Thesegames were chosen soastocovertwonatural application domains for EFCE: conflict resolution via a mediator,andbargainingandnegotiation.

While Nash equilibrium in extensive-form games is well understood, very little is known about the properties of extensive-form correlated equilibrium (EFCE), both from a behavioral and from a computational point of view. In this setting, the strategic behavior of players is complemented by an external device that privately recommends moves to agents as the game progresses; players are free to deviate at any time, but will then not receive future recommendations.

A vast body of literature in computational game theory has focused on computing Nash equilibria (NEs) in two-player zero-sum imperfect-information extensive-form games.

Re "...how decomposing the polytope now allows it to be mapped?" If you meant "how does the decomposition help map the problem of computing an optimal correlated Re "I wasn't sure what I was supposed to take away from the experiments" As We'll take all of them into account. Re "broader impact" Thanks for the feedback, we agree with all your points. As you correctly recognized, we use the term "social welfare" to mean the sum of utilities of the players as is typical in the game The maximum payoff is 15. Gurobi is freely available for academic use, but we'll also mention the open-source We are definitely the first to compute optimal EFCE in it. We strongly disagree that " this paper just tells us that the work in Farina et al. [12] is Extending the construction by Farina et al. to handle the more general We strongly disagree with that.

In the former, the mediator draws and recommends a complete normal-form plan to each player before the game starts. This is beneficial when the mediator wants to maximize, e.g., the Appendix A.1 provides a suitable formal definition of the set of EFCEs via the notion of trigger agent (originally This holds for arbitrary EFGs with multiple players and/or chance moves. Unfortunately, that algorithm is mainly a theoretical tool, and it is known to have limited scalability beyond toy problems. However, their algorithm is centralized and based on MCMC sampling which may limit its practical appeal. B.1 Proofs for Section 4 The following auxiliary result is exploited in the proof of Theorem 1. Lemma 4. This concludes the proof.Theorem 1.