Cooperation and the evolution of intelligence

June 21, 2013 by Keven Poulin

One of the puzzles of evolutionary anthropology is to understand how our brains got to grow so big. At first sight, the question seems like a no brainer (pause for eye-roll): big brains make us smarter, more adaptable and thus result in an obvious increase in fitness, right? The problem is that brains need calories, and lots of them. Though it accounts for only 2% of your total weight, your brain will consume about 20-25% of your energy intake. Furthermore, the brain from behind its barrier doesn’t have access to the same energy resources as the rest of your body, which is part of the reason why you can’t safely starve yourself thin (if it ever crossed your mind).

So maintaining a big brain requires time and resources. For us, the trade-off is obvious, but if you’re interested in human evolutionary history, you must keep in mind that our ancestors did not have access to chain food stores or high fructose corn syrup, nor were they concerned with getting a college degree. They were dealing with a different set of trade-offs and this is what evolutionary anthropologists are after. What is it that our ancestors’ brains allowed them to do so well that warranted such unequal energy allocation?



EvoAnth’s Why Our Brains Are Big provides a good overview of the current hypotheses on the subject; the main one is the Social Brain Hypothesis (Dunbar, 1998) which states that as our distant ancestors became more social and as their social groups grew larger, there was increasing need for better memory, more effective decision-making and more comprehensive modes of representation (e.g. Theory of the Mind). These needs in turn exerted the evolutionary pressure for bigger, more complex brains. Support for this hypothesis comes mostly from correlational studies and no mechanistic description has so far been proposed.

In their paper, McNally, Brown & Jackson (2012) looked into whether a computational simulation of the evolutionary dynamics between agents of varying cognitive abilities could provide such a description. In their simulation, each agent is a neural network which consists of input and output nodes, weighted edges, and a hidden layer of cognitive and context nodes. The number of “neurons” in the hidden layer is used as a measure of intelligence. From one generation to the next, an offspring inherits its parent’s network structure as well as the weights. Those traits are however subject to a constant rate of mutation. In particular, the hidden layer can gain or lose nodes in its hidden layer (higher or lower intelligence).

During each generation, each agent will interact with every other agent in the population in instances of the Prisoner’s Dilemma game, or the Snowdrift game. The paper claims that they are iterated games, but given the description, this is clearly inaccurate. After each interaction, the agent’s network takes as inputs its own and its partner’s payoffs and computes the probability of cooperating during the next interaction. Agents never play against the same agent twice.

Computation in the neural network is accomplished when each node takes the weighted sum of its inputs. The result is passed in a sigmoid squashing function that will make determine whether the node will fire. The sigmoid function will push the final probability towards the extremes of 0 or 1, but there will be some noise. From this point of view, it seems randomness will be difficult to achieve, a fact that contrast some ideas about agency explored before on TheEGG blog.

Context nodes also play an interesting role: during a neural computation the context node first passes its value to its associated cognitive node which is integrated along with the other inputs, and then it is updated with the current value of the cognitive node. This mechanism is said to be similar to “emotional states” whereby “memory of past interactions” is accumulated without keeping a detailed sequence of events.

When all interactions have been played, agents produce an offspring with a probability proportional to their mean payoff minus a cost for intelligence. Taxing higher cognitive ability is important: without it, we would be studying a trade-off anymore. Artem used a similar implementation for cost on cognition in Kaznatcheev (2010). Finally, before the next cycle commences, parent networks die.

When I remarked that it was inaccurate to call these games ‘iterated’, I was not merely being picky about terminology: I think that the way the authors use these neural networks undermines the claims they hope to make about the evolution of intelligence. An intuitive understanding of the social intelligence hypothesis is that our greater cognitive abilities made it more likely for us to predict whether our partner will cooperate or defect and thus adjust our strategy accordingly in order to maximize our payoff. In this model however, it is clear that the decision to cooperate or defect has already been made when an agent meets its partner.

That being said, it is true that context nodes store information about all past interactions, hence effectively providing an educated guess about the whole population, and by the same token, the next player. Presented like that, the model already sounds more convincing and in accordance with this scheme, the proxy for intelligence should be more tightly linked with the number of context nodes only. Talking about these games as iterated, and comparing the emergent networks to well-known IPD strategies such as tit-for-tat or win-stay, lose-shift is misleading at best.

What this paper wanted to provide is evidence for an evolutionary history resembling a Machiavellian arms race where selection for efficient decision-making in cooperative games exerts a pressure for greater cognitive abilities (more complex strategies) which in turn select for greater intelligence. The strongest case for this story is given by the positive correlations between the frequency of contingent strategies and the selection for intelligence (measured here as the covariance between fitness and intelligence). However, this correlation is closely dependent on the level of cooperation which goes through a cycle: an important surge in cooperation levels brought about by high frequencies of contingent strategies, followed by invasion of simple, always-cooperate-like strategies, and a subsequent invasion by always-defect-like strategies. This oscillation pattern between cooperation and defection is well-known in the IPD literature, but it is interesting to see its emergence here from an initially random population of networks in a non-IPD setting.

I think the logical extension of this model would be to make agents more discerning, that is to allow the neural network to use information about its current partner (e.g. total payoff, last payoff, or average payoff) in order to make an informed decision, and after the interaction, allow it to update its values according to its payoff. While such conditional strategies have been studied (e.g. Szolnoki, Xie, Wang, Perc (2011); Szolnoki, Xie, Ye, Perc (2013)), applying the idea to neural networks would, to my knowledge, be novel.

In another possible extension, as the authors remark, agents could play not just one but many possible games. Indeed, perhaps what makes social interactions so complex is not only that different players may have different strategies, but also that we are constantly playing different games, sometimes simultaneously thus giving rise to increased needs for conflict-management abilities. Artem mentioned this idea as meta-game in an earlier post.

I’ll end this post on a few thoughts about deception, which is my primary line of research these days. Supposing the Social Brain Hypothesis is true, we can move on to the next question: Given that human’s social interactions are extremely varied and complex, which ones proved instrumental in this intelligence growth process? One idea is that higher intelligence allows more refined tactical deception and that in turn, even higher intelligence allows more efficient detection, therefore leading to an arms race (see this post for a model exploring this idea). While the idea is intuitive, there are inherent problems that come with studying deception because the concept itself is often ill-defined or loaded with assumptions. For example, McNally et al. (2012) claims their agents are doing something akin to deception, but for reasons I’ve mentioned here, it can’t be called that (whatever it is they’re doing). Nevertheless, I think the use of neural networks will prove useful in studying deception as it could give us a way to observe internal representations (albeit in a very simplified manner). I hope to write more on this topic, so stay tuned!

Reference

Dunbar, R. I. M. (1998) The social brain hypothesis. Evol. Anthropol. 6, 178–190. (doi:10.1002/(SICI)1520-6505(1998)6:5,178::AID-EVAN5.3.0.CO;2-8)

Kaznatcheev, Artem (2010). The cognitive cost of ethnocentrism. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society

McNally L., Brown S.P. & Jackson A.L. (2012). Cooperation and the evolution of intelligence, Proceedings of the Royal Society B: Biological Sciences, 279 (1740) 3027-3034. DOI: 10.1098/rspb.2012.0206

Szolnoki, A., Xie, N.-G., Wang, C., and Perc, M. (2011) Imitating emotions instead of strategies in spatial games elevates social welfare. EPL, 96, 38002.

Szolnoki, A., Xie, N.-G., Ye, Y., and Perc, M. (2013) Evolution of emotions on networks leads to the evolution of cooperation in social dilemmas. EPL, 96, 38002.