 Classical game theory was developed during the mid-20th century, primarily for application in economics and political science. But in the 1970s, a number of biologists started to recognize how similar the games being studied were to the interaction between animals within ecosystems. Game theory then quickly became a hot topic in biology, as they started to find it relevant to all sorts of animal and microbial interactions from the feeding of bats to the territorial defense of stickleback fish. Originally, evolutionary game theory was simply the application of game theory to evolving populations in biology, asking how cooperative systems could have evolved over time from various strategies that biological creatures might have adopted. However, the development of evolutionary game theory has produced a theory which holds great promise as a general theory of games. More recently, evolutionary game theory has become of increased interest to economists, sociologists, anthropologists, and social scientists in general. In this video, we'll talk about this more general application of evolutionary game theory. Whereas the game theory that we've been talking about so far has been focused on static strategies, that is to say, strategies that do not change over time. Evolutionary game theory differs from classical game theory in focusing more on the dynamics of strategy change. Here, we're asking how strategies evolve over time and which kind of dynamic strategies are most successful in this evolutionary process. One of the interesting differences between evolutionary game theory and standard game theory is that the evolutionary version does not require players to act rationally. When we talk about biological cells or ants, we know that they do not sit in front of payoff matrix and ask themselves what is the best payoff. In evolutionary game theory, natural selection does this for us. So if we have a group of cooperators and defectors who randomly meet each other, the average payoff for the defectors is higher than the cooperators, therefore they will reproduce better. Payoffs in evolutionary biology correspond to reproductive success. So after some time, evolution would have favored defectors to the point where all of the cooperators will be extinct. And from this, evolution via selection has imposed some concept of rationality on the game. The basic logic is that for something to survive the course of time, it must be an optimal strategy or as any other strategy that is more efficient will eventually come to dominate the population. Traditionally, the story of evolution is told as one of competition and there's certainly plenty of this. But there is also mutualism where organisms and people manage to work together cooperatively and survive in the face of defectors. Many research papers have been written on the topic of how cooperation could evolve in the face of such evolutionary dynamics. The general question of interest in evolutionary game theory is in how do patterns of cooperation evolve and what are optimal strategies to use in a game that evolves over time. The basic mechanism that underlies the evolution of cooperation is the interdependency between actors over time. In a single shot game, it makes sense to always defect but with repetition, cooperation becomes greatly more viable. If the game is repeated, it is no longer the case that strict defection is the best option. If the prisoner's dilemma situation is repeated, it allows for non-cooperation to be more easily punished and cooperation to be rewarded more than what the single shot version of the problem would allow. We can understand this better by looking at a number of experiments that were done to investigate this dynamic. The political scientist Robert Axelrod in the late 70s performed a number of highly influential computer experiments asking what is a good strategy for playing a repeated prisoner's dilemma game. Axelrod asks for various researchers to submit computer algorithms to a competition to see which of these algorithms would fare best against each other. Computer models of the evolution of cooperation showed that indiscriminate cooperators almost always end up losing against defectors who accept helpful acts from others but do not reciprocate them. People who are cooperative and helpful indiscriminately all of the time will end up getting taken advantage of by others. However, if we have a population of pure defectors, they will also lose out on the possible rewards of cooperation that will give them all higher payoffs. Many strategies have been tested. The best competitive strategies are generally cooperative with the reserved retaliatory response if necessary. The most famous and one of the most successful of these is Tit for Tat. Tit for Tat is a very simple algorithm of just three rules. I start with cooperation. If you cooperate, then I'll cooperate. If you defect, then I'll defect. Computer tournaments in which different strategies were pitted against each other showed Tit for Tat to be the most successful strategy in social dilemmas. Tit for Tat is a common strategy in real world social dilemmas because it is nice but firm. It makes cooperation a possibility but is also quick to reprimand the defectors. It is a strategy that can be found naturally in everything from international trade politics to people borrowing and lending money and in repeated iterations cooperation can emerge when people adopt a Tit for Tat strategy. To go beyond Tit for Tat, researchers started to use computers to simulate the process of evolution. Instead of people submitting solutions, the computer itself generated mutations and selected from them with the researchers then recording and analyzing the results. From these experiments they found that if the players play randomly, the winners are those that always defect. But then when everyone has come to play defect strategies, if a few people play Tit for Tat strategies, a small cluster can form but where among themselves they can get a good payoff. Evolutionary selection can then start to favor them and they do not get exploited by all the defectors because they immediately switched defect in retaliation. But the Tit for Tat strategy did not last long in this setting as a new solution came to emerge given this new context. This strategy was a mutant of Tit for Tat that was more forgiving called