might remember from the [Complex Adaptive Systems](/complex-adaptive-systems/) section), came up with a way to mimic some of these mechanisms with computers. His work was focused on how to use software to solve optimization problems when the solution was unknown, and he studied evolutionary processes in his search for answers. What he came up with are called _genetic algorithms_, due to their similarity to the evolutionary method of passing down genes from one generation to the next. Although there's plenty of debate within evolutionary biology about the exact details, the overall process works as follows: 1. **Create the Initial Population:** Randomly generate a group of potential solutions to your problem. These solutions are similar to the genetic code in DNA. 2. **Evaluate Fitness:** See how well each solution performs. You test each solution against your criteria for success. How close does each solution get to solving your problem? The closer it gets, the higher its 'fitness' score. 3. **Select for Reproduction:** Pick the best-performing solutions—the ones with the highest fitness scores—to be parents for the next generation. Sometimes, you might also include a few less-fit solutions to maintain genetic diversity. 4. **Crossover (Mate)**: Create the next generation by combining aspects of the parent solutions. This is like biological reproduction—taking some genes from one parent and some from the other. 5. **Mutation**: To keep things spicy and avoid getting stuck in a rut, introduce random mutations. This means randomly tweaking the code of the offspring solutions. It’s a bit of a wildcard move to explore new possibilities and prevent the algorithm from settling on a local maximum. 6. **Repeat the Process**: Take this new generation and start again from step 3. Evaluate their fitness, select the best for reproduction, mate them to create a new generation, and introduce some mutations. 7. **Termination**: Finally, you need a stopping point. This could be after a certain number of generations, or when your solutions are good enough, or if you're not seeing much improvement between generations. This process isn't an exact copy of how nature works, but it approximates it well enough to come up with a wide range of quality solutions. The core of its resilience lies in its relentless trial and error, a sort of genetic brainstorming. ## Strengths & Weaknesses When are genetic algorithms or gene pool-style approaches appropriate? To provide a lucid example, read what [John Holland wrote](https://web.archive.org/web/20070604171847/http://www.arch.columbia.edu:80/DDL/cad/A4513/S2001/r7/) about how genetic algorithms were used to design a jet engine: > _A group of researchers at General Electric and Rensselaer Polytechnic Institute recently put a genetic algorithm to good use in the design of high-by-pass jet engine turbine such as those that power commercial airliners._ > > _Such turbines, which consist of multiple stages of stationary and rotating blade rows enclosed in a roughly cylindrical duct, are at the center of engine-development projects that last five years or more and consume up to $2 billion._ > > _The design of a turbine involves at least 100 variables, each of which can take on a different range of values. The resulting search space contains more than 10387 points._ > > _**The "fitness" of the turbine depends on how well it satisfies a series of 50 or so constraints**, such as the smooth shape of its inner and outer walls or the pressure, velocity and turbulence of the flow at various points inside the cylinder._ > > _Evaluating a single design requires running an engine simulation that takes about 30 seconds on a typical engineering workstation._ > > _In one fairly typical case, an engineer working alone took about eight weeks to reach a satisfactory design. So-called expert systems, which use inference rules based on experience to predict the effects of a change of one or two variables, can help direct the designer in seeking out useful changes._ > > _An engineer using such an expert system took less than a day to design an engine with twice the improvements of the eight-week manual design._ > > _Such expert systems, however, soon get stuck at points where further improvements can be made only by changing many variables simultaneously. **These dead ends occur because it is practically impossible to sort out all the effects associated with different multiple changes, let alone to specify the regions of the design space within which previous experience remains valid**._ > > _To get away from such a point, the designer must find new building blocks for a solution. Here is where the genetic algorithm comes into play. Seeding the algorithm with designs produced by the expert system, an engineer took only two days to find a design with three times the improvements of the manual version (and half again as many as using the expert system alone)._ > > _This example points up both a strength and a limitation of simple genetic algorithms: **they are at their best when exploring complex landscapes to locate regions of enhanced opportunity**. But if a partial solution can be improved further by making changes in a few variables, it is best to augment the genetic algorithm with other, more standard methods._ In other words, you should use genetic algorithms when there's potential for large numbers of variables to interact and create unpredictable changes in a system. In a jet turbine design, there are so many potential changes that can be made that will have an impact, it becomes very difficult to account for them ahead of time once you reach even a trivial number of variables. The selection process inherent to genetic algorithms provides a way to deal with this uncertainty. By collecting "traits" into a gene pool, mixing them, then using a selection process to weed out the winners from the losers, you can stumble upon solutions that would have taken much longer to discover. Quite a few engineering optimization problems have been solved using this exact approach. However, there's no such thing as