Avoiding Existential Threats as a Non-Zero-Sum Game

How a new set of challenges might induce a phase change (i.e., a sudden leap in complexity) in human evolution.

In Robert Wright’s 2000 book Nonzero, he argued that biological and cultural evolution have a direction. The evolutionary process does not proceed randomly because there is an underlying logic to it that inevitably leads to greater levels of complexity over time. This complexity comes in the form of an increase in the scope of non-zero-sum games. In both biological and cultural evolution, new adaptations (genetic, social, or otherwise) consistently emerge that convert zero-sum situations into positive sum situations. This conversion cashes out as an increase in complexity. 

More recently, and totally independently of Wright, computational biologist Yuri Wolf and colleagues published a paper in The Proceedings of the National Academy of Sciences that generalizes Wright’s theory. In their 2018 paper entitled “The physical foundations of biological complexity”, Wolf and colleagues argue that competing interactions induce complexification at all levels of analysis, including in the non-biological world. According to their theory, complexification is always a process by which “competition begets cooperation”. Even at the molecular level of analysis, competition between long-range and short-range interactions in a molecule cash out in a more complex molecular structure. Thus, biological and cultural complexification are particular instances of a general process that goes on even in the non-biological world. 

As Robert Wright argued in Nonzero, and as the complexity scientist Peter Turchin echoed in his 2015 book Ultrasociety, it is warfare that has driven the complexification of human civilization for the last 10,000 years or so. This is because, in the context of warfare, every individual within a group is engaged in a non-zero-sum game with every other individual in the group. If the group wins, everybody within the group wins (even if “winning” just means not dying or being enslaved). If the group loses, everybody within the group loses. War itself is a zero-sum game, of course, but the individuals within a group are engaged in a non-zero-sum game. Competition between groups begets cooperation within groups. 

When human groups go to war with each other, the largest, most cohesive, most cooperative groups will tend to win. For this reason, any cultural innovation that allows a group to scale up in size or which allows multiple smaller groups to come together into a larger group would have been selected for. Over the last 10,000 years this process has propelled us from groups of hundreds of nomads to global civilizations with millions of inhabitants. 

That process must now come to an end. The threat of nuclear annihilation is too great to allow nation-states to further their own interests through warfare. Mutually assured destruction is a terrible, nightmarish kind of non-zero-sum game which has largely put a stop to total war between superpowers (although smaller skirmishes can still occur). 

What does that mean for the future of cultural complexification? If warfare can no longer drive cultural complexification, is that process now at an end? Will we, as Francis Fukuyama suggested in his now (in)famous 1993 book The End of History and the Last Man, remain in a world of liberal democratic nation-states forever more? If not war, what then could propel us into higher levels of cultural complexification? 

I have a hypothesis. 

Existential Threats as a Non-Zero-Sum Interaction

Modern technology may have put an end to total war between superpowers, but in doing so it has given birth to a new kind of non-zero-sum game: intrinsic existential risk. I make a distinction here between intrinsic risks and extrinsic risks. Intrinsic risks are risks that you have some control over. Every time you get in a car, you risk having a fatal crash, but you can exert some control over this risk by wearing a seatbelt, going the speed limit, etc. The risk of having a fatal car crash is at least partially intrinsic. On the other hand, whether or not you have a genetic disease is largely an extrinsic risk. There is no decision you can make to reduce your own risk of having a genetic disease like cystic fibrosis. 

Humanity has always been threatened by extrinsic existential risks. At any point in time during human evolution an asteroid or comet could have struck earth and wiped us out, and there is nothing we could have done about it. Extrinsic existential risks do not change anything about the nature of interdependence between human beings. Interdependence is just another way to express a non-zero-sum situation. Individuals engaged in a non-zero-sum game with each other are said to be interdependent. 

Modern technological advances have created intrinsic existential risks. This is a relatively recent phenomenon and one that humanity has never encountered before. Nuclear war, artificial intelligence, manmade pandemics, authoritarian techno-states, and runaway climate change are all potential existential risks created by recent technological advances (whether or not all of these are actual X-risks is uncertain). Presumably, technological advances in the future will continue to create more existential risks (one could imagine, as Nick Bostrom has, a self-replicating nano-technology that eats up the biosphere). Because these are manmade X-risks, they are all intrinsic. At least in theory, humanity has the power to reduce the risk associated with each of them. 

