Tag: Complex Systems

Competition, Cooperation, and the Selfish Gene

Richard Dawkins has one of the best-selling books of all time for a serious piece of scientific writing.

Often labeled “pop science”, The Selfish Gene pulls together the “gene-centered” view of evolution: It is not really individuals being selected for in the competition for life, but their genes. The individual bodies (phenotypes) are simply carrying out the instructions of the genes. This leads most people to a very “competition focused” view of life. But is that all?

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More than 100 years before The Selfish Gene, Charles Darwin had famously outlined his Theory of Natural Selection in The Origin of Species.

We’re all hopefully familiar with this concept: Species evolve over long periods time through a process of heredity, variation, competition, and differential survival.

The mechanism of heredity was invisible to Darwin, but a series of scientists, not without a little argument, had figured it out by the 1970’s: Strands of the protein DNA (“genes”) encoded instructions for the building of physical structures. These genes were passed on to offspring in a particular way – the process of heredity. Advantageous genes were propagated in greater numbers. Disadvantageous genes, vice versa.

The Selfish Gene makes a particular kind of case: Specific gene variants grow in proportion to a gene pool by, on average, creating advantaged physical bodies and brains. The genes do their work through “phenotypes” – the physical representation of their information. As Helena Cronin would put in her book The Ant and the Peacock, “It is the net selective value of a gene's phenotypic effect that determines the fate of the gene.”

This take of the evolutionary process became influential because of the range of hard-to-explain behavior that it illuminated.

Why do we see altruistic behavior? Because copies of genes are present throughout a population, not just in single individuals, and altruism can cause great advantages in those gene variants surviving and thriving. (In other words, genes that cause individuals to sacrifice themselves for other copies of those same genes will tend to thrive.)

Why do we see more altruistic behavior among family members? Because they are closely related, and share more genes!

Many problems seemed to be solved here, and the Selfish Gene model became one for all-time, worth having in your head.

However, buried in the logic of the gene-centered view of evolution is a statistical argument. Gene variants rapidly grow in proportion to the rest of the gene pool because they provide survival advantages in the average environment that the gene will experience over its existence. Thus, advantageous genes “selfishly” dominate their environment before long. It's all about gene competition.

This has led many people, some biologists especially, to view evolution solely through the lens of competition. Unsurprisingly, this also led to some false paradigms about a strictly “dog eat dog” world where unrestricted and ruthless individual competition is deemed “natural”.

But what about cooperation?

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The complex systems researcher Yaneer Bar-Yam argues that not only is the Selfish Gene a limiting concept biologically and possibly wrong mathematically (too complex to address here, but if you want to read about it, check out these pieces), but that there are more nuanced ways to understand the way competition and cooperation comfortably coexist. Not only that, but Bar-Yam argues that this has implications for optimal team formation.

In his book Making Things Work, Bar-Yam lays out a basic message: Even in the biological world, competition is a limited lens through which to see evolution. There’s always a counterbalance of cooperation.

Counter to the traditional perspective, the basic message of this and the following chapter is that competition and cooperation always coexist. People see them as opposing and incompatible forces. I think that this is a result of an outdated and one-sided understanding of evolution…This is extremely useful in describing nature and society; the basic insight that “what works, works” still holds. It turns out, however, that what works is a combination of competition and cooperation.

Bar-Yam uses the analogy of a sports team which exists in context of a sports league – let’s say the NBA. Through this lens we can see why players, teams, and leagues compete and cooperate. (The obvious analogy is that genes, individuals, and groups compete and cooperate in the biological world.)

In general, when we think about the conflict between cooperation and completion in team sports, we tend to think about the relationships between the players on a team. We care deeply about their willingness to cooperate and we distinguish cooperative “team players” from selfish non-team players, complaining about the latter even when their individual skill is formidable.

The reason we want players to cooperate is so that they can compete better as a team. Cooperation at the level of the individual enables effective competition at the level of the group, and conversely, the competition between teams motivates cooperation between players. There is a constructive relationship between cooperation and competition when they operate at different levels of organization.

The interplay between levels is a kind of evolutionary process where competition at the team level improves the cooperation between players. Just as in biological evolution, in organized team sports there is a process of selection of winners through competition of teams. Over time, the teams will change how they behave; the less successful teams will emulate strategies of teams that are doing well.