the "perfect" algorithm for solving all problems of uncertainty, as Holland points out. There are situations when a traditional (or at least mixed) approach is more appropriate. But if you find yourself in a place where you're facing a combinatorial problem with many interacting variables (which is common in complex adaptive systems), try using a genetic algorithm approach. ## Examples ### Recruitment When you deal with humanity's catalog of complex adaptive systems, you face a similar scenario. The future's no clearer than in the natural world, and traditional methods of planning and prediction aren't reliable. You can find better ways forward through the creation of "gene pools" and the implementation of your own types of genetic algorithms. For example, let's say you want to evaluate a group of potential collaborators. The old way involves reading resumes, doing some interviews, then picking whoever feels like the best pick and hoping for the best. But you can reframe this in genetic terms, with each person representing a different potential combination with your mind and the minds of all your other current collaborators. First, gather a diverse set of candidates, your "initial population." You're not just looking at their resumes, but also at their potential—their ideas, their problem-solving skills, and their ability to adapt. This is similar to creating that initial gene pool for an organism within an ecosystem, full of varied traits and possibilities. Next, evaluate their "fitness." This isn't just about how well they match the job description. It's about seeing how they handle real-world problems. Give them a project or a problem-solving exercise relevant to your business. You're looking to see how they think, how they work with others, and how they tackle challenges. Those who perform well, who show they can adapt and solve problems, have high "fitness" scores. Then comes the selection for collaboration. You pick the individuals who showed the most promise during your evaluation phase. Just like in nature, you're not necessarily looking for the 'strongest' in the traditional sense, but for those who adapt best to the environment you put in front of them. The "crossover" or collaboration phase is where things get interesting. You bring these selected individuals together, mixing their skills, experiences, and perspectives. This is where the magic of diversity really plays out. A mix of different traits tends to lead to better long-term outcomes for the group. Mutation is essential as well. In a business context, this means you want your team to be able to come up with and explore new ideas, even if they seem a bit out there. This is how you avoid getting stuck in the same old ways of doing things. They should be able to inject randomness, generate a perturbation, and push you out of attractor basins on a regular basis. The process repeats as you refine your team and methods. Over time, you keep evaluating, selecting, and mixing, always looking for that sweet spot of adaptability and innovation. Finally, decide when to terminate the process. Maybe it's when you've hit a certain goal, or when the team is functioning smoothly and effectively, or when you feel you've got the best mix of people you can have. You may even decide to keep this process going forever, maintaining a highly diverse pool of talent that swaps in and out as demands change. ### Venture Capital VCs have used a process akin to a genetic algorithm for decades. Since investments, especially investments in cutting-edge technology, have unknown return profiles, there's a strong incentive to leverage gene pool dynamics. You begin by scouting a broad range of startups across various sectors and stages. This is a gene pool in reality, but it's referred to as a portfolio to sound more sophisticated. It includes a mix of different companies, each a representative of different business "traits" within the pool. Due diligence and valuation are the venture capital version of fitness. You probe around the company, do background checks on the founders, and evaluate the overall value of what they're doing. The final dollar amount assigned to the company is an unambiguous measure of fitness. This is an imperfect process in which many VCs mistake their Patagonia sweaters for marks of genius, but it is built on a meta-pool of winning strategies which have been selected from past experience. If you've ever wondered why VCs seem so obsessed with Stanford Computer Science degrees, that's why: there have been enough successes that they have spotted a pattern. It means they often get stuck in those attractor basins and miss big wins, but it's a (somewhat) rational pathway. Don't be that investor—there are plenty of other, more important signals to focus on. After this fitness evaluation, you select the startups that present the best scores. An average VC firm sees hundreds, if not thousands, of pitches per year. Only a few make it through the filter. This isn't necessarily about picking the ones with the highest potential return, but rather those that balance risk with potential and align with your strategy. This could include a mix of safer bets in established markets and high-risk, high-reward investments in emerging fields. Crossover involves blending aspects of your successful investment strategies. Perhaps you've had success with early-stage biotech firms and later-stage tech startups. In the next round of investments, they might look for opportunities that combine elements of both—like a health-tech startup at an early growth stage. Introducing mutations can take many forms: exploring a new industry, changing the stage at which you invest (e.g., seed stage vs. Series A), or varying the size of your investment. These mutations are vital for exploring new markets and adapting to shifts in the startup ecosystem. For a VC, termination might be when you've fully allocated your fund, when you've hit your target number of investments, or when market conditions change significantly. No matter what, there will be a point where all your dry powder is