The advent of intrinsic existential risks puts our species in a novel situation: We are now engaged in a non-zero-sum game with every other human being on the planet. We will either make it through this period of technological advancement without wiping ourselves out, or we won’t. If we make it through, we all benefit. If we don’t make it through, we all lose. Either way, we are all in this together now.

Complex systems often go through phase changes when faced with a novel situation that threatens the integrity of the system. The advent of intrinsic existential risk is precisely the kind of situation that can induce a phase change in a complex system. In this case, the complex system consists of the interdependent relationships that now characterize all of humanity. The phase change refers to the re-organization that may need to occur to deal with the novel problem of intrinsic existential risks. In order to understand how and why that might happen, we will first need to have some general understanding of phase changes in complex systems. 

Phase Changes and Complexification

Alicia Juarrero is a philosopher who has spent her career exploring the scientific and philosophical implications of our new understanding of complex, dynamical systems. In her 2002 book Dynamics in Action, Juarrero discusses the importance of phase changes in complex systems. In normal discourse, a phase change simply refers to the transition from one state to another. This includes the transition from water to ice, for example, or the fusion process that produces helium from hydrogen. These are both relatively simple phase changes. Complex dynamical systems go through phase changes that are a little more complicated than that. The phase change that produced the transition from single-celled to multicellular life is an example of a more complex phase change. In her 2002 book Dynamics in Action, Juarrero reviews some characteristics of phase changes in complex dynamical systems. These are:

1. Phase changes only occur when a dynamical system becomes unstable. These systems can become unstable because of external events or because of their own internal dynamics.

2. Instability in a dynamical system can cause that system to either fall apart or go through a phase change. Instability does not inevitably lead to a phase change.

3. If the dynamical system doesn’t fall apart, the phase change will result in a higher level of complexity (i.e., the system will become more differentiated and integrated). 

4. Phase changes are inherently unpredictable. It is impossible to tell ahead of time whether the dynamical system will fall apart or go through a phase change. If the system does go through a phase change, it’s impossible to predict the particular form that its new organization will take.

5. Because time and context play such crucial roles in the unfolding of a phase change, narrative reconstructions (which give due importance to time and context) are the best way to retrospectively explain and understand phase changes.

Each of these points requires a brief explanation. I will discuss the first four points now and come back to the fifth point at the end of this essay. 

1. Instability precedes a phase change

Why is instability necessary for a phase change to occur in a complex system? Perhaps the easiest way to visualize this comes from dynamical systems theory. Dynamical systems theory construes the state of a system as movement through phase space. The phase space of most complex systems will be highly multi-dimensional, meaning that there will be hundreds or thousands of dimensions along which the system can change. This makes it impossible for our third dimensional brains to visualize movement in phase space for these kinds of systems. Nevertheless, visualizing a system with two or three dimensions can still be useful for understanding some general characteristics of dynamical systems. 

Phase spaces can be visualized as landscapes with basins (called attractors) and hills (called repellers). Attractors are states that the system has a tendency to revisit and to stay in when it visits them. A phase change can be thought of as a movement from a shallow attractor to a deeper attractor. Visualizing this movement can help us to understand why instability is necessary for it to take place. In the image below, the state of the system is represented by the red ball.

The system is currently stuck in attractor A, but attractor B is a more functional, more stable state. How can the system move from attractor A to attractor B? As Juarrero put it:

… complex systems don't wander out of a deep basin of attraction, nor do they fall off a page with high ridges around the edge, so to speak. For bifurcations and phase changes to occur, the current landscape must show signs of flattening out: it must first become unstable. (Juarrero, 2002 p. 255)

There are basically two ways to visualize how this movement might occur. In the first place, the attractor landscape could temporarily flatten out. This would allow the ball to escape attractor A and potentially roll over to the deeper, more stable attractor B. The other way is to create vibration in the attractor landscape so that the ball is shaken out of its current location and can potentially find another location to end up in. Each of these visualizations is equivalent to an increase in the entropy of the attractor landscape. Entropy can be thought of as the uncertainty or unpredictability of the system. A totally flat attractor landscape would have maximum entropy because the movement of the system in that landscape would be totally random. A temporary increase in entropy (represented by a temporary flattening of the landscape) is necessary for phase changes to occur. Let me give two real-world examples to make this more concrete. 

