John Nash’s View on Mental Illness

In 2007, John Nash, one of the pioneers of game theory, delivered a speech reflecting upon his own history with mental illness, specifically schizophrenia. Not only did he not regret his circumstances: he attributed much of his success and many of his most brilliant ideas to his ability to think outside of the academic norms of his time while under psychiatric care.

Throughout the years, he argued that being labeled insane was the consequence of deviating from societal norms, and thus compared it to being “on strike” against the consensus over reality. As a game theorist, the notion of going on strike was particularly significant to him because it’s the mechanism through which individuals can oppose the pressure towards a sub-optimal state.

John Nash called us to re-evaluate the role of severe mental illness for our species and the consequence of society’s pressure to conform to a mental health paradigm for our evolutionary future.

John Nash receiving the Nobel Prize

Game Theory

Game theory applies mathematics to the study of interaction between rational agents, which make it broadly applicable to economics, political science, biology, and computer science.

One the key concepts introduced by John Nash is the Nash Equilibrium (NE), which consists of the set of strategies among players where no player has anything to gain from deviating. Rational agents behave in such a way as to converge towards the NE because it is the necessary outcome based on their self-interested motivation to maximize their gain after factoring in their understanding of their opponents’ possible strategies.

The NE is not necessarily the best outcome for all players, and it’s often the case that it is not, as evidenced by the prisoner’s dilemma, where each prisoner gains more by successfully betraying the other than they do by effectively cooperating and staying silent, thereby leading both players to betray each which ultimately results in higher jail time for all.

Many games have multiple NEs, such as in the Bach or Stravinsky game (AKA the Battle of the Sexes), and in those cases the players converge towards one of the NEs and tends to remain there because the only reason to deviate is if there was a guarantee that all players coordinate towards a superior NE.

The two NEs are (Ballet, Ballet) and (Fight, Fight)

This model proves that rational agents often fail too coordinate to achieve mutually beneficial outcomes precisely because they are rational, and this dynamic can lead to terrible consequences.

The Segregation Game

In “Microforces and Macrobehavior”, Thomas Schelling, one the great game theorists of all time, build a model to explain segregation in nature. He constructed a simple game where 100% integration gains all players 1, 100% segregation leads to all players to gain 0, and anything in between leads to a -1 payoff. Based on this model, any random initial configuration where the players are free to move where they please leads to segregation, even though (segregate, segregate) and (integrate, integrate) are equally valid NEs, with the latter yielding the highest payoff.

This model explains why de-segregation is so difficult: it requires a robust coordination mechanism that can effectively manage the entire population since the risk is for many of them to end up in worst off position. It also explains how fragile full integration is, even though it’s the objectively best outcome for all.

On a broader philosophical level, it’s useful to think about the consequences of segregated society.

Unless a society has been 100% integrated at some point, there would be no reference point for either population to appeal to or even know that there is a better outcome outside of segregation. All of the evidence from experimenting with deviating from the NE would reinforce the notion that segregation is the better option, since, unless the entire society deviates, the deviants would incur a negative payoff. Even if a prophetic voice managed to be heard and articulated the collective gains for a fully integrated society, the unprecedented level of coordination required to achieve integration would make any rational agent skeptical that integration can be achieved: it would be seen as unrealistic, since anything less than 100% integration makes the scheme collapse, and be dismissed as utopian thinking.

In essence, in a world where segregation has always been the status squo, a prophetic voice encouraging us to integrate would be seen a threat to the stable configuration the society has grown accustomed to, and even the supporters would not stand by the prophet as they dismiss the vision as impractical and unachievable.

Nash Equilibria in Biology

Game theory is not just an abstract mathematical model, but rather a very real and observable phenomenon in nature. Game theoretical matrices show up in two forms in nature: in ecological behavior and gene distributions.

Therefore, game theory models not just biological behavior but also genetic distributions, and being biological creatures ourselves those insights can prove useful to analyze society.

Evolutionary Game Theory and Memes

In “The Selfish-Gene”, famous biologist and militant atheist Richard Dawkins coined the term “meme” to describe a piece of self-replicating ideological information. He and Susan Blackmore argue that in the same way a “gene”, as a self-replicating piece of biological information, seeks to replicate itself at all times, so does a “meme”, and hence the field of the memetics was born.

