Abstract
Systems and methods monitor autonomous pricing markets for tacit collusion using externally observable posted prices and market outcomes. Multiple indicators are computed, including a price elevation index with Newey-West HAC uncertainty, Granger causality using vector autoregression with heteroskedasticity-consistent errors, a constrained three-state hidden Markov model producing a collusive-state posterior probability, and conditional mutual information estimated by k-nearest neighbors and normalized against a permutation null. The indicators are combined into a composite collusion score. An encoding regime is inferred from observables including price dimensionality, correlation structure, and within-run dispersion, and regime-specific thresholds are applied for detection. The system may estimate latent collusion potential for aggregated regimes using an empirically measured aggregation-collapse ratio, and may apply interventions that alter information exposure through forced aggregation, noise injection, information randomization, or temporal delay injection.
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Recommended Citation
Anonymous, "Encoding-Aware Collusion Detection and Intervention System for Autonomous Pricing Markets", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10630