Is Anyone Enforcing the Cartel? What Reinforcement Learning Teaches LLM Agents in Markets
We introduce an audit that tests enforcement by forcing a member to cheat in counterfactual copies of a market, and use it to study what reinforcement learning (RL) teaches agents in pricing and trading.
ProofPaper ↗
Key points
- Evaluations of large language model (LLM) agents report that pricing agents collude at others' expense.
- Yet collusion is defined by enforcement: a cartel, in which competitors share monopoly profits, holds only if a member who cheats is punished.
- In self-play, they learn fixed rules ignoring their rivals; against a punishing rival, to be deterred; against a cheating rival, to match its cuts, never the costly price war that would deter it, which they rarely try.
- Q-learners enforce collusion only in pricing.
Sources (1)
- [1]Is Anyone Enforcing the Cartel? What Reinforcement Learning Teaches LLM Agents in MarketsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 06:18 PM
We introduce an audit that tests enforcement by forcing a member to cheat in counterfactual copies of a market, and use it to study what reinforcement learning (RL) teaches agents in pricing and trading.
Evaluations of large language model (LLM) agents report that pricing agents collude at others' expense.
Extractive summary: sentences quoted from the sources.
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