Social contracts and human–computer interaction with simulated adapting agents

Abstract

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.