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AI Risk

Incompatible strategies

"Incompatible Strategies. Even if all agents can perform well in isolation, miscoordination can still occur due to the agents choosing incompatible strategies (Cooper et al., 1990). Competitive (i.e., two- player zero-sum) settings allow designers to produce agents that are maximally capable without taking other players into account. Crucially, this is possible because playing a strategy at equilibrium in the zero...

AI Risk7. AI System Safety, Failures, & Limitations7.6 > Multi-agent risks2 - Post-deployment

Record summary

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Techniques0Attack methods connected to this risk.
Mitigations0Defenses that may help with related attacks.
Domain7. AI System Safety, Failures, & LimitationsThe broad risk area this belongs to.

Risk profile

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"Incompatible Strategies. Even if all agents can perform well in isolation, miscoordination can still occur due to the agents choosing incompatible strategies (Cooper et al., 1990). Competitive (i.e., two- player zero-sum) settings allow designers to produce agents that are maximally capable without taking other players into account. Crucially, this is possible because playing a strategy at equilibrium in the zero-sum setting guarantees a certain payoff, even if other players deviate from the equilibrium (Nash, 1951). On the other hand, common-interest (and mixed-motive) settings often allow a vast number of mutually incompatible solutions (Schelling, 1980), which is worsened in partially observable environments (Bernstein et al., 2002; Reif, 1984)."

Domain7. AI System Safety, Failures, & Limitations
Subdomain7.6 > Multi-agent risks
Entity2 - AI
Intent2 - Unintentional
Timing2 - Post-deployment
CategoryMiscoordination
SubcategoryIncompatible strategies

Suggested mitigations

Defenses that may help with related attacks.

No propagated mitigations. No defense is available through the connected attack methods.

Source

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