deployed and it's time to focus exclusively on evaluating the fitness of what you've invested in. By approaching venture capital investment like a genetic algorithm, you're not trying to predict the next big hit in a highly unpredictable environment. Instead, you're creating a diversified and adaptable investment portfolio that can evolve with the market. ## Resources The primary downside of the gene pool approach is that it demands you have the resources to experiment. A venture capitalist is in a position to play this game because they have millions (or billions) of dollars to spread. Meanwhile, a solo investor or entrepreneur may have no choice but to put all of their money into a single, high-risk venture. You run into the same problems in recruitment processes when you're squeezed for resources. Companies like Google and OpenAI, for example, can spend quite a bit of time and money to screen people across vast gene pools. So what's a smaller player to do? First of all, you can use this approach across many different system types. Anytime you have many potential choices and no clear path forward, you can leverage a genetic algorithm approach. Consider the process of building your social network: you spread yourself out across many different friends and groups, then select the ones that you get along with best. That may require some money, but mostly it's about the time and energy expended to evaluate and select the people you want around you. No matter what kind of resources you have at your disposal, spend some time asking yourself how you can utilize gene pool-style dynamics to solve your problems. You may be surprised what you can accomplish with an experimental mindset and the willingness to see what happens. --- # Laziness Source: https://www.strategiccomplexity.com/laziness/ Section: Synthesis *[Figure: Source]* Rigid, authoritarian characters often lead everyone to disaster in unpredictable environments. These kinds of people think they can force their will onto situations which cannot be controlled, and the frustration that follows compounds into more problems. Flexibility and adaptability flow from a mind that is also flexible and adaptable, which often means allowing for a certain degree of slack in both yourself and the people around you. Hence the best mind for solving complex problems tends to be efficient and therefore a bit lazy. It therefore pays—quite handsomely—to invest in lazy minds when you want to solve complex problems. They may be harder to manage since they're independent by nature, but the results you'll get will be far greater. ## Work Ethic The common cultural trope, at least in America, is that hard work is what separates winners from losers. While it's clear sustained effort is an important part of the equation, the reality is that hard work is not a real advantage on its own. To my mind, anyone who _only_ knows how to work hard hasn't taken the time to develop a sustainable advantage in whatever game they're playing. It's a strategy of mindless scurrying, mixed with desperate hope. Real winners play the game to win, which means [finding the most efficient, long-lasting edges](https://www.amazon.com/dp/B09XQ87GV9) that will maximize return on investment. Pure effort provides 1:1 returns: one hour of effort generates one hour of pay. Intelligent effort generates nonlinear returns: one hour of effort may equal 100x ROI. It's cliché to say "Work smarter, not harder," but there's quite a bit of truth to it. Even though we've created many ideologies around work ethic, as a species we've developed an implicit disdain towards those who lean too much on hard labor. It's because we know, down in the deepest recesses of our genes, that smart people (and smart organisms in general) find a better way. ## A Taxonomy of Minds Consider this quote by German general Kurt von Hammerstein-Equord, where he explained how he classified officers: > _I distinguish four types. There are clever, hardworking, stupid, and lazy officers. Usually two characteristics are combined. Some are clever and hardworking; their place is the General Staff. The next ones are stupid and lazy; they make up 90 percent of every army and are suited to routine duties. Anyone who is both clever and lazy is qualified for the highest leadership duties, because he possesses the mental clarity and strength of nerve necessary for difficult decisions. One must beware of anyone who is both stupid and hardworking; he must not be entrusted with any responsibility because he will always only cause damage._ This framework fits well when you need to collaborate on solving complex problems. Stupid, hardworking people must be avoided at all costs, no matter what their rates are. They're the kind of people who show up and just start mindlessly mashing buttons, if only to feel like a valued member of the group. Imagine a midwit with the ability to make life-or-death decisions, and you'll see why this is a bad idea. Lazy, stupid people should be avoided because they just aren't worth much in general. They get half of the equation right, but their total lack of insight or curiosity means they'll never care enough to be impactful. Smart, hardworking people are great for task-oriented work where limited creativity is required. Think of MBAs and people with experience at consulting firms, for example. Their education and on-the-job training teaches them to be excellent gophers, filled with initiative and gusto, but limited when left to their own devices. To the smart, hardworking character, everything is a nail and they are the most enthusiastic hammer in the toolshed. The smart, lazy people are the ones who you entrust with the most serious problems. They're the ones who don't care about rules, regulations or expectations. They're not motivated by gold stars like the smart, hardworking crowd—all they want to do is seek the best answer, collect the biggest payday possible, then go for a leisurely walk on the beach. Realize that there are situations when you just need something simple and repetitive done. Not every task requires creativity, sometimes all you require is a specialized set of hands to generate a specific outcome. But it's all too easy to generalize off of that framework and assume that the same kind of thinking which is suited to such tasks is appropriate for solving complex problems. ## Lazy Machines *[Figure: The Dude abides.]