Inspired by psychological and evolutionary studies, we present two theoretical models wherein agents have the potential to express guilt, with the ambition to study the role of this emotion in the promotion of pro-social behaviour.We show that the inclusion of the emotion of guilt, in the sense arising from actual harm done to others from inappropriate action or inaction, is worthwhile to incorporate in evolutionary game theory models of cooperation, for it can increase cooperation by correcting and inhibiting defection. The abstract study thereof profitably transpires to concrete considerations in the design of artificial multi-agent populations. To achieve this goal, analytical and numerical methods from evolutionary game theory have been employed, but not shown in too fine detail here, to identify that reasonable conditions exist for which enhanced cooperation emerges within the context of the iterated prisoners dilemma. Guilt is modelled explicitly as two features, i.e. a counter that keeps track of the number of transgressions and a threshold that dictates when alleviation (through for instance apology and self-punishment) is required for an emotional agent. Such alleviation introduces an effect on the payoff of the agent experiencing guilt. We show that when the system consists of agents that resolve their own guilt without considering the co-player's attitude towards guilt alleviation then cooperation does not emerge. In that case, agents expressing no guilt or having no incentive to alleviate the guilt they experience easily dominate the guilt prone ones. On the other hand, when the guilt prone focal agent requires that guilt only needs to be alleviated when guilt alleviation is also manifested by a defecting co-player, then cooperation may thrive. This observation proves consistent in a generalised model discussed in this article. In summary, our analysis provides important insights into the design of multi-agent and cognitive agent systems, wherein the inclusion of guilt modelling can improve agents' cooperative behaviour and overall benefit.
|Number of pages||4|
|Publication status||Published - 1 Jan 2017|
|Event||2017 Annual Convention of the Society for the Study of Artificial Intelligence and the Simulation of Behaviour, AISB 2017 - Bath, United Kingdom|
Duration: 18 Apr 2017 → 21 Apr 2017
|Conference||2017 Annual Convention of the Society for the Study of Artificial Intelligence and the Simulation of Behaviour, AISB 2017|
|Period||18/04/17 → 21/04/17|