Darwinian evolution is often visualized as a “fitness landscape” in which peaks are states of higher fitness while valleys are states of lower fitness. Evolution by natural selection is constantly moving species uphill towards higher levels of fitness. This is, in fact, the same thing as an attractor landscape but the landscape has been turned upside down. Fitness peaks are functionally equivalent to basins of attraction in a dynamical system. 

How is it that a species might move from one peak on this landscape to another? This is easy if the peaks aren’t separated by valleys. But what if a species has to move downhill before it reaches another peak? In the image below, for example, how can the species represented by the red ball move from peak A to peak B?

Natural selection is always moving species uphill on a fitness landscape, so natural selection by itself cannot solve this problem. 

There are multiple processes that can help a species move from a lower peak to a higher peak, including sexual selection, genetic recombination, and genetic drift. Here I just want to talk about genetic drift because it is the most relevant process for our purposes. While selection is decidedly non-random, genetic drift represents the process by which new mutations that are evolutionarily neutral (i.e., they don’t substantially increase or decrease the fitness of an organism) can spread through populations through chance. 

In his 2019 book Life Finds a Way, biologist Andreas Wagner summarizes the importance of genetic drift for traversing fitness landscapes: 

… genetic drift affects life’s evolution in two fundamental ways. In the short run—the few million years needed to form new species — it helps evolution attain new and higher peaks in nature’s genetic landscapes. In doing so, it accelerates the creation of new species with unique lifestyles. And in the long run, drift alters the architecture of genomes and increases their potential for future innovation. As different as these manifestations of genetic drift are, they share a common principle: good things can happen when evolution is free to explore the landscapes of nature’s creativity, temporarily liberated from selection’s shortsighted and relentless uphill drive. (p. 79)

I said above that “vibration” in an attractor landscape is one way that a system might move from one attractor to another. In other words, the system needs to “shake things up” in order to escape a non-optimal attractor. Genetic drift does this. Because of its quasi-random nature, genetic drift can shake things up in a species’ genetic architecture enough to allow it to traverse from one fitness peak to another. This traversing can only occur, however, if selection isn’t too strong or if there is a temporary decrease in the strength of selection (which would correspond to a temporary flattening out of the attractor landscape). The effect of genetic drift on traversing fitness landscapes is therefore one example of how instability (in the form of random changes induced by drift) is necessary for a phase change.

The second example of an entropy-induced phase change comes from modern cognitive science and is one that I have discussed on this substack before. Some cognitive scientists conceptualize an insight as a phase change in a dynamical system. Stephen and Dixon, for example, have shown that insights are preceded by an increase in behavioral entropy and result in a decrease in behavioral entropy such that there is even less entropy than there was before the insight. The figure below is adapted from one of their papers (I inverted it and added text to make it more understandable) and is meant to represent this process.

Entropy increases when somebody “breaks frame” (i.e., recognizes that their current framing of the problem is not functional) and decreases when they establish a new, more functional frame. 

Psychological entropy can be thought of as uncertainty. In order to have an insight, there must be a temporary increase in uncertainty so that the previous frame can be broken and a new frame adopted. You can’t break a frame unless you are uncertain about it. This is another example of how instability (in the form of uncertainty) is required for a phase change to occur. 

2. Phase changes are not inevitable

Instability, of course, does not always lead to phase changes. Sometimes instability just causes the system to fall apart. Species do not necessarily move from lower fitness peaks to higher fitness peaks. To the contrary, species go extinct all the time when faced with novel circumstances. Similarly, not all problems that could induce an insight do so. Sometimes, if the problem is dramatic enough, it will simply induce a nervous breakdown or even a psychotic episode (although psychotic episodes, in some cases, might just be a protracted and dangerous kind of insight; e.g., here and here). 