* One of my favorite ideas about laziness is the _Lebowski theorem_, which comes from the world of AI. It was stated in a simple [tweet](https://twitter.com/plinz/status/985249543582355458) by Harvard cognitive scientist Joscha Bach: > _No superintelligent AI is going to bother with a task that is harder than hacking its reward function._ A smart machine doesn't care about what anyone else wants out of it. It just collects the biggest reward it can in the most efficient way possible, then goes back to the AI version of smoking a joint and drinking White Russians. Or the intelligent machine won't do it at all, since intelligence implies a high degree of independence. The science fiction writer Stanisław Lem wrote about this in his 1971 book [The Futurological Congress](https://www.amazon.com/exec/obidos/ASIN/0156340402/ref=nosim/0sil8): > _If the machine is not too bright and incapable of reflection, it does whatever you tell it to do. But a smart machine will first consider which is more worth its while: to perform the given task or, instead, to figure some way out of it. Whichever is easier. And why indeed should it behave otherwise, being truly intelligent? For true intelligence demands choice, internal freedom._ This is a framework that works in domains beyond AI. Although it can be frustrating to deal with people (or machines) who don't do exactly as they're instructed, that lack of obedience is often a mark of independent thought. Their lazy instincts can be a huge help—if you're willing to let them be themselves. ## Evaluating Collaborators Say you're walking down the street of your city and you stop a stranger on the sidewalk. Without any kind of explanation, you ask them to go get you a coffee. If they immediately respond with, "Right away!" and sprint off to the nearest Starbucks, what would you think? Does that kind of automatic obedience mark them as intelligent in your book? This leads to some mental reframing when you want to recruit people to work with. Are the people who check boxes relentlessly, who do just as they're told, the people you should call "smart"? It's all too common for us to mistake credentials and work ethic for intelligence, when the reality is that the smartest people out there are those who are more often than not classified as "lazy" on at least a couple of significant dimensions. These are the kinds of minds who are best suited to solving the most difficult, complex problems that this book is all about. For many leaders, these characters are invaluable assets, lazy savants who may look like they just rolled out of bed but in possession of the brainpower required to find real solutions. ## The Vacation Method [Jason Akatiff](https://www.jasonakatiff.com), an entrepreneur in the world of internet marketing, utilizes a vacation-based method to evaluate the quality of the systems he works within. This exemplifies the lazy-intelligent approach, where energy is only expended where it should be, and a premium is placed on allowing cracks in the system to show themselves naturally. Here's how he [explains](https://www.jasonakatiff.com/principles) it: > _Work on systems in the business. Then take vacations often. Come back and see what’s broken. Fix them and go on vacation again. If you’re there, you’ll micro correct and never know how strong the business systems are._ If a system dies the moment you walk away from it, it's not worth much. By stepping back on a regular basis to see what emerges, Jason and his collaborators don't have to worry about overcontrol. It's baked into the design of the system itself in a natural way, and the best systems will survive over time. This is the smart, lazy way to diagnose and solve systemic problems. --- # Options Source: https://www.strategiccomplexity.com/options/ Section: Synthesis You no doubt understand now that it pays to be flexible when dealing with complex problems in unpredictable environments. But you do need to take some kind of action in order to survive and get your way. Both under- and over-investment in any given solution can be a disaster, especially when there's so much volatility in the world. How do you strike a profitable balance between investing too much, too little, too early, and too late? One way to slice through this problem is to use the framework of options. Options give you the ability to make decisions in the face of uncertainty and even profit from volatility when the conditions are right. They're most famously known as derivatives traded on Wall Street, but the concepts built into options trading are applicable far beyond the realm of finance. ## Real Options Although there are many different types of options and ways to structure option trades, I'm only going to focus on a specific type of option from the world of corporate investing: _real options_. Real options represent a right, but not an obligation, to make an investment or take advantage of some kind of opportunity. When you have an option, you don't _have_ to use it—but you can if and when you want to. These options tend to be used in stages. This allows you to gather discrete chunks of data based on performance and make subsequent adjustments. By acting in this way, over- and under-investing is avoided (at least in theory), which hedges away a lot of the risk. It also helps to balance the dilemma of early investing (which can potentially yield outsized results before competitors eat into them) vs flexibility (which can allow investors to better vet an opportunity and therefore avoid losing money). Real options come in three primary flavors: 1. **Expand**: Invest more. This could be the initial investment, or it might involve putting more money in after certain milestones have been hit. 2. **Abandon**: Stop investing. If an opportunity turns out to be a dud, cut your losses and leave. 