A theory of algorithmic markets

This paper develops a dynamic framework for analyzing markets in which decision-making is increasingly delegated to adaptive algorithms. In contrast to classical approaches that treat equilibrium as the primitive object of analysis, the framework models markets as systems of interacting learning processes operating within endogenous informational environments. Market outcomes arise from the interaction between adaptive decision rules, endogenous data generation, and feedback amplification. The analysis shows that market stability depends jointly on learning responsiveness and feedback intensity. When adaptive updating is sufficiently regular and feedback effects remain weak, the system converges toward stable equilibrium-like behavior, nesting standard equilibrium models as limiting benchmark cases. As learning responsiveness and endogenous feedback increase jointly, however, the adaptive process may generate persistent fluctuations, endogenous volatility, oscillatory dynamics, and local instability. Equilibrium therefore emerges not as a maintained assumption, but as a possible limiting outcome of the underlying learning dynamics. The framework further implies an adaptation-stability trade-off: faster responsiveness may improve information processing and adjustment while simultaneously amplifying endogenous instability. The analysis also shows how decentralized adaptive interaction may generate synchronized or coordinated patterns of behavior without explicit communication. A stylized quantitative illustration demonstrates how the interaction between learning and feedback shapes distinct dynamic regimes, while an illustrative empirical application to algorithmic trading environments documents patterns broadly consistent with the amplification mechanisms emphasized by the theory. The empirical evidence is interpreted as suggestive rather than definitive causal confirmation. More broadly, the paper suggests that understanding algorithmic markets may require analytical approaches that place adaptive learning, endogenous information generation, and feedback dynamics at the center of economic analysis.