The point is that instability is not an unalloyed good that automatically propels a system into higher levels of complexity. Instability represents a real danger. Systems fail and fall apart all the time. As such, phases changes are never inevitable. Chaos is both creative and destructive. It represents both great opportunity and grave danger.

3. Phase changes result in higher levels of complexity

If the system doesn’t fall apart, it will come out on the other side of a phase change with a higher level of complexity. As Alicia Juarrero put it in Dynamics in Action: 

Far enough from equilibrium… dynamical systems can abruptly and irreversibly undergo a radical transformation. On the other side of this "bifurcation", a system either reorganizes into a higher level of complexity characterized by renewed potential and possibilities, or falls apart. (p. 9)

This increase in complexity has manifested as the transition from single-celled life to multicellular life, from small groups of hunter-gatherers with little division of labor to civilizations with millions of highly specialized workers, and from the foolishness preceding an insight to the wisdom one attains after an insight. Each of these represents a leap in complexity. 

One prominent definition of complexity defines a more complex system as being simultaneously more differentiated and integrated. This kind of complexity can be understood as an increase in the power of a system. More complex systems are typically more powerful than less complex systems in the sense that they have more behavioral options available to them and suffer from less internal conflict. For example, as we become more cognitively complex over the course of development (through the insights we achieve), we end up with more behavioral options available to us while simultaneously decreasing internal conflict between competing beliefs and goals (e.g., the goal of losing weight and the goal of having some delicious ice cream). In other words, cognitive development through insight makes us more cognitively powerful. As civilizations become more complex, through greater specialization of labor (i.e., differentiation) and internal cohesiveness (i.e., integration), they also become more powerful. 

When phase changes are successful, they result in a system that is more complex and therefore more powerful, which corresponds to a system that is more capable of responding to novel circumstances and less prone to internal conflict. 

4. Phase changes are unpredictable

It is impossible to tell ahead of time whether a complex system will go through a phase change or fall apart. It is also impossible to tell ahead of time what the system will look like after a phase change (although some general principles will apply). This is so because systems going through a phase change are extremely sensitive to initial conditions. This is most famously known as the “butterfly effect”. Complex systems with even slightly different initial conditions can have wildly different trajectories. 

This does not mean that complex systems are unconstrained in how they can transform. A bunny rabbit will not become a butterfly no matter what the circumstances are. It does, however, suggest that attempts to predict and tightly control the outcome of a phase change in a complex system are doomed to fail. This is reflected in the fact that insights rarely occur when somebody is trying to have an insight and very often occur when somebody is not actively thinking about the problem at hand. Trying to control an insight is often counterproductive. In the same way, micromanaging a team can reduce creativity and flexibility. 

The unpredictability and uncontrollability of phase changes suggests that an “engineering” approach (which focuses on top-down design) might not be appropriate for understanding how they occur. Phase changes typically involve bottom-up, spontaneous organization that emerges from the interactions of each of the parts of the system with each other and the environment. 

The fifth characteristic of phase changes (that they are best understood with narratives) will be discussed further below.

Phase Changes and the Evolution of Management

In his 2000 book Evolution’s Arrow, biologist John Stewart argues that when leaps in complexity are made in biology they tend to be accompanied by new forms of “management”. This management exerts some control over the parts of the system to bring those parts in line with the overall goals of the system at large. This management does not necessarily involve top-down control. It can involve informal regulation between and within the parts of the system. Within your own body, for example, “selfish genetic elements” are attempting to become over-represented in your gametes (usually sperm), thus putting their genetic interests forward at the expense of your overall genetic interests. Your body exerts some “management” on these selfish genetic elements so that they can’t succeed at this, although occasionally they do. You also have mechanisms for managing the “selfish” interests of your own cells, which may attempt to replicate even when that replication is detrimental to your body as a whole. This management is not necessarily top-down but emerges from interactions between the cells themselves. Your own cells commit suicide (referred to as “apoptosis”) when they detect that they have ceased to be functional or when another cell sends them a signal to do so. When this kind of management breaks down you can get cancer. 