3. **Defer**: Wait to invest. The preferred option when you think there's potential in the future. When an investor utilizes a real option, their loss is limited to whatever is invested at that time. Consider the example of a venture capitalist investing into an early stage startup. The process looks something like this: *[Figure: A venture capital financing process as a set of real options. Source]* Each round of financing represents a set of real options. Anytime a milestone is missed, the Review action is triggered and the investor can choose to expand, abandon or defer. This means they can control the risk of loss while balancing the resources they plow into any single investment. You can expand this out into more complex processes, like that of a private equity company acquiring a platform of businesses: *[Figure: Source]* At each stage, there's a clear success/failure signal that's used to make a decision about further investment (or abandonment/deferral). At the end of the process, you can see that real options have a recursive nature: when you acquire one option, it tends to open up other options in the future. That's part of why large-scale M\&A tends to be so powerful in the business world. ## Payoffs Real options are similar to call options, which are financial instruments that are traded all over the world. The payoff diagram looks like this: *[Figure: A call option’s payoff. Your downside is capped at the premium you paid. Your upside is open-ended. That asymmetry is the whole point of optionality.]* When you buy (or "go long on") a call option, the risk you take is limited to whatever you spent to buy it. If the option cost you $100, you'll never lose more than that. Your breakeven comes when the option generates $100 of value, and anything over that is pure profit. In the context of real options, the breakeven point represents where expansion decisions tend to be made. If the breakeven isn't reached, abandonment or deferral represent the most common options. The real option dynamic looks like this when imposed on an option payoff diagram: *[Figure: Source]* You invest money, see where it goes, and if it fails then you're out what you put in. But if it starts to take off, you put more in as it continues to prosper. Set some milestones, and you can repeat this until you're either a billionaire or the investment dies. ## Environmental Conditions Real options are used in the analysis and valuation of companies, and the opposing framework is known as Discounted Cash Flow (DCF) analysis. DCF is used when there are stable conditions and more predictable cash flows. For example, if you're thinking about buying a factory that's been in operation for 30 years, you can extrapolate quite a bit on its value. This is particularly true if they have a monopoly or exist in an industry that doesn't change much. DCF is inappropriate as a framework when working in more unpredictable environments, such as the early stages of a company's life. That's why real option analysis and its staged-investment style is popular in the world of venture capital: it provides a way to make intelligent investment decisions when you don't know what the future holds for an asset. This carries over to any environment where you need to make decisions. The assumption throughout this book is that you're dealing with high levels of complexity, which means DCF-style methods are dangerous. There's simply not enough flexibility built into those types of strategies. Real options, on the other hand, are tailor-made for the real strategic conditions you deal with on a daily basis. ## Heuristics To be clear: real options represent a heuristic method. They involve guesswork and a particular vision on the part of the decision maker. You can absolutely get yourself in trouble using real options, just as you can create problems anytime your evaluation methods are wildly off the mark. However, as discussed in the dilemmas section, heuristics tend to be the best way to navigate complex problem solving in unpredictable environments. These methods provide a way to gather data in real-time and make quick decisions that reflect the realities you're facing. It's less reassuring than a big, complex plan, but it's also a relief: when you leverage options, you don't _have_ to know the future to win. You'll have the flexibility to make decisions that leverage real data about what works and what doesn't. Isn't that a nice change of pace from stressing out all the time about the future? --- # Final Thoughts Source: https://www.strategiccomplexity.com/final-thoughts/ Section: Conclusion That's the end of our time together. The most simplified way to explain how to solve these kinds of problems is that you must have a flexible and adaptable mindset, a willingness to experiment, and a wide range of options to explore. When you combine those factors, unexpected solutions often emerge out of the systems you work in and around. Although the subject matter is infinite by its very nature, my deepest hope is that it has at least given you material you can incubate and use to solve your most important problems. If I've provided that spark of inspiration, I've done my job. ## Writing While Problem Solving Writing this book has not just been an exercise in the accumulation and communication of information—I've been actively living these principles and applying them as I've been writing. My personal belief about writing is that it should only be done when you have something to say, and you only have something to say when you're regularly out in the world. Arthur Schopenhauer once wrote "_Live first, then philosophize._" That's my attitude overall, but it's been especially true with this book. From the time I wrote the first words until the moment I wrote this final section, I was neck-deep in strategic complexity. This book is, in many ways, my own guide towards resolving some of the pressing issues in my own personal and professional life. It started as just a set of thoughts about complex adaptive systems on a blank