Generous Tit for Tat. Generous Tit for Tat is an algorithm that starts with cooperation and then will reciprocate cooperation from others but if the other defects it will defect with some probability. Thus it uses probability to enable the quality of what we might call forgiveness. It cooperates when others do but when they defect there is still a probability that it will continue to cooperate. This is a random decision by the algorithm so it is not possible for others to predict when it will continue to cooperate which is an important part of its strategy. It turns out that this forgiveness strategy is optimal in environments when there is some degree of noise and communications as is characteristic of real world environments. In the real world we often do not know for certain if our partner cheated or if someone really meant to say what they said and these errors have to be compensated for by some degree of forgiveness. In a world of errors in action and perception such a strategy can be a Nash equilibrium and evolutionary stable. The more beneficial that cooperation is the more forgiving Generous Tit for Tat can be while still resisting invasion by defectors. The extraordinary thing that now happens is that once everyone has moved towards playing Generous Tit for Tat cooperation becomes a much stronger tractor and at this stage players can now play an unconditional cooperative strategy without having any disadvantages. In a world of Generous Tit for Tat there is no longer a need for any other actions and thus unconditional cooperators survive. In order for a strategy to be evolutionary stable it must have the property that if almost every member of the population follows it no mutants can successfully invade where a mutant is an individual who adopts a novel strategy different to the mass of the population. In many situations cooperation is favoured and it even benefits an individual to forgive an occasional defection but cooperative societies are always unstable because mutants inclined to defect can upset any balance and this is the downfall of the cooperative strategy. What happens next is somewhat predictable. In a world where everyone is cooperating unconditional defection becomes an optimal strategy once it takes hold. Thus we can see in these games a dynamic cyclical process as higher forms of cooperation arise and then collapse. In many ways then this reflects what we see in the real world of economies and empires rising and falling as institutional structures for cooperation are formed, mature and eventually decline. These experiments describe the evolution of systems of cooperation through direct interaction and much of our interactions are repeated with people we have interacted with before and built up an understanding of their capacity for reciprocity. However in large societies we have to interact with many people that we have not interacted with before and this may only be a once-off interaction. Experiments have shown that people help those who have helped others and have shown reciprocity in the past and this form of indirect reciprocity has a high payoff in the end. Reputation systems are what allow for the evolution of cooperation by indirect reciprocity. Evolutionary selection favours strategies that base the decision to help on the reputation of the recipient. The idea is that you interact with others and that interaction is seen and people note whether you acted cooperatively or non-corruptively. That information is then circulated so that others learn about your behaviour. Direct reciprocity is where I help you and you help me. Indirect reciprocity is where I help you and then somebody helps me because now I have a reputation for cooperating. The result is the formation of reputation. When you cooperate that helps your reputation when you defect it reduces it. That reputation then follows us around and is used as the basis for our interaction with others in their assessment of whether they want to cooperate with us in turn. Thus reputation forms a system of the evolution of cooperation in larger societies where people may interact frequently with people that they may not know personally. But because of various reputation systems they are able to identify those who are cooperative and enter into mutually beneficial reciprocal relationships. The more sophisticated and secure these reputation systems the greater the capacity for cooperative organisations. We can create large systems wherein we know who to cooperate with and thus can be cooperative ourselves potentially creating a successful community. But of course as the society gets bigger we have to form more complex institutions for enabling functional reputation systems. In such a way we have gone from small communities where local gossip was suffice to know everyone's capacity for cooperation to large modern societies where centralised organisations vouched for people's reputation to today's burgeoning global reputation system based on information technology and mediated through the internet. Research shows that cooperators create better opportunities for themselves than non-corporators. They are selectively preferred as collaborative partners, romantic partners and group leaders. This only occurs however when people's social dilemma choices are seen and recorded by others in some way. However this kind of indirect reciprocity is cognitively complex. No other creature has mastered it to even a fraction of what humans have. Games of indirect reciprocity lead to the evolution of social intelligence and ever more sophisticated means of communications, social and cultural institutions that are characteristic of human civilisation. The basic problem of the evolution of cooperation is that the nice guys get taken advantage of and thus there must be some form of supporting structure to enable cooperation. More than any other primate species, humans have overcome this problem through a variety of mechanisms such as reciprocating cooperative acts, forming reputations of others and the self and caring about those reputations. We create pro-social norms about good behaviour that everyone in the group will enforce on others through disapproval if not punishment and will enforce on themselves through feelings of guilt and shame, all of which form the fabric of our socio-cultural institutions that enable advanced forms of cooperation.