Informational Resolution and Collective Extraction: An Informational Convergence Theorem for Large Games

Suresh Deman

Abstract: This paper develops an informational convergence theorem for large dynamic games with imperfect public monitoring. I show that equilibrium differences between finite-agent and continuum-agent models are governed not by cardinality but by informational resolution- the precision with which individual deviations are statistically detectable. In an n-agent economy where unilateral deviations shift aggregates by 1/n and public signals are observed with noise σ_n, equilibrium selection depends on the scaling of n σ_n.If n σ_n → 0, finite-agent extraction equilibria survive; if n σ_n → ∞, the economy converges to the continuum equilibrium. The result provides a general convergence principle linking population size and monitoring precision and yields institutional implications for takeover markets, redevelopment holdouts, and fiscal capacity.

Keywords: large games, informational precision, equilibrium convergence, public monitoring, statistical detectability

How to Cite?: Suresh Deman, "Informational Resolution and Collective Extraction: An Informational Convergence Theorem for Large Games", Volume 15 Issue 3, March 2026, International Journal of Science and Research (IJSR), Pages: 1134-1137, https://www.ijsr.net/getabstract.php?paperid=SR26227044644, DOI: https://dx.doi.org/10.21275/SR26227044644

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