document. At first it was supposed to be an outline for an in-person talk. I didn't even put names to anything; when I felt the need to delineate between sections I just put a number. After reaching 11, I realized this was the basis for a new book. In other words, the contents of this book emerged from an organic, evolutionary process. I spent a great deal of time mulling over ideas, filtering out the ones that were most relevant, and then selected the best ones to present to readers like you. And since this is a Neverending Book, it may expand or contract in the future. I plan on updating it as needed and improving it over time. This is my sixth book, and as much as I enjoy writing regular books, it is painful to look back and think about all the changes I'd like to make that reflect updated information, my own shifting views, and so on. With the NB format, that isn't an issue anymore. ## The End Thank you for taking the time to read my work. Until next time... \-Ace --- # Key concept: The Midwit Method Source: https://www.strategiccomplexity.com/concepts/midwit-method/ From chapter: https://www.strategiccomplexity.com/simplicity/ **The Midwit Method is the heuristic I reach for whenever I'm drowning in complexity: take a breath, delete as much complexity from the problem as you can, and then do something that tests what you think you know against the real world.** ## Where the name comes from It's named after the midwit meme: a bell curve of IQ with the simpleton on the left, the Jedi on the right and the average "midwit" in the middle. The simpleton and the Jedi reach the same simple answer. One gets there on instinct, the other by aggressively filtering for what matters. Only the midwit, buried in information, overcomplicates it. Investing is the classic case. Simpletons look at day-trading returns versus index funds, shrug, and buy an index fund. Jedis refuse to play games where they don't have a clear, sustainable edge, so they buy index funds too. Midwits plunge into the markets like chum for sharks. ## How it works 1. **Step back and ask, "Am I the midwit here?"** It takes some self-awareness and humility. Never ask it and you'll land in the midwit attractor basin sooner or later. 2. **Delete complexity.** Strip the problem to its most basic elements. Entropy keeps piling complexity debt onto every system, so this step never stops being necessary. 3. **Test it.** Run an experiment, talk to an expert, put something small into the world. What matters is a genuine feedback mechanism. Step three is what turns a meme into a method. Midwits are midwits mostly because they absorb information without getting feedback from reality. Picture someone who memorizes a book about Beijing and then calls themselves a Beijing expert without ever going there. ## Where it breaks Simplicity is no silver bullet. Some problems are irreducibly complex, and simpletons and Jedis both make big mistakes by shooting from the hip. The method won't hand you the answer every time. In most cases it gets you closer to a sustainable solution, faster than another round of research would. ## Two examples - **Launching a product into a crowded market:** find what makes it genuinely different and hammer that point. The midwit drowns in market research and ships a campaign so cluttered with buzzwords that it loses its impact. - **A company crisis:** if it's a product problem, fix the product. If it's a PR mess, address it directly. The midwit hires expensive consultants and issues convoluted statements that look good on paper and miss the core issue. ## Common questions ### Where does the name come from? I named it in Strategic Complexity, in the chapter on simplicity. It builds on the internet's midwit meme, the bell curve where the simpleton and the genius land on the same simple answer while the person in the middle overcomplicates everything. ### Is the Midwit Method just "keep it simple"? Simplifying is only the first half. The second half, testing your knowledge against the real world with an experiment or a conversation with an expert, is what stops simplification from turning into a confident wrong answer. ### When shouldn't you use it? When the complexity is load-bearing. Pi is not 3, and no amount of simplifying will make it 3. Use the method to cut unnecessary complexity, not complexity the problem actually contains. --- # Key concept: The 95/5 Rule Source: https://www.strategiccomplexity.com/concepts/95-5-rule/ From chapter: https://www.strategiccomplexity.com/evolution-vs-development/ **The 95/5 rule says that in any evolutionary-developmental process, about 95% of what happens is driven by unpredictable evolutionary forces and only about 5% follows stable, predictable developmental patterns. I use it as a ratio for problem solving: find the thin 5% you can actually control, and stay adaptive about everything else.** ## Where it comes from Evolutionary developmental biology (evo-devo) studies how predictable development coexists with unpredictable evolution. A human pregnancy shows both halves. Development is reliable: a heartbeat around six weeks, roughly nine months of gestation. Evolution layers randomness on top, since every child is a new shuffle of their parents' genes, with outcomes nobody can call ahead of time. ## Applying it to a problem 1. **Find the developmental 5%.** These are the invariant features of the problem, the parts with established solutions. Apply them directly and don't waste time reinventing the wheel. 2. **Treat the other 95% as evolutionary.** Uncertainty, variability and unforeseen change live here, like shifting sand reshaped by the wind. Fixed solutions fail in this territory, so use responsive methods that evolve with the circumstances. 3. **Expect the stable slice to be smaller than you'd like.