Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents
CoRR(2024)
摘要
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., caring about maximizing some
outcome over time) or norm-based (i.e., focusing on conforming to a specific
norm here and now). 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 a 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 classes of moral agents are able to steer selfish
agents towards more cooperative behavior.
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