At every level then, there is an interplay between cooperation and competition. Players compete for playing time, and yet must be intensively cooperative on the court to compete with other teams. At the next level up, teams compete with each other for victories, and yet must cooperate intensively to sustain a league at all.

They create agreed upon rules, schedule times to play, negotiate television contracts, and so on. This allows the league itself to compete with other leagues for scarce attention from sports fans. And so on, up and down the ladder.

Competition among players, teams, and leagues is certainly a crucial dynamic. But it isn’t all that’s going on: They’re cooperating intensely at every level, because a group of selfish individuals loses to a group of cooperative ones.

And it is the same among biological species. Genes are competing with each other, as are individuals, tribes, and species. Yet at every level, they are also cooperating. The success of the human species is clearly due to its ability to cooperate in large numbers; and yet any student of war can attest to its deadly competitive nature. Similar dynamics are at play with ants, rats, and chimpanzees, among other species of insect and animal. It’s a yin and yang world.

Bar-Yam thinks this has great implications for how to build successful teams.

Teams will improve naturally – in any organization – when they are involved in a competition that is structured to select those teams that are better at cooperation. Winners of a competition become successful models of behavior for less successful teams, who emulate their success by learning their strategies and by selecting and trading team members.

For a business, a society, or any other complex system made up of many individuals, this means that improvement will come when the system’s structure involves a completion that rewards successful groups. The idea here is not a cutthroat competition of teams (or individuals) but a competition with rules that incorporate some cooperative activity with a mutual goal.

The dictum that “politics is the art of marshaling hatreds” would seem to reflect this notion: A non-violent way for competition of cooperative groups for dominance. As would the incentive systems of majorly successful corporations like Nucor and the best hospital systems, like the Mayo Clinic. Even modern business books are picking up on it.

Individual competition is important and drives excellence. Yet, as Bar-Yam points out, it’s ultimately not a complete formula. Having teams compete is more effective: You need to harness competition and cooperation at every level. You want groups pulling together, creating emerging effects where the whole is greater than the sum of the parts (a recurrent theme throughout nature).

You should read his book for more details on both this idea and the concept of complex systems in general. Bar-Yam also elaborated on his sports analogy in a white-paper here. If you're interested in complex systems, check out this post on frozen accidents. Also, for more on creating better groups, check out how Steve Jobs did it.

A Cascade of Sand: Complex Systems in a Complex Time

We live in a world filled with rapid change: governments topple, people rise and fall, and technology has created a connectedness the world has never experienced before. Joshua Cooper Ramo believes this environment has created an “‘avalanche of ceaseless change.”

In his book, The Age of the Unthinkable: Why the New World Disorder Constantly Surprises Us And What We Can Do About It he outlines what this new world looks like and gives us prescriptions on how best to deal with the disorder around us.

Ramo believes that we are entering a revolutionary age that will render seemingly fortified institutions weak, and weak movements strong. He feels we aren’t well prepared for these radical shifts as those in positions of power tend to have antiquated ideologies in dealing with issues. Generally, they treat anything complex as one dimensional.

Unfortunately, whether they are running corporations or foreign ministries or central banks, some of the best minds of our era are still in thrall to an older way of seeing and thinking. They are making repeated misjudgments about the world. In a way, it’s hard to blame them. Mostly they grew up at a time when the global order could largely be understood in simpler terms, when only nations really mattered, when you could think there was a predictable relationship between what you wanted and what you got. They came of age as part of a tradition that believed all international crises had beginnings and, if managed well, ends.

This is one of the main flaws of traditional thinking about managing conflict/change: we identify a problem, decide on a path forward, and implement that solution. We think in linear terms and see a finish line once the specific problem we have discovered is ‘solved.’

In this day and age (and probably in all days and ages, whether they realized it or not) we have to accept that the finish line is constantly moving and that, in fact, there never will be a finish line. Solving one problem may fix an issue for a time but it tends to also illuminate a litany of new problems. (Many of which were likely already present but hiding under the old problem you just “fixed”.)