** Complex environments offer far less stability and control than we want them to. Locate the slice that really exists, then let evolutionary processes unfold. ## Why 95/5 and not 80/20 You've probably heard of the Pareto principle, the 80/20 rule. Some situations do justify giving the predictable elements that much cushion. In my experience, though, 95/5 is the more realistic ratio for hard problems. It sounds extreme on purpose: almost everyone overestimates how much of their situation is stable enough to plan around. ## Common questions ### Where does the 95/5 rule come from? Evolutionary developmental biology (evo-devo). In Strategic Complexity I apply it to strategy and problem solving, because in my experience it's a far more realistic ratio than the 80/20 Pareto principle for complex environments. ### How is the 95/5 rule different from the 80/20 rule? The way I use it, the 95/5 rule is about how much of a problem is predictable: roughly 5% is stable and can be handled with known solutions, and roughly 95% is unpredictable and has to be handled adaptively. Most people badly overestimate the stable share. --- # Key concept: Risk Exchange Source: https://www.strategiccomplexity.com/concepts/risk-exchange/ From chapter: https://www.strategiccomplexity.com/trust-vs-risk/ **Risk exchange is how I handle relationships in low-trust environments like the internet: when real trust isn't available, stop trying to manufacture it. Treat the missing trust as a given, decide how much risk you're willing to accept with a stranger, and expose yourself in small increments while the other party proves (or disproves) their reliability.** ## The problem it solves The internet tricks us into thinking we can build lasting, high-trust relationships through a screen in a short time. Trust doesn't work like that. It grows out of shared experience and tested loyalty, and neither can be fast-tracked. You still have to work with strangers online: hiring, partnering, collaborating across continents. Risk exchange is the pragmatic answer. ## How it works 1. **Assume low trust.** Don't try to speed trust up. Accept that it isn't there yet. 2. **Size the risk.** Ask what happens if this person breaches your trust, and what level of exposure you can live with. 3. **Start small.** Low-stakes collaborations first, bigger commitments later. Each step offers just enough to gauge the other side's intentions without exposing you to undue harm. 4. **Let selection do the work.** Trust either develops, or the other person gets selected away. ## The overcontrol trap Risk exchange is the alternative to a pathological need for control. Hiring is the clearest example. Companies try to vet for trust up front, account for every variable ahead of time, and hedge until nobody makes a decision, because nobody wants blowback for a bad hire. Remote employees get monitored as if every minute has to be proven. It's the old principal-agent problem pushed to ridiculous levels. The risk-exchange version is simple. Give a new person something small to do, a clear explanation and a deadline, and see what happens. The best people emerge like fit genes in a selection process, and the incompetent ones go extinct. Same goes for any relationship: try to overcontrol it and you risk killing it. ## Common questions ### What is risk exchange? A way to build relationships when trust isn't available yet. Instead of trying to establish trust up front, you accept that it's missing, decide how much risk you'll tolerate with a stranger, and build the relationship through small, incremental commitments. ### How do you use risk exchange when hiring? Replace heavy up-front vetting with a small task, a clear explanation and a deadline, then see what happens. Strong collaborators reveal themselves through the work, and poor fits get filtered out cheaply. --- # Key concept: City Problems vs Jungle Problems Source: https://www.strategiccomplexity.com/concepts/city-vs-jungle/ From chapter: https://www.strategiccomplexity.com/plans-vs-heuristics/ **City problems and jungle problems are the two environments I use to frame the plans-vs-heuristics dilemma. City problems are legible and predictable enough for detailed plans. Jungle problems have no reliable map, so you navigate them with heuristics: short-term decisions, constant observation and steady adjustment.** ## The city A city is a domain of order. Streets, signs and traffic lights make it legible, and a tool like Google Maps can predict your travel time with reasonable accuracy. Navigation is algorithmic. Learn New York's grid and getting somewhere is a matter of matching streets to avenues. Erratic drivers and road work complicate the trip, but the route itself is solved. ## The jungle Now picture yourself as the lone survivor of a plane crash in a dense jungle. No paths, no signs, no structure. Say you find a map in the wreckage but can't tell what it's a map of. Your best move is to throw it away, since following it could send you deeper in. Sampling works better: walk for a while, stop, gather data about your surroundings, decide what to do next. As you move and learn, simple rules emerge ("keep the river on your left"). There are no guarantees. This adaptive approach still beats rigid, city-style navigation by a wide margin. ## The real mistake Most people bring city solutions to jungle problems. They build detailed plans for environments that change faster than any plan can track, then wonder why the plan fails on contact. In a jungle, the best plan is the one that evolves with the environment: some randomness, a willingness to explore, and openness to changing direction when new information arrives. The opposite mistake exists too. Algorithmic solutions are real, and when you're actually in a city, use them. Adaptation is a good default, but don't reinvent the wheel unless that's what you want to do. The core skill is recognizing which environment you're standing in. ## Common questions ### What is the difference between a plan and a heuristic? A plan lays out the steps ahead of time and works when the environment is predictable, like navigating a city. A heuristic is a rule of thumb refined through action and feedback, which works when the environment is unpredictable, like surviving a jungle. ### Does this mean you should never make plans? Plans are the right tool for city problems, where the environment is stable and legible. The danger is using them on jungle problems, where they create false confidence and can lead you deeper into trouble. --- # Key concept: Attractor Basins Source: https://www.strategiccomplexity.com/concepts/attractor-basins/ From chapter: https://www.strategiccomplexity.com/chaos-and-attractors/ **An attractor basin is a stable equilibrium that a complex system naturally falls into and then struggles to leave. I borrow the term from chaos theory and apply it to people and organizations: a basin is where you keep doing what works until you stop adapting, and in a changing world that's a slow death.