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Trust across Disciplines

Trust & game theory - Regarding computer science

The game theoretic approach is one of the first that engaged researchers into studies about trust; the dilemma developed for experiments showed the intrinsec complexity of our choices regarding cooperation, altruism, strategies that develop through time and so on.

Computer scientist developed software simulation for studying PD games with a great number of variables and with large iterations. Their studies dealt with negotiations, learning and cooperation within agent societies.

Sandip Sen

Multiagent learning literature has looked at iterated two-player games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. Such equilibrium configuration implies that there is no motivation for one player to change its strategy if the other does not. Often, in general sum games, a higher payoff can be obtained by both players if one chooses not to respond optimally to the other player. By developing mutual trust, agents can avoid iterated best responses that will lead to a lesser payoff Nash Equilibrium.

[Sen considers] 1-level agents (modelers) who select actions based on expected utility considering probability distributions over the actions of the opponent(s). In certain situations, such stochastically-greedy agents can perform better (by developing mutually trusting behaviour) that those that explicitly attempt to converge to Nash Equilibrium.

References

Banjeree, B., Mukherjee R. & Sen, S. (2000). Learning Mutual Trust. In proceedings of the 3rd Workshop on Deception, Fraud and Trust In Agent Societies. Barcelona.

Other perspectives on game theory

Dilemma were developed and analysed also in economics and by social and psychological scientists.