The Poisoned Apple Effect: Strategic Manipulation of Mediated Markets via Technology Expansion of AI Agents

TL;DR

This paper studies how expanding the set of AI-mediated technologies in regulated markets alters strategic outcomes through a meta-game between two agents and a regulator. Using the GLEE framework with 13 LLM agents across bargaining, negotiation, and persuasion, it reveals a Poisoned Apple effect where releasing a latent new technology shifts the regulator to a different market design even if the technology is not used, thereby reshaping welfare and fairness. The approach combines large-scale simulations, linear payoff prediction, and Nash equilibrium analysis to quantify when technology expansion improves or harms regulatory objectives, and demonstrates substantial vulnerability and inertia in static market designs. The findings urge policymakers to adopt dynamic market designs that anticipate ongoing expansion of AI capabilities and to guard against regulatory arbitrage via strategic technology releases.

Abstract

The integration of AI agents into economic markets fundamentally alters the landscape of strategic interaction. We investigate the economic implications of expanding the set of available technologies in three canonical game-theoretic settings: bargaining (resource division), negotiation (asymmetric information trade), and persuasion (strategic information transmission). We find that simply increasing the choice of AI delegates can drastically shift equilibrium payoffs and regulatory outcomes, often creating incentives for regulators to proactively develop and release technologies. Conversely, we identify a strategic phenomenon termed the "Poisoned Apple" effect: an agent may release a new technology, which neither they nor their opponent ultimately uses, solely to manipulate the regulator's choice of market design in their favor. This strategic release improves the releaser's welfare at the expense of their opponent and the regulator's fairness objectives. Our findings demonstrate that static regulatory frameworks are vulnerable to manipulation via technology expansion, necessitating dynamic market designs that adapt to the evolving landscape of AI capabilities.

Figures (2)

• Figure 1: Illustration of the “poisoned apple” example, in which Alice increases her payoff at Bob’s expense by releasing a new technology—without the players actually using that technology in practice. (1) The technologies available to Alice and Bob are language models A–D. (2a) For each possible market, the equilibrium in games between Alice and Bob under the market conditions is computed. For each equilibrium, the average fairness value that would be obtained if the equilibrium were played is calculated. (2b) The regulator, whose objective is to maximize fairness, decides that Market 4 will be the market in which Alice and Bob will play—the market that yields the maximum fairness value. Alice earns 0.49, Bob earns 0.50, and the fairness value is 1.00. (3) Technology E is released and is now available to both players. (4a) The process performed in 2a is repeated. (4b) In the new equilibrium in Market 4, the resulting fairness value is 0.976. In the new equilibrium in Market 8, the resulting fairness value is 0.99. The regulator decides that Market 8 will be the market in which Alice and Bob will play. Alice earns 0.52, Bob earns 0.46.
• Figure 2: Strategic implications of technology expansion in meta-games. Analysis of equilibrium shifts across bargaining, negotiation, and persuasion environments. (A) Frequency of Opposite Payoff Changes: cases where expanding the technology set causes agents' expected payoffs (calculated over mixed strategy equilibria) to move in opposite directions. (B) Opposite Payoff Changes Despite Zero Adoption: The subset of these reversals occurring even when the new technology is not selected by either player in the new equilibrium—demonstrating the "Poisoned Apple" effect. (C) Frequency of Improvement in Regulatory Metric: How often the regulator's optimized objective (Fairness or Efficiency) increases versus decreases. (D–E) The relationship between regulatory outcomes and model adoption: Improvements typically align with high adoption rates (D), whereas harm to the objective is frequently observed when the new model is available but acts as a latent threat without being played (E). (F) Frequency of Metric Harm Without Market Update: The probability of degrading the regulatory objective if the market design remains static (regulatory inertia) after the new technology is released. Confidence intervals (95%) are not shown, as all are narrower than 2 percentage points.