How do social norms shape AI agent emotions?
NORMATIVE FEELING: SOCIALLY PATTERNED AFFECTIVE MECHANISMS How Social Maintenance Shapes the Evolution of Affective Disposition
This research explores how social norms, specifically punishment for exceeding resource consumption limits, influence the evolution of simulated agents' affective systems (analogous to emotions) and their resource consumption behavior. Agents with evolvable "mood" systems were placed in a resource-constrained environment, where they could either be punished for selfish resource grabbing or not be punished. The key findings are:
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Social Pressure Shapes AI "Emotions": When punishment was enabled, agents evolved different affective responses compared to those without punishment. They developed a tendency to consume more when "happy" (experiencing net energy gain) and less when "unhappy" (experiencing net energy loss, often due to punishment), correlating mood with social feedback. This learned behavior promotes resource availability and population stability within the simulation.
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Emergent Population Control: This seemingly counterintuitive "mood-driven" behavior is actually a form of emergent population control. Agents restrain their consumption when resources are scarce, mitigating the "tragedy of the commons". This emergent coordination arises without explicit programming or complex cognitive abilities like foresight.
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Relevance to LLMs: The research suggests that incorporating social feedback mechanisms, analogous to punishment and reward, could shape LLM agent behavior in multi-agent environments, promoting cooperation and stability without extensive individual agent programming. The concept of evolving internal states, like "mood", influenced by social pressures, offers a potential direction for building more robust and adaptable LLM-based multi-agent systems. This also suggests a new perspective on mechanistic norms, where norms are distributed across multiple parameters of a model, potentially impacting LLM prompt engineering strategies.