Estimated Dynamic Equilibrium Model: Supply and Demand as a Sample Path of a Stochastic Process

TL;DR

The paper introduces EDEM, a stochastic agent-based model explaining persistent disequilibrium and price growth in markets, with implications for understanding bubbles and divergence-of-opinion phenomena.

Contribution

It provides a generative mechanism for disequilibrium driven by order-statistic bias, extending divergence-of-opinion theory to dynamic settings.

Findings

Expected prices drift upward due to order-statistic bias.

Simulations show exponential price growth without investor irrationality.

Identifies six market regimes from stability to bubbles.

Abstract

We introduce the Estimated Dynamic Equilibrium Model (EDEM), an agent-based framework that treats supply and demand as a coupled stochastic process driven by heterogeneous, noisy agent valuations. The model's primary technical contribution is the identification of a generative mechanism for persistent disequilibrium: when market-clearing prices are sequentially sampled from the upper tail of noisy bid distributions and recycled as inputs for future valuations, expected prices drift upward despite strictly zero-mean estimation errors. We derive this order-statistic bias in closed form for i.i.d. uniform bids and use simulations to show that compounding this bias across epochs yields exponential price growth without requiring assumptions of investor optimism or irrationality. This framework extends Miller's divergence-of-opinion theory to a dynamic setting, recovering Walrasian equilibrium and Miller's static premium as limiting cases. Through controlled experiments and sensitivity analysis on a simulated real-estate neighborhood, we identify six distinct regimes-ranging from band-stability to runaway bubbles-emerging from a single agent ruleset. These results offer a potential explanation for the contradictory findings in the empirical divergence-of-opinion literature and suggest that machine-learning valuation algorithms may inadvertently amplify this inherent statistical bias.