Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents
<|reference_start|>Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents: Growing concerns about safety and alignment of AI systems highlight the importance of embedding moral capabilities in artificial agents: a promising solution is the use of learning from experience, i.e., Reinforcement Learning. In multi-agent (social) environments, complex population-level phenomena may emerge from interactions between individual learning agents. Many of the existing studies rely on simulated social dilemma environments to study the interactions of independent learning agents; however, they tend to ignore the moral heterogeneity that is likely to be present in societies of agents in practice. For example, at different points in time a single learning agent may face opponents who are consequentialist (i.e., focused on maximizing outcomes over time), norm-based (i.e., conforming to specific norms), or virtue-based (i.e., considering a combination of different virtues). The extent to which agents' co-development may be impacted by such moral heterogeneity in populations is not well understood. In this paper, we present a study of the learning dynamics of morally heterogeneous populations interacting in a social dilemma setting. Using an Iterated Prisoner's Dilemma environment with a partner selection mechanism, we investigate the extent to which the prevalence of diverse moral agents in populations affects individual agents' learning behaviors and emergent population-level outcomes. We observe several types of non-trivial interactions between pro-social and anti-social agents, and find that certain types of moral agents are able to steer selfish agents towards more cooperative behavior.<|reference_end|>
@article{tennant2024dynamics,
    title={Dynamics of Moral Behavior in Heterogeneous Populations of Learning
  Agents},
    author={Elizaveta Tennant, Stephen Hailes, Mirco Musolesi},
    journal={Proceedings of the 7th AAAI/ACM Conference on AI, Ethics, and
  Society (AIES), vol. 7, (2024), pp 1444-1454.},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04202},
    primaryClass={cs.MA cs.AI cs.CY cs.LG}
}