Abstract
Behavioral monitoring for recommendation marketplaces is described. Observable time-series actions of autonomous agents and platform signals are ingested per market segment. A competitive-equilibrium baseline is computed and deviations from the baseline are measured. Coordination evidence is derived without inspecting agent internals, including behavioral correlation beyond common cause using Granger-causality-based analysis, reward-punishment dynamics detected from deviation events and retaliatory responses, and platform-mediated signaling exploitation measured by mutual information between platform outputs and subsequent actions. A composite collusion score aggregates component measures using calibrated weights and produces tiered alerts. A causal audit trail may be generated with timelines and analyses and may be cryptographically signed. Platform interventions may be deployed adaptively, including noise injection into ranking or trending signals, information randomization across agent subsets, diversification constraints in top-K outputs, auction mechanism changes including dynamic reserve prices, and temporal delay randomization, with escalation based on subsequent scores.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anonymous, "Behavioral Monitoring and Intervention System for Detecting Emergent Tacit Collusion Among Autonomous Agents in Recommendation Platforms", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10737