In fact, our actions in trying to solve X will sometimes have a cascade effect because the world is actually a series of complex and interconnected systems.

Some great thinkers have spoken about these problems in the past. Ramo highlights some interesting quotes from the Nobel Prize speech that Austrian economist Friedrich August von Hayek gave in 1974, entitled The Pretence of Knowledge.

To treat complex phenomena as if they were simple, to pretend that you could hold the unknowable in the cleverly crafted structure of your ideas —he could think of nothing that was more dangerous. “There is much reason,” Hayek said, “to be apprehensive about the long-run dangers created in a much wider field by the uncritical acceptance of assertions which have the appearance of being scientific.”

Concluding his Nobel speech, Hayek warned, “If man is not to do more harm than good in his efforts to improve the social order, he will have to learn that in this, as in all other fields where essential complexity of an organized kind prevails, he cannot acquire the full knowledge which would make mastery of the events possible.” Politicians and thinkers would be wise not to try to bend history as “the craftsman shapes his handiwork, but rather to cultivate growth by providing the appropriate environment, in the manner a gardener does for his plants.”

This is an important distinction: the idea that we need to be gardeners instead of craftsmen. When we are merely creating something we have a sense of control; we have a plan and an end state. When the shelf is built, it's built.

Being a gardener is different. You have to prepare the environment; you have to nurture the plants and know when to leave them alone. You have to make sure the environment is hospitable to everything you want to grow (different plants have different needs), and after the harvest you aren’t done. You need to turn the earth and, in essence, start again. There is no end state if you want something to grow.

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So, if most of the threats we face to today are so multifaceted and complex that we can’t use the majority of the strategies that have worked historically, how do we approach the problem? A Danish theoretical physicist named Per Bak had an interesting view of this which he termed self-organized criticality and it comes with an excellent experiment/metaphor that helps to explain the concept.

Bak’s research focused on answering the following question: if you created a cone of sand grain by grain, at what point would you create a little sand avalanche? This breakdown of the cone was inevitable but he wanted to know if he could somehow predict at what point this would happen.

Much like there is a precise temperature that water starts to boil, Bak hypothesized there was a specific point where the stack became unstable, and at this point adding a single grain of sand could trigger the avalanche.

In his work, Bak came to realize that the sandpile was inherently unpredictable. He discovered that there were times, even when the pile had reached a critical state, that an additional grain of sand would have no effect:

“Complex behavior in nature,” Bak explained, “reflects the tendency of large systems to evolve into a poised ‘critical’ state, way out of balance, where minor disturbances may lead to events, called avalanches, of all sizes.” What Bak was trying to study wasn’t simply stacks of sand, but rather the underlying physics of the world. And this was where the sandpile got interesting. He believed that sandpile energy, the energy of systems constantly poised on the edge of unpredictable change, was one of the fundamental forces of nature. He saw it everywhere, from physics (in the way tiny particles amassed and released energy) to the weather (in the assembly of clouds and the hard-to-predict onset of rainstorms) to biology (in the stutter-step evolution of mammals). Bak’s sandpile universe was violent —and history-making. It wasn’t that he didn’t see stability in the world, but that he saw stability as a passing phase, as a pause in a system of incredible —and unmappable —dynamism. Bak’s world was like a constantly spinning revolver in a game of Russian roulette, one random trigger-pull away from explosion.

Traditionally our thinking is very linear and if we start thinking of systems as more like sandpiles, we start to shift into nonlinear thinking. This means we can no longer assume that a given action will produce a given reaction: it may or may not depending on the precise initial conditions.

This dynamic sandpile energy demands that we accept the basic unpredictability of the global order —one of those intellectual leaps that sounds simple but that immediately junks a great deal of traditional thinking. It also produces (or should produce) a profound psychological shift in what we can and can’t expect from the world. Constant surprise and new ideas? Yes. Stable political order, less complexity, the survival of institutions built for an older world? No.

Ramo isn’t arguing that complex systems are incomprehensible and fundamentally flawed. These systems are manageable, they just require a divergence from the old ways of thinking, the linear way that didn’t account for all the invisible connections in the sand.

