most citedMulti-Agent Risks from Advanced AI

10 citations · 10 across the 5 of their papers we have counts for

collaborators

9 papers

cs.GT2025

Choosing What Game to Play without Selecting Equilibria: Inferring Safe (Pareto) Improvements in Binary Constraint Structures

Caspar Oesterheld, Vincent Conitzer

We consider a setting in which a principal gets to choose which game from some given set is played by a group of agents. The principal would like to choose a game that favors one o…

cs.GT2025

Maximizing Social Welfare with Side Payments

Ivan Geffner, Caspar Oesterheld, Vincent Conitzer

We examine normal-form games in which players may \emph{pre-commit} to outcome-contingent transfers before choosing their actions. In the one-shot version of this model, Jackson an…

cs.GT2025

Promises Made, Promises Kept: Safe Pareto Improvements via Ex Post Verifiable Commitments

Nathaniel Sauerberg, Caspar Oesterheld

A safe Pareto improvement (SPI) [41] is a modification of a game that leaves all players better off with certainty. SPIs are typically proven under qualitative assumptions about th…

cs.GT2025

Computing Game Symmetries and Equilibria That Respect Them

Emanuel Tewolde, Brian Hu Zhang, Caspar Oesterheld +2

Strategic interactions can be represented more concisely, and analyzed and solved more efficiently, if we are aware of the symmetries within the multiagent system. Symmetries also…

cs.MA202510 cited

Multi-Agent Risks from Advanced AI

Lewis Hammond, Alan Chan, Jesse Clifton +41

The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity. These s…

cs.GT2025

Characterising Simulation-Based Program Equilibria

Emery Cooper, Caspar Oesterheld, Vincent Conitzer

In Tennenholtz's program equilibrium, players of a game submit programs to play on their behalf. Each program receives the other programs' source code and outputs an action. This c…