Research map / Multi-agent systems and games / Games and equilibria
Observation Interference in Partially Observable Assistance Games
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Theoretically and experimentally studies when an optimal AI assistant in a partially observable assistance game (POAG) would take observation-interfering actions, showing such incentives arise from private information, preference querying, and human irrationality.
Problem
Whether AI assistants that share the human's goals would ever have an incentive to interfere with the human's observations in partially observable cooperative settings, which is relevant to AI deception and value alignment.
Approach
Defines observation interference via information garbling (less-informative observation distributions) at the action and policy levels, and proves that optimal policy pairs in POAGs may require action-level interference but never require policy-level interference; also shows interference arises for preference querying and Boltzmann-irrational humans.
Key results
- Proves optimal policy pairs can require action-level observation interference even when the human plays optimally, but a non-interfering policy-level solution always exists.
- Shows that with a Boltzmann-rational human, the assistant prefers interference for beta above approximately 0.77361 in the constructed example.
- In a product selection experiment (d=5 products, Monte Carlo with 30,000 trials), more private observations for the assistant leads to more interference (k) in optimal policy.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the number of assistant private observations | Observation-interference policies · Product selection game | expected reward | Helped |
| Increase the number of interfered observations when the assistant has no private observations | Product selection game · Product selection game | expected reward | Hurt |
| Interfere with as many observations as possible when human rationality is very low (β = 0.01) | Product selection game · Product selection game | expected reward | Helped |
Surprising findings
- Interference is preferred at high human rationality (beta above about 0.77) in the Boltzmann example, contrary to the intuition that interference only helps irrational humans.
- Action-level interference can be optimal even though the classic value-of-information theorem says more information is never harmful in single-agent settings.
Limitations
- Focuses on optimal solutions (optimal policy pairs and best responses) rather than learned or approximate policies.
- Experiments use a single simple product-selection model game with a Boltzmann human model.
- Considers only a single human and single assistant, and mostly the Boltzmann rationality model without human studies.
Experimental setup
Metrics: expected reward
Builds on: assistance games cooperative inverse reinforcement learning DecPOMDP Boltzmann rationality
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