Human groups have a variety of management strategies for keeping selfish individuals from harming the interests of the group. Individuals, for example, have evolved a moral psychology that automatically judges and condemns people who act too selfishly. We also create group-specific norms and institutions that help to encourage group-beneficial behavior over selfish behavior. More recently, human groups have instituted written laws and formal court systems to censure selfish or anti-social behavior. Although written laws are a kind of top-down system of management, the most functional systems of law involve a bottom-up evolutionary process by which laws are interpreted and enforced based on real world interactions (e.g., the English common law system). All of these forms of management have allowed human groups to continue growing larger in size and complexity. 

The relation between management and complexity suggests that some new form of management must emerge that will keep selfish individuals and groups from imposing existential risks on everyone else. What will that management look like? There’s no way to know in advance, but I would suggest that it will be quite different from the informal and formal management that currently characterizes human civilization. The two most obvious forms of management are morality (broadly construed) and state control. I do not think that either of these can contain intrinsic existential risks. I will briefly address them both.

Morality as management

If you regularly read this substack, you probably already know what I think of morality, so I’ll be brief. Morality makes people blind and stupid. When we become morally invested in a problem, we automatically lose our ability to think clearly about it. In his book The Righteous Mind, Jonathan Haidt explains: 

Morality binds and blinds. This is not just something that happens to people on the other side. We all get sucked into tribal moral communities. We circle around sacred values and then share post hoc arguments about why we are so right and they are so wrong. We think the other side is blind to truth, reason, science, and common sense, but in fact everyone goes blind when talking about their sacred objects. (Haidt, 2012 p. 332)

Everyone goes blind when talking about their sacred objects. Humans have the ability to treat almost anything as sacred, including physical objects, people, ideas, and empirical beliefs. Our moral psychology evolved in the context of tribal warfare. We are very good at construing our group as victimized angels while construing our enemies as morally perverse demons. There is plenty of evidence suggesting that people will gladly ignore and distort evidence in the service their sacred moral values (much of this is reviewed by Haidt in The Righteous Mind). 

The more something becomes a moral issue, the more we become unable to think clearly about it. Our moral psychology evolved to make us better at waging war with outgroups. It did not evolve to help us engage in scientific inquiry or solve collective action problems with people who may have very different moral values, customs, and traditions from us. In fact, morality makes us worse at cooperating with outgroups because morality, by its very nature, cannot stand for moral disagreement (anyone who has conflicting moral values is not just different, but evil). The problems posed by intrinsic existential threats are complicated enough that we need to be able to think clearly about them. Moralizing will only be an obstacle to that.

State control as management

Government regulation will no doubt play some role in managing existential risks, but (at least in its current form) it cannot solve the problem on its own. In order for state control to totally solve the problem of intrinsic existential risk, it would require a global governing body with nearly unlimited surveillance capabilities. Something like this might end up being necessary, but unless we figure out some way to keep a global surveillance state from abusing its power, it will inevitably turn into an authoritarian nightmare. History and a common sense knowledge of human nature are very clear: Power that can be abused will be abused. If a global soviet-like state were to arise with modern technology, including constant surveillance with facial recognition, microchip tracking from birth onwards (with a microchip being necessary for making purchases, getting a job, etc.), and the capacity to turn off one’s ability to participate in the economy for any reason at any time, then the rest of human history might look, in the words of Orwell, like “a boot stamping on a human face— forever.” This possibility represents its own kind of existential risk. 

All of that technology is already available, and if you don’t think it could be used in the ways I’ve described then you might consider studying the 20^th century a little more closely. A state that is powerful enough to completely control intrinsic existential risks is too powerful. Unless some other form of management is devised to keep the state in check, this will not be a viable solution to the problem.  

Looking to the Past for Solutions in the Present

The archaic revival is this overarching metaphor that is the way for us to go to save our necks at this point. When a culture gets into trouble, instinctively… it goes back through its own past until it finds a moment where things seem to make sense, and then it brings that moment forward into the present. — Terence Mckenna

Modern problems require modern solutions. That much is obvious. I would suggest, however, that our ancient traditions, which have stood the test of time for millenia, are not irrelevant. Although the so-called new atheists like to construe ancient modes of social organization (i.e., religion) as nothing but superstitious nonsense, I would suggest that our newfound understandings of cultural evolution and complexity will render this attitude obsolete. Both the new atheists (e.g., Harris, Dawkins) and religious fundamentalists treat religious beliefs as if they are scientific theories. Religious beliefs are not scientific theories. “Creationism” is obviously not a replacement for Darwinian evolution, but at the same time the creation story of Genesis (and other traditions) should not be regarded as merely superstitious bullshit (although its literal/historical interpretation is obviously wrong).  

