I urval från Stockholms universitets publikationsdatabas

• Game theory in biology: 50 years and onwards

  2023. Olof Leimar, John M. McNamara. Philosophical Transactions of the Royal Society of London. Biological Sciences 378 (1876)

  Artikel

  Game theory in biology gained prominence 50 years ago, when Maynard Smith & Price formulated the concept of an evolutionarily stable strategy (ESS). Their aim was to explain why conflicts between animals of the same species usually are of a ‘limited war’ type, not causing serious injury. They emphasized that game theory is an alternative to previous ideas about group selection, which were used by ethologists to explain limited aggression. Subsequently, the ESS concept was applied to many phenomena with frequency dependence in the evolutionary success of strategies, including sex allocation, alternative mating types, contest behaviour and signalling, cooperation, and parental care. Both the analyses of signalling and cooperation were inspired by similar problems in economics and attracted much attention in biology. Here we give a perspective on which of the ambitions in the field have been achieved, with a focus on contest behaviour and cooperation. We evaluate whether the game-theoretical study of the evolution of cooperation has measured up to expectations in explaining the behaviour of non-human animals. We also point to potentially fruitful directions for the field, and emphasize the importance of incorporating realistic behavioural mechanisms into models.

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• The Evolution of Social Dominance through Reinforcement Learning

  2021. Olof Leimar. American Naturalist 197 (5), 561-575

  Artikel

  Groups of social animals are often organized into dominance hierarchies that are formed through pairwise interactions. There is much experimental data on hierarchies, examining such things as winner, loser, and bystander effects, as well as the linearity and replicability of hierarchies, but there is a lack evolutionary analyses of these basic observations. Here I present a game theory model of hierarchy formation in which individuals adjust their aggressive behavior toward other group members through reinforcement learning. Individual traits such as the tendency to generalize learning between interactions with different individuals, the rate of learning, and the initial tendency to be aggressive are genetically determined and can be tuned by evolution. I find that evolution favors individuals with high social competence, making use of individual recognition, bystander observational learning, and, to a limited extent, generalizing learned behavior between opponents when adjusting their behavior toward other group members. The results are in qualitative agreement with experimental data, for instance, in finding weaker winner effects compared to loser effects.

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• Learning leads to bounded rationality and the evolution of cognitive bias in public goods games

  2019. Olof Leimar, John M. McNamara. Scientific Reports 9

  Artikel

  In social interactions, including cooperation and conflict, individuals can adjust their behaviour over the shorter term through learning within a generation, and natural selection can change behaviour over the longer term of many generations. Here we investigate the evolution of cognitive bias by individuals investing into a project that delivers joint benefits. For members of a group that learn how much to invest using the costs and benefits they experience in repeated interactions, we show that overestimation of the cost of investing can evolve. The bias causes individuals to invest less into the project. Our explanation is that learning responds to immediate rather than longer-term rewards. There are thus cognitive limitations in learning, which can be seen as bounded rationality. Over a time horizon of several rounds of interaction, individuals respond to each other's investments, for instance by partially compensating for another's shortfall. However, learning individuals fail to strategically take into account that social partners respond in this way. Learning instead converges to a one-shot Nash equilibrium of a game with perceived rewards as payoffs. Evolution of bias can then compensate for the cognitive limitations of learning.

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