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

Sheyan Lalmohammed

Papers from arXiv.org

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.

Date: 2025-01
New Economics Papers: this item is included in nep-ain, nep-cmp, nep-evo, nep-gth and nep-hme
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2501.15317 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2501.15317

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().