Our emerging understanding of cultural evolution indicates that long lasting traditions may have value that is not immediately obvious. In his 2016 book The Secret of Our Success, Joseph Henrich states:

Like natural selection, our cultural learning abilities give rise to “dumb” processes that can, operating over generations, produce practices that are smarter than any individual or even group. Much of our seeming intelligence actually comes not from raw brainpower or a plethora of instincts, but rather from the accumulated repertoire of mental tools (e.g., integers), skills (differentiating right from left), concepts (fly wheels), and categories (basic color terms) that we inherit culturally from earlier generations. (p. 12)

Aaron Lightner and Ed Hagen, in their recent paper, suggest that supernatural beliefs often represent just this kind of mental tool which helps us to get a grip on the world around us (I discuss that paper at length here). In their own words:

…supernatural explanations can be adaptive and simplified falsehoods that serve as starting points for modeling, at a high level of abstraction, the complex causal processes that generate noisy and ambiguous observations. (p. 2)

 These kinds of “adaptive and simplified falsehoods” will be subject to cultural evolution. Over time, cultural evolutionary processes will cause more functional supernatural beliefs to proliferate. Although they are “falsehoods” in the sense that they are not like scientific theories, they can still be very useful for understanding the world around us and acting appropriately in it. I wouldn’t be inclined to call them falsehoods, but I won’t argue semantics here (a fiction can be “more real than reality” if it offers up a more generalizable model of the world; see Hoel, 2019). 

This understanding of religious and supernatural beliefs vindicates something that Jordan Peterson said in his 1999 book Maps of Meaning:

How is it that complex and admirable ancient civilizations could have developed and flourished, initially, if they were predicated upon nonsense? If a culture survives, and grows, does that not indicate in some profound way that the ideas it is based upon are valid? If myths are mere superstitious proto-theories, why did they work? Why were they remembered?…

Is it actually sensible to argue that persistently successful traditions are based on ideas that are simply wrong, regardless of their utility? Is it not more likely that we just do not know how it could be that traditional notions are right, given their appearance of extreme irrationality? (pp. 7-8)

But how is it that our ancient mythologies could be right? They are clearly not describing historical events. Below I will suggest that ancient mythologies are describing, implicitly, the structure of a phase change. We will now come back to the fifth characteristic of phase changes. 

Phase changes are best understood with narratives

Because time and context play such crucial roles in the unfolding of a phase change, narrative reconstructions (which give due importance to time and context) are the best way to retrospectively explain and understand phase changes. Alicia Juarrero discusses this important characteristic of phase changes in Dynamics in Action:

Across phase changes… there are no established dynamics that can serve as the context from which the parts derive their meaning; the change itself in the dynamics governing the system's stable states needs explaining. Since these phase changes are unpredictable, the only way to explain them is with a retrospective narrative that retraces the actual leap. (p. 9)

Phase changes embody essentially incompressible information. That is, there exists no law or algorithm more concise than the process itself that can capture and describe what happened. That is why fiction and drama, Bible stories, fairy tales, epics, novels, and plays will always be better than deductions or formulas for explaining personal transformations of this sort. The rich, vivid descriptions and reenactments that these genres provide represent meaningfully for the reader and spectator the processes that precipitate such personal transformations. They do so by paying special attention to the role played by both the agent's internal dynamics and the particular environmental perturbations that drive a system far from equilibrium. (p. 235)

Particular phase changes are best understood in the form of a retrospective narrative. But what if you want to understand the general character of phase changes in a way that is practical and functional for your own life? 

Mythology and phase changes

I think that’s what mythology is. The mythological narratives that have underpinned the civilizations of the past have a general character, described as “the hero’s journey” by Joseph Campbell and as the “meta-mythology” by Jordan Peterson. The figure below represents this meta-mythology.

