Fundamental market design as a layer of AI-agent alignment

Abstract

This paper argues that AI-agent alignment in markets should not be understood only as a property of agents, but also as a property of the interaction infrastructure in which agents act. In financial markets, this infrastructure is the market core: the rule system that determines how orders enter, interact, match, persist, and stabilize. If this fundamental interaction layer allows or rewards undesired behaviour, then higher-level alignment of agents may be insufficient. We propose to view fundamental market design as a layer of AI-agent alignment. Alongside the important work of computational economics in modelling agents, strategies, and learning, we focus on a complementary but more fundamental layer: the formal modelling of the market core itself. Market design, especially at the level of the core mechanism, can benefit from a rigour characteristic of theoretical computer science. This gives a transparent-box model of the market, whose core properties can be formally specified and reasoned about. It also lets us treat the trading venue not as a static order book, but as a computational process combining resident orders with incoming order flow, and ask which computational model, perhaps yet unknown, naturally lies at its core. This perspective is especially relevant for markets populated by adaptive or AI agents. Such agents may learn what the mechanism rewards, including speed, delay, liquidity provision, or manipulation. These behaviours are not only properties of individual agents, but may emerge from the agent-mechanism system. We therefore argue that transparent formal models of market cores can support incentive-oriented analysis and the design of mechanisms in which desirable behaviours are structurally favoured and undesirable behaviours are harder to sustain.