Hybrid Human–AI Interaction in Game-Theoretic Corporate Governance: Matching ESG Targets with Overarching Sustainable Development Goals

Abstract: Corporate governance may admit multiple Nash equilibria. I develop a game-theoretic framework in which AI functions as an equilibrium selector by reducing information frictions, synchronizing beliefs, and expanding the basin of attraction for high‑ESG outcomes. Using 450 firms (2014–2024), I estimate that governance-directed AI adoption lifts ESG by 8–15% and is associated with lower implied cost of equity, higher Tobin’s Q, greater investment, and tighter credit spreads. Mechanism tests show improved disclosure (+8.24), tighter target discipline, and dampened strategic complementarities, with stronger effects in network‑central firms and high‑complementarity industries. Effects grow over time and are most pronounced where disclosure is complex. The results link governance technology to financing conditions and firm value.

Keywords: Nash equilibria, AI governance, ESG performance, Bayesian forecasts, Multilayer networks, Sustainable governance

DOI: 10.54941/ahfe1007083

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