Evolutionary advantage of guilt: Co-evolution of social and non-social guilt in structured populations

Building ethical machines may involve bestowing upon them the emotional
capacity to self-evaluate and repent on their actions. While apologies
represent potential strategic interactions, the explicit evolution of
guilt as a behavioural trait remains poorly understood. Our study delves
into the co-evolution of two forms of emotional guilt: social guilt
entails a cost, requiring agents to exert efforts to understand
others' internal states and behaviours; and non-social guilt, which
only involves awareness of one's own state, incurs no social cost.
Resorting to methods from evolutionary game theory, we study analytically,
and through extensive numerical and agent-based simulations, whether and
how guilt can evolve and deploy, depending on the underlying structure of
the systems of agents. Our findings reveal that in lattice and scale-free
networks, strategies favouring emotional guilt dominate a broader range of
guilt and social costs compared to non-structured well-mixed populations,
so leading to higher levels of cooperation. In structured populations,
both social and non-social guilt can thrive through clustering with
emotionally inclined strategies, thereby providing protection against
exploiters, particularly for less costly non-social strategies. These
insights shed light on the complex interplay of guilt and cooperation,
enhancing our understanding of ethical artificial intelligence.