Economies 2026, 14(8), 343; https://doi.org/10.3390/economies14080343 - 13 Aug 2026
Abstract
Digital markets increasingly coordinate prices, matches, orders, and allocations through automated systems whose decision–execution loops can close faster than humans can intervene. This article develops a microfounded framework in which latency is a temporal feasibility constraint that complements adjustment costs, information delay, costly
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Digital markets increasingly coordinate prices, matches, orders, and allocations through automated systems whose decision–execution loops can close faster than humans can intervene. This article develops a microfounded framework in which latency is a temporal feasibility constraint that complements adjustment costs, information delay, costly information acquisition, queueing, and technological execution costs. The model distinguishes common latency, human intervention latency, and relative latency. Common latency is produced by platform and participant investment and affects welfare through the freshness of the state on which decisions are executed. Human intervention is represented by a smooth, task- and organization-specific probability q(L,z,s), derived from a distribution of completion times and modified by interface and organizational support. Human, hybrid, and algorithmic decision technologies differ in speed, accuracy, cost, and systematic misspecification risk. Relative speed is modeled as a strategic priority contest in which each intermediary’s best response depends on rivals’ investments, while platform rules determine the sensitivity and value of being first. The framework derives conditions for human-algorithm substitution, welfare-improving common-speed investment, socially excessive strategic speed investment, and welfare-enhancing batching or latency floors. It separates temporal from structural market distortions, integrates decision-technology quality and state freshness in a total-welfare function, and develops an incidence model that traces gains across heterogeneous users, intermediaries, and infrastructure owners. Robustness results cover diffusion, mean-reverting, jump, stochastic-volatility, and regime-switching state processes. An illustrative dynamic simulation, explicit scope conditions, and an operational empirical agenda show how the theory can be tested without claiming empirical calibration. The central contribution is a non-equivalence result: when latency enters the probability of successful intervention, shortening the decision window can change the technology and locus of marginal choice even when information, objectives, adjustment costs, and the substantive decision rule are held fixed.
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(This article belongs to the Special Issue New Technologies, Green Transformation and Education for Sustainable Growth)
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