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I. Anagnostides

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#machine learning Preprint Oct 2026

Normal-Form Correlation in Markov Games

There has been a surge of recent work on correlated equilibrium concepts in Markov games. However, existing results focus on concepts weaker than normal-form correlated equilibria (NFCEs), leaving open the more challenging question of computing such equilibria, which goes back to the seminal work of Papadimitriou and R...

I. Anagnostides, Constantinos Daskalakis, Gabriele Farina et al. · 0 citations
#machine learning Preprint Sep 2026

Algorithmic Collusion and the Complexity of Information-Value-Free Equilibria

The complexity of IVF(C)CEs in succinct games, which model more realistic strategic interactions that feature either many players or exponentially many pure strategies, is examined, showing that it is at least as hard as the P-matrix linear complementarity problem, and hence as hard as simple stochastic games.

I. Anagnostides, Weiqiang Zheng · 1 citation
#artificial intelligence Preprint Jul 2026

Preference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Values

A novel preference elicitation algorithm for linear utilities that outperforms prior techniques in practice and is applied to heart transplant allocation where a policy must balance competing objectives such as post-transplant outcomes, waitlist mortality, geographic ease, and equity.

Itai Zilberstein, I. Anagnostides, Zachary W. Sollie et al. · 0 citations

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