Look at something like the Internet; it’s a perfect example of a complex system with a seemingly infinite amount of connections, but it thrives. This system is constantly bombarded with unsuspected risk, but it is so malleable that it has yet to feel the force of an avalanche. The Internet was designed to thrive in a hostile environment and its complexity was embraced. Unfortunately, for every adaptive system like the Internet there seems to be a maladaptive system, ones so rigid they will surely break in a world of complexity.

The Age of the Unthinkable goes on to show us historical examples of systems that did indeed break; this helps to frame where we have been particularly fragile in the past and where the mistakes in our thinking may have been. In the back half of the book, Ramo outlines strategies he believes will help us become more Antifragile, he calls this “Deep Security”.

Implementing these strategies will likely be met with considerable resistance, many people in positions of power benefit from the systems staying as they are. Revolutions are never easy but, as we’ve shown, even one grain of sand can have a huge impact.

Using Multidisciplinary Thinking to Approach Problems in a Complex World

Complex outcomes in human systems are a tough nut to crack when it comes to deciding what's really true. Any phenomena we might try to explain will have a host of competing theories, many of them seemingly plausible.

So how do we know what to go with?

One idea is to take a nod from the best. One of the most successful “explainers” of human behavior has been the cognitive psychologist Steven Pinker. His books have been massively influential, in part because they combine scientific rigor, explanatory power, and plainly excellent writing.

What's unique about Pinker is the range of sources he draws on. His book The Better Angels of Our Nature, a cogitation on the decline in relative violence in recent human history, draws on ideas from evolutionary psychology, forensic anthropology, statistics, social history, criminology, and a host of other fields. Pinker, like Vaclav Smil and Jared Diamond, is the opposite of the man with a hammer, ranging over much material to come to his conclusions.

In fact, when asked about the progress of social science as an explanatory arena over time, Pinker credited this cross-disciplinary focus:

Because of the unification with the sciences, there are more genuinely explanatory theories, and there’s a sense of progress, with more non-obvious things being discovered that have profound implications.

But, even better, Pinker gives out an outline for how a multidisciplinary thinker should approach problems in a complex world.

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Here's the issue at stake: When we're viewing a complex phenomena—say, the decline in certain forms of violence in human history—it can be hard to come with up a rigorous explanation. We can't just set up repeated lab experiments and vary the conditions of human history to see what pops out, as with physics or chemistry.

So out of necessity, we must approach the problem in a different way.

In the above referenced interview, Pinker gives a wonderful example how to do it: Note how he carefully “cross-checks” from a variety of sources of data, developing a 3D view of the landscape he's trying to assess:

Pinker: Absolutely, I think most philosophers of science would say that all scientific generalizations are probabilistic rather than logically certain, more so for the social sciences because the systems you are studying are more complex than, say, molecules, and because there are fewer opportunities to intervene experimentally and to control every variable. But the exis­tence of the social sciences, including psychology, to the extent that they have discovered anything, shows that, despite the uncontrollability of human behavior, you can make some progress: you can do your best to control the nuisance variables that are not literally in your control; you can have analogues in a laboratory that simulate what you’re interested in and impose an experimental manipulation.

You can be clever about squeezing the last drop of causal information out of a correlational data set, and you can use converging evi­dence, the qualitative narratives of traditional history in combination with quantitative data sets and regression analyses that try to find patterns in them. But I also go to traditional historical narratives, partly as a sanity check. If you’re just manipulating numbers, you never know whether you’ve wan­dered into some preposterous conclusion by taking numbers too seriously that couldn’t possibly reflect reality. Also, it’s the narrative history that provides hypotheses that can then be tested. Very often a historian comes up with some plausible causal story, and that gives the social scientists something to do in squeezing a story out of the numbers.

Warburton: I wonder if you’ve got an example of just that, where you’ve combined the history and the social science?

Pinker: One example is the hypothesis that the Humanitarian Revolution during the Enlightenment, that is, the abolition of slavery, torture, cruel punishments, religious persecution, and so on, was a product of an expansion of empathy, which in turn was fueled by literacy and the consumption of novels and journalis­tic accounts. People read what life was like in other times and places, and then applied their sense of empathy more broadly, which gave them second thoughts about whether it’s a good idea to disembowel someone as a form of criminal punish­ment. So that’s a historical hypothesis. Lynn Hunt, a historian at the University of California–Berkeley, proposed it, and there are some psychological studies that show that, indeed, if people read a first-person account by someone unlike them, they will become more sympathetic to that individual, and also to the category of people that that individual represents.

