Rory Smead

The evolution of fairness through spite

The presence of apparently irrational fair play in the ultimatum game remains a focal point for studies in the evolution of social behaviour. We investigate the role of negative assortment in the evolution of fair play in the ultimatum game. Spite—social behaviour that inflicts harm with no direct benefit to the actor—can evolve when it is disproportionally directed at individuals playing different strategies. The introduction of negative assortment alters the dynamics in a way that increases the chance fairness evolves, but at a cost: spite also evolves. Fairness is usually linked to cooperation and prosocial behaviour, but this study shows that it may have evolutionary links to harmful antisocial behaviour.

The evolutionary dynamics of spite in finite populations.

Spite, the shady relative of altruism, involves paying a fitness cost to inflict a cost on some recipient. Here, we investigate a density dependent dynamic model for the evolution of spite in populations of changing size. We extend the model by introducing a dynamic carrying capacity. Our analysis shows that it is possible for unconditionally spiteful behavior to evolve without population structure in any finite population. In some circumstances spiteful behavior can contribute to its own stability by limiting population growth. We use the model to show that there are differences between spite and altruism, and to refine Hamilton’s original argument about the insignificance of spite in the wild. We also discuss the importance of fixing the measure of fitness to classify behaviors as selfish or spiteful.

The Role of Social Interaction in the Evolution of Learning

It is generally thought that cognition evolved to help us navigate complex environments. Social interactions make up one part of a complex environment, and some have argued that social settings are crucial to the evolution of cognition. This article uses the methods of evolutionary game theory to investigate the effect of social interaction on the evolution of cognition broadly construed as strategic learning or plasticity. I delineate the conditions under which social interaction alone, apart from any additional external environmental variation, can provide the selective pressure necessary for the initial evolution of learning. Furthermore, it is argued that in the context of social interactions we should notexpect traditional learners that ‘best-respond’ to dominate the population. Consequently, it may be important to consider non-traditional learners when modelling social evolution. 1 Introduction 2 The Model 3 Adapting to the Population   3.1 Learning an equlibrium 4 Adapting to Individuals   4.1 Learning a best response and non-traditional learners 5 Conclusion 1 Introduction 2 The Model 3 Adapting to the Population   3.1 Learning an equlibrium   3.1 Learning an equlibrium 4 Adapting to Individuals   4.1 Learning a best response and non-traditional learners   4.1 Learning a best response and non-traditional learners 5 Conclusion

Game theoretic equilibria and the evolution of learning

Equilibrium concepts in game theory can be justified as the outcomes of some plausible learning rules. Some scholars have sought a deeper kind of justification arguing that learning rules which do not find equilibria of a game will not be evolutionarily successful. This article presents and examines a model of evolving learning rules. The results are mixed for learning rules that lead to equilibria, showing that they are often successful, but not strongly stable. It is also shown that evolved learning rules, when taken in isolation, may not lead to equilibria. This is a case of reflexive modelling; where game theoretic models are used to assess other features of game theory. I argue that it is possible for reflexive modelling to provide a weak form of justification, but that it falls short in this case.

Deception and the Evolution of Plasticity

Recent models using simple signaling games provide a theoretical setting for investigating the evolutionary connection between signaling and behavioral plasticity. These models have shown that plasticity is typically eliminated in common-interest signaling games. In many real cases of signaling, however, interests do not align. Here, I present a model of the evolution of plasticity in signaling games and consider games of common, opposed, and partially aligned interests. I find that the setting of partial common interest is most conducive to the evolution of plasticity. Plastic individuals succeed by learning when to deceive others and when to trust others’ signals.

Stochasticity, Selection, and the Evolution of Cooperation in a Two-Level Moran Model of the Snowdrift Game

The Snowdrift Game, also known as the Hawk-Dove Game, is a social dilemma in which an individual can participate (cooperate) or not (defect) in producing a public good. It is relevant to a number of collective action problems in biology. In a population of individuals playing this game, traditional evolutionary models, in which the dynamics are continuous and deterministic, predict a stable, interior equilibrium frequency of cooperators. Here, we examine how finite population size and multilevel selection affect the evolution of cooperation in this game using a two-level Moran process, which involves discrete, stochastic dynamics. Our analysis has two main results. First, we find that multilevel selection in this model can yield significantly higher levels of cooperation than one finds in traditional models. Second, we identify a threshold effect for the payoff matrix in the Snowdrift Game, such that below (above) a determinate cost-to-benefit ratio, cooperation will almost surely fix (go extinct) in the population. This second result calls into question the explanatory reach of traditional continuous models and suggests a possible alternative explanation for high levels of cooperative behavior in nature.

Conflict and convention in dynamic networks

An important way to resolve games of conflict (snowdrift, hawk–dove, chicken) involves adopting a convention: a correlated equilibrium that avoids any conflict between aggressive strategies. Dynamic networks allow individuals to resolve conflict via their network connections rather than changing their strategy. Exploring how behavioural strategies coevolve with social networks reveals new dynamics that can help explain the origins and robustness of conventions. Here, we model the emergence of conventions as correlated equilibria in dynamic networks. Our results show that networks have the tendency to break the symmetry between the two conventional solutions in a strongly biased way. Rather than the correlated equilibrium associated with ownership norms (play aggressive at home, not away), we usually see the opposite host–guest norm (play aggressive away, not at home) evolve on dynamic networks, a phenomenon common to human interaction. We also show that learning to avoid conflict can produce realistic network structures in a way different than preferential attachment models.

The coevolution of recognition and social behavior

Recognition of behavioral types can facilitate the evolution of cooperation by enabling altruistic behavior to be directed at other cooperators and withheld from defectors. While much is known about the tendency for recognition to promote cooperation, relatively little is known about whether such a capacity can coevolve with the social behavior it supports. Here we use evolutionary game theory and multi-population dynamics to model the coevolution of social behavior and recognition. We show that conditional harming behavior enables the evolution and stability of social recognition, whereas conditional helping leads to a deterioration of recognition ability. Expanding the model to include a complex game where both helping and harming interactions are possible, we find that conditional harming behavior can stabilize recognition, and thereby lead to the evolution of conditional helping. Our model identifies a novel hypothesis for the evolution of cooperation: conditional harm may have coevolved with recognition first, thereby helping to establish the mechanisms necessary for the evolution of cooperation.

The Evolution of Spite, Recognition, and Morality

Recognition of and responsiveness to the behavioral dispositions of others are key features of moral systems for facilitating social cooperation and the mediation of punishment. Here we investigate the coevolutionary possibilities of recognition and conditional social behavior with respect to both altruism and spite. Using two evolutionary models, we find that recognition abilities can support both spite and altruism but that some can only coevolve with spite. These results show that it is essential to consider harmful social behaviors as both a product of and an influence on the core features of our moral systems.