Game theory has been useful for understanding risk-taking and cooperative behavior. However, in studies of the neural basis of decision-making during games of conflict, subjects typically play against opponents with predetermined strategies. The present study introduces a neurobiologically plausible model of action selection and neuromodulation, which adapts to its opponent's strategy and environmental conditions. The model is based on the assumption that dopaminergic and serotonergic systems track expected rewards and costs, respectively. The model controlled both simulated and robotic agents playing Hawk-Dove and Chicken games against subjects. When playing against an aggressive version of the model, there was a significant shift in the subjects' strategy from Win-Stay-Lose-Shift to Tit-For-Tat. Subjects became retaliatory when confronted with agents that tended towards risky behavior. These results highlight the important interactions between subjects and agents utilizing adaptive behavior. Moreover, they reveal neuromodulatory mechanisms that give rise to cooperative and competitive behaviors.

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Game theory is commonly used to study social behavior in cooperative or competitive situations. One socioeconomic game, Stag Hunt, involves the trade-off between social and individual benefit by offering the option to hunt a low-payoff hare alone or a high-payoff stag cooperatively. Stag Hunt encourages the creation of social contracts as a result of the payoff matrix, which favors cooperation. By playing Stag Hunt with set-strategy computer agents, the social component is degraded because of the inability of subjects to dynamically affect the outcomes of iterated games, as would be the case when playing against another subject. However, playing with an adapting agent has the potential to evoke unique and complex reactions in subjects because of its ability to change its own strategy based on its experience over time, both within and between games. In the present study, 40 subjects played the iterated Stag Hunt with five agents differing in strategy: exclusive hare hunting, exclusive stag hunting, random, Win-Stay-Lose-Shift, and adapting. The results indicated that the adapting agent caused subjects to spend more time and effort in each game, exhibiting a more complicated path to their destination. This suggests that adapting agents exhibit behavior similar to human opponents, evoking more natural social responses in subjects.

Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collab-oration in cooperative tasks. Here, we discuss variations of centralized training and describe a recent survey of algorithmic approaches. The goal is to explore how different implementations of information sharing mechanism in centralized learning may give rise to distinct group coordinated behaviors in multi-agent systems performing cooperative tasks.