So now we have a bit of experimental psychology supporting the historical qualita­tive narrative. And, in addition, one can go to economic histo­rians and see that, indeed, there was first a massive increase in the economic efficiency of manufacturing a book, then there was a massive increase in the number of books pub­lished, and finally there was a massive increase in the rate of literacy. So you’ve got a story that has at least three vertices: the historian’s hypothesis; the economic historians identifying exogenous variables that changed prior to the phenomenon we’re trying to explain, so the putative cause occurs before the putative effect; and then you have the experimental manipulation in a laboratory, showing that the intervening link is indeed plausible.

Pinker is saying, Look we can't just rely on “plausible narratives” generated by folks like the historians. There are too many possibilities that could be correct.

Nor can we rely purely on correlations (i.e., the rise in literacy statistically tracking the decline in violence) — they don't necessarily offer us a causative explanation. (Does the rise in literacy cause less violence, or is it vice versa? Or, does a third factor cause both?)

However, if we layer in some other known facts from areas we can experiment on — say, psychology or cognitive neuroscience — we can sometimes establish the causal link we need or, at worst, a better hypothesis of reality.

In this case, it would be the finding from psychology that certain forms of literacy do indeed increase empathy (for logical reasons).

Does this method give us absolute proof? No. However, it does allow us to propose and then test, re-test, alter, and strengthen or ultimately reject a hypothesis. (In other words, rigorous thinking.)

We can't stop here though. We have to take time to examine competing hypotheses — there may be a better fit. The interviewer continues on asking Pinker about this methodology:

Warburton: And so you conclude that the de-centering that occurs through novel-reading and first-person accounts probably did have a causal impact on the willingness of people to be violent to their peers?

Pinker: That’s right. And, of course, one has to rule out alternative hypotheses. One of them could be the growth of affluence: perhaps it’s simply a question of how pleasant your life is. If you live a longer and healthier and more enjoyable life, maybe you place a higher value on life in general, and, by extension, the lives of others. That would be an alternative hypothesis to the idea that there was an expansion of empathy fueled by greater literacy. But that can be ruled out by data from eco­nomic historians that show there was little increase in afflu­ence during the time of the Humanitarian Revolution. The increase in affluence really came later, in the 19th century, with the advent of the Industrial Revolution.

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Let's review the process that Pinker has laid out, one that we might think about emulating as we examine the causes of complex phenomena in human systems:

  1. We observe an interesting phenomenon in need of explanation, one we feel capable of exploring.
  2. We propose and examine competing hypotheses that would explain the phenomena (set up in a falsifiable way, in harmony with the divide between science and pseudoscience laid out for us by the great Karl Popper).
  3. We examine a cross-section of: Empirical data relating to the phenomena; sensible qualitative inference (from multiple fields/disciplines, the more fundamental the better), and finally;  “Demonstrable” aspects of nature we are nearly certain about, arising from controlled experiment or other rigorous sources of knowledge ranging from engineering to biology to cognitive neuroscience.

What we end up with is not necessarily a bulletproof explanation, but probably the best we can do if we think carefully. A good cross-disciplinary examination with quantitative and qualitative sources coming into equal play, and a good dose of judgment, can be far more rigorous than the gut instinct or plausible nonsense type stories that many of us lazily spout.

A Word of Caution

Although Pinker's “multiple vertices” approach to problem solving in complex domains can be powerful, we always have to be on guard for phenomena that we simply cannot explain at our current level of competence: We must have a “too hard” pile when competing explanations come out “too close to call” or we otherwise feel we're outside of our circle of competence. Always tread carefully and be sure to follow Darwin's Golden Rule: Contrary facts are more important than confirming ones. Be ready to change your mind, like Darwin, when the facts don't go your way.

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Still Interested? For some more Pinker goodness check out our prior posts on his work, or check out a few of his books like How the Mind Works or The Blank Slate: The Modern Denial of Human Nature.

The Map is Not the Territory