Stochastic Modeling of Agentic Information Finance: Convergence Analysis of the Information-Incentive Gap

Анотація

We study the epistemic efficiency of decentralized prediction markets under autonomous agentic liquidity. We introduce the information-incentive gap (G) – the discrepancy between ground truth and the market-implied probability – and establish, via Itô's calculus and exact solution of the resulting moment ODE, exponential convergence of its second moment together with an explicit upper bound for the gap of order O(σ/λ−−√). A two-level empirical study on information-driven event categories (Politics, Economics, Finance, Crypto Markets), drawing on approximately 40 million time-series records collected over the study period, is consistent with the model: (i) platform-level analysis of N=100 resolved binary events per platform shows the mean gap decreasing from G¯=0.517 at T−168 h to G¯=0.229 at T−30 min for Kalshi, and from 0.583 to 0.002 for Polymarket, with an empirical convergence rate λemp≈1.4×10−6 s−1; (ii) a paired cross-platform comparison of N=34 matched event groups shows that Polymarket exhibits a lower mean gap than Kalshi (mean ΔG=0.27 at T−6 h; Polymarket leads in 85% of pairs), consistent with the theoretical dependence of convergence speed on liquidity-driven λ. Monte Carlo simulation (N=50000 paths) confirms a >276× reduction in convergence latency and a 109× improvement in the Information Efficiency Ratio (IER) compared to the human-centric baseline.

Класифікація

Ідентифікатори

Схожі роботи