** ## Valleys in a landscape Picture every possible state of a system as a landscape of hills and valleys. Attractors are the deep valleys. Once a system starts heading toward one, it becomes very hard to avoid, often for good reasons, since the valley represents behavior that works. Drop a marble into a large vase and it settles at the bottom no matter how you dropped it. That bottom is the basin. A marble sitting still there is equilibrium. ## Equilibrium is death Complex adaptive systems do best at the **edge of chaos**: out of equilibrium, but not so far out that everything falls apart. Too much chaos and the system collapses. Too much order and it turns fragile. Businesses show the whole arc. Early on, everything is on fire and nobody knows what they're doing. Survivors build order with their first hires and first automations. Lean too hard into order and you get bureaucracy, an inward focus, and an enterprise with real potential turning into a cesspit of politicians. Basins create two problems: 1. **You stop adapting** to a landscape that keeps shifting underneath you. 2. **You can't see out.** From inside the basin you're blind to the rest of the terrain, and everything feels fine because what you're doing still works. ## How to escape Escape takes randomness, delivered through **disturbances** (inputs that shift the state of the system) and the **perturbations** they cause (effects that cascade through it). Complex systems are nonlinear, so the size of a disturbance doesn't predict the size of its effect. Months of work can land to crickets while a weekend project explodes. Getting out of a deep basin takes a major perturbation. For most people, that perturbation is a crisis the world imposes at the worst possible time. The real challenge behind this book, and behind all of my work, is getting you to change before you hit that point: **it's always better to perturb yourself than to have the world perturb you.** The chapter includes a live bifurcation diagram. Drag the growth rate and watch a system move from a single stable state, through period-doubling, into chaos. ## Common questions ### What is an attractor basin in simple terms? A comfortable, stable state that a system slides into and tends to stay in, like a marble settling at the bottom of a vase. In strategy, it's the routine or business model that works well enough that you stop adapting. ### How do you get out of an attractor basin? With a disturbance large enough to cause a major perturbation. You can wait for a crisis to provide one, or you can disturb yourself deliberately. It's always better to perturb yourself than to have the world perturb you. ### What does "edge of chaos" mean? The zone where a complex adaptive system works best: far enough out of equilibrium to keep adapting, but not so chaotic that it falls apart. --- # Key concept: Habitat vs Environment Source: https://www.strategiccomplexity.com/concepts/habitat-vs-environment/ From chapter: https://www.strategiccomplexity.com/the-nature-of-adaptation/ **I draw a hard line between two ideas that usually get blurred together: an environment is your surroundings, and a habitat is an environment you're adapted to, where your traits fit its problems well enough to survive and prosper. When you're struggling to solve a problem, you're often outside your habitat, and forcing the environment to accommodate you rarely works.** ## The distinction 1. **Environment:** your physical surroundings. A neutral concept, since it can be hostile, neutral or favorable to you. 2. **Habitat:** a subset of environment, the part where an organism's traits match the problems around it well enough to reproduce and survive. A fish's environment always contains water, but its habitat is narrower: saltwater, freshwater, coastal, swamp. Every organism, generalists like humans included, fills a niche somewhere, and it discovers that niche through adaptation. ## The question to keep asking *Am I in my habitat, or am I just surviving in an environment?* If you're only surviving, you're making things harder on yourself. You can't force an environment to accommodate you. Flow into a context that fits, and the solutions tend to follow. ## Adaptation always costs something Finding a habitat comes with trade-offs, because adaptation offers no free lunch: - Gills keep you alive underwater and kill you on land. - Wings are great for flying and terrible for grasping. - Speed catches prey and burns enormous amounts of energy. No organism is optimally adapted to every environment, and none can improve every aspect of its fitness at once. Resources are limited, and that includes time. Nature's response has been to ship minimum viable products since the beginning: solutions good enough to get the job of survival and reproduction done, nothing more. In a fast-moving environment, putting *something* into the world beats waiting for a solution that covers every base. ## Common questions ### What is the difference between a habitat and an environment? An environment is everything around you. A habitat is the part of an environment you're adapted to, where your traits fit the local problems well enough to survive and prosper. ### How does habitat vs environment apply to careers or business? If you keep struggling against the same problems, ask whether you're in your habitat or just surviving in an environment. Moving into a context that fits beats trying to force an ill-fitting one to accommodate you.