The meta-mythology can be described as having four stages: 

1. An initial stable state

2. An anomaly that disrupts that state

3. A descent into chaos as a result of the anomaly

4. A re-emergence into a higher form of order

As an example, I will use the narrative of the Buddha’s enlightenment to demonstrate this pattern. The meta-mythology begins with order. There is some stable, pre-adventure, paradisal state that the characters find themselves in. In the case of the Buddha (known as Siddhartha Gautama before his enlightenment), he is living a life of pleasure and comfort. This was provided for him by his father, who has set out to protect Siddhartha from the knowledge of suffering and death. In the midst of this stability, there is some anomalous information which disrupts the current narrative. For Siddhartha, this anomalous information came through his exposure to suffering, aging, and death. Next, there is a descent into chaos, in which the previous narrative structure has shattered, but a new one has not yet taken its place. Siddhartha experiences a psychological crisis. He leaves the protection of his home and goes on a journey to find a solution to the problem of suffering. Finally, there is the adoption of a new narrative, which restores stability while accounting for the anomalous information that disrupted the previous story. Siddhartha achieves enlightenment, becoming the Buddha, and returning to the community to share his newly found wisdom. 

For all intents and purposes, this is the same as the pattern that characterizes a phase change. I have discussed this before in the context of an insight, but it’s worth coming back to here.

Phase changes in complex systems emerge at the border between order and chaos (also known as self-organized criticality) and they involve a temporary increase in entropy followed by a re-emergence into a higher level of complexity. The meta-mythology, as described by Jordan Peterson, also emerges at the border between order and chaos and involves a descent into chaos and re-emergence into a higher form of order.

Phases changes in complex systems occur when the system is faced with instability (from an outside threat or from its own internal dynamics). Similarly, the pattern of behavior described by the meta-mythology represents what to do when you don’t know what to do. As Peterson (1999) put it: 

The pattern of behavior characteristic of the hero – that is, voluntary advance in the face of the dangerous and promising unknown, generation of something of value as a consequence and, simultaneously, dissolution and reconstruction of current knowledge, of current morality – comes to form the kernel for the good story, cross-culturally. That story — which is what to do, when you no longer know what to do — defines the central pattern of behavior embedded in all genuinely religious systems… (pp. 180-181). 

What if the central pattern embedded in all genuinely religious systems is actually the same as the pattern that characterizes a phase change in a complex system? We might want to pay attention to that. 

What’s the Solution?

Like other phase changes in complex systems, the new form of management that will emerge in response to intrinsic existential risks will likely emerge from the bottom-up, through the interactions of the parts of the system (in this case, individuals and nation-states). I do not think it will be imposed from the top down by a global state. What that new form of management will look like in detail, however, is impossible to predict. 

But I am hopeful. I think that this kind of problem is exactly the kind of problem that human beings evolved to solve. Evolutionary anthropologists Ed Hagen and Zachary Garfield have argued that the evolution of the human brain was propelled by social and sexual selection for the propensity and capacity for joint utility improvement. This is another way of saying that human beings were selected for their propensity and capacity to discover and facilitate non-zero-sum games (see section 3 of my “Intimations” essay for a summary of this idea). Avoiding existential threats might be the most important non-zero-sum game in human history.

Tradition has epistemic authority because of the logic of cultural evolution. Science has epistemic authority for different reasons, not least of which is its proven track record of increasing our ability to predict and control the world around us. To the extent that there is conflict between tradition and science, there is conflict within us and within our culture. My suspicion is that reconciling this inner conflict (which I attempt to do here) will be important for solving the problem of intrinsic existential risk.

Whatever the solution is, I hope to have brought to light some relevant facts in this post:

1. Intrinsic existential risks are a novel phenomenon in human evolution.

2. Because of the existence of intrinsic existential risks, every human being is now in an interdependent relationship (i.e., a non-zero-sum game) with every other human being on the planet. 

3. Radically novel problems cause instability in complex systems and typically require a phase change, and therefore an increase in complexity, in order to overcome them. The advent of intrinsic existential risks is probably no different.