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AI Risk

Multi-Agent Safety Is Not Assured by Single-Agent Safety

"A foremost lesson of game theory is that optimal decision-making within a single-agent setting (i.e. selfishly optimizing for an agent’s own utility) can produce sub-optimal outcomes in the presence of other strategic agents. Failing to account for the strategic nature of other agents can cause an agent to adopt strategies under which potentially everyone, including the agent itself, ends up worse off (Schelling...

AI Risk7. AI System Safety, Failures, & Limitations7.6 > Multi-agent risks3 - Other

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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"A foremost lesson of game theory is that optimal decision-making within a single-agent setting (i.e. selfishly optimizing for an agent’s own utility) can produce sub-optimal outcomes in the presence of other strategic agents. Failing to account for the strategic nature of other agents can cause an agent to adopt strategies under which potentially everyone, including the agent itself, ends up worse off (Schelling, 1981; Harsanyi, 1995; Roughgarden, 2005; Nisan, 2007). Examples include collective action problems (or ‘social dilemmas’) such as arms races or the depletion of common resources, as well as other kinds of market failures such as those caused by asymmetric information or negative externalities (Bator, 1958; Coase, 1960; Buchanan and Stubblebine, 1962; Kirzner, 1963; Dubey, 1986)."

Domain7. AI System Safety, Failures, & Limitations
Subdomain7.6 > Multi-agent risks
Entity3 - Other
Intent3 - Other
Timing3 - Other
CategoryMulti-Agent Safety Is Not Assured by Single-Agent Safety
Subcategoryn/a

Suggested mitigations

Defenses that may help with related attacks.

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

Source

Research source for this risk, when available.