Welfare Modeling with AI as Economic Agents: A Game-Theoretic and Behavioral Approach

TL;DR

This paper develops a welfare model for human-AI interactions using game theory and behavioral insights, highlighting trust, collaboration, and equity to optimize societal benefits in AI-integrated economic systems.

Contribution

It introduces a novel agent-based framework combining game theory and behavioral factors to evaluate welfare in human-AI ecosystems.

Findings

Trust and skill development are key to maximizing welfare.

AI complexity impacts trade-offs between equity and efficiency.

Dynamic trust influences collaboration outcomes.

Abstract

The integration of artificial intelligence (AI) into economic systems represents a transformative shift in decision-making frameworks, introducing novel dynamics between human and AI agents. This paper proposes a welfare model that incorporates both game-theoretic and behavioral dimensions to optimize interactions within human-AI ecosystems. By leveraging agent-based modeling (ABM), we simulate these interactions, accounting for trust evolution, perceived risks, and cognitive costs. The framework redefines welfare as the aggregate utility of interactions, adjusted for collaboration synergies, efficiency penalties, and equity considerations. Dynamic trust is modeled using Bayesian updating mechanisms, while synergies between agents are quantified through a collaboration index rooted in cooperative game theory. Results reveal that trust-building and skill development are pivotal to maximizing welfare, while sensitivity analyses highlight the trade-offs between AI complexity, equity, and efficiency. This research provides actionable insights for policymakers and system designers, emphasizing the importance of equitable AI adoption and fostering sustainable human-AI collaborations.