If ideas and behaviors are carried through memes, and memes behave like genes, this means that evolutionary game theory’s analysis of gene dynamics can also be applied to the evolution of memes, and by extension culture as a whole because culture is a collection of all of the dominant memes in a population.

Let’s unpack that for a second: if memes are to culture what genes are to ecology, and evolutionary game theory explains the distributions of genes in nature, then evolutionary game theory can also explain the distribution of memes in a particular society.

Therefore culture has NEs, both pure like those of the segregation game, and mixed like those of rock-paper-scissors. These NEs are analogous to our human ideologies: collections of behaviors and ideas unified by a shared framework.

Deviance, Rebellion and Survival of the Fittest

At this point, John Nash’s position on insanity should make more sense. He is pointing out the social utility of having individuals challenge the norms we have converged to over time, even when it’s against their self-interest to do so. Strikes fail most of the time because not enough people band together as they fear repercussion from employers, even though the equilibrium brought about by a successful strike in the interest of all workers.

John Nash formulated one of the earliest versions of evolutionary psychology, and thus argued that nature would always keep producing gene configurations that would deviate from the NE at any given time because even though those deviations would be wiped out most of the time, eventually they would be able to shift the NE away from the sub-optimal one and towards an increasingly more optimal one, something that would have never happened automatically if genetically the population was homogeneous.

The game theoretical view of evolutionary psychology thus forces to entirely re-evaluate our view on mental illness, of what it means to be crazy and of our natural tendency to identify diversity as deviance and as threatening.

Confirmation from Computer Science

In artificial intelligence research, evolutionary strategies seek to mimic biological evolution by recreating the mechanics of mutation and selection in order to arrive to a solution to a given math problem. Early on, researchers observed that even though the AIs would converge towards a pretty good solution, it would often reveal itself to be suboptimal to other possible solutions, and yet the AIs would be stuck.

Essentially, in evolution strategies, a population of AI agents is created and they each try to solve a math a problem in their own, random way. After each “round’, only the top AIs that solved the math problem quickly get to move on, and they are recombined to create new AIs that use the insight from their parent AIs to solve the problem potentially even more quickly. This evolutionary process is repeated until only AIs with the same solution remain, and then the researchers pick up that solution to solve a math problem.

Conclusion

What I just described is existence: we humans, complicated artificial intelligences, try in our own way to solve “life”, the biggest math problem of all, better than everyone else. We alternate between copying each other and innovating on our own until we converge towards a culture, a set of “best practices on how to live life”, that we can all follow. Sure, our culture often turns out to be pretty good, especially when compared to all the random alternatives that would lead us into a worst place. But homogeneity is never healthy. Just like an immune system, our culture needs to confront new obstacles and adversaries to get stronger over time, otherwise our society becomes fragile, and vulnerable to a random pathogen emerging unexpectedly.

With gene editing on the rise, our technology will eventually enable us to implement a eugenic utopia where everyone is a strong and healthy genius. If we all have the same genetic code however, we become collectively vulnerable to the same pathogens on an existential level.

Even though we feel the impulse to avoid getting sick, when taken to an extreme it actually ends up making our immune system weaker, as it never evolved to adapt to viruses of any kind. When extended to our entire species, the same genetic code persuades us initially that we have reached a utopia, until we all go extinct when a new pathogen no one is ready for arrives.

That’s the fundamental argument John Nash was making: insanity is what emerges naturally in any society. It’s the phenomenon that challenges everyone to consider things from a different point of view. It’s the “noise” that enables the AIs to escape being stuck in a local optima and instead reach for a global optima. It’s the mechanism that enables visionaries to take us to the other NE in the segregation game because they visualize a world that appears crazy to all but them. It’s the key to our long term survival and our long term progress: it’s the force that propels us away from sub-optimal Nash equilibria.

Every time we marginalize the mentally ill. Every time we label dissenting opinions as “dangerously insane”. Every time we dream of a world where we can entirely eliminate down syndrome, “treat” autism, and take painkillers to never feel anxiety or depression ever again. Every time we do anything but constructively engage who do not share our paradigm of mental health, we end up, just like John Nash argued, suppressing the very source of our human genius.