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We consider an information elicitation game where the center needs the agent to self-report her actual usage of a service and charges her a payment accordingly. The center can only observe a partial signal, representing part of the agents true consumption, that is generated randomly from a publicly known distribution. The agent can report any information, as long as it does not contradict the signal, and the center issues a payment based on the reported information. Such problems find application in prosumer pricing, tax filing, etc., when the agents actual consumption of a service is masked from the center and verification of the submitted reports is impractical. The key difference between the current problem and classic information elicitation problems is that the agent gets to observe the full signal and act strategically, but the center can only see the partial signal. For this seemingly impossible problem, we propose a penalty mechanism that elicits truthful self-reports in a repeated game. In particular, besides charging the agent the reported value, the mechanism charges a penalty proportional to her inconsistent reports. We show how a combination of the penalty rate and the length of the game incentivizes the agent to be truthful for the entire game, a phenomenon we call fear of tomorrow verification. We show how approximate results for arbitrary distributions can be obtained by analyzing Bernoulli distributions. We extend our mechanism to a multi-agent cost sharing setting and give equilibrium results.
We consider an example of stochastic games with partial, asymmetric and non-classical information. We obtain relevant equilibrium policies using a new approach which allows managing the belief updates in a structured manner. Agents have access only t
This paper considers repeated games in which one player has more information about the game than the other players. In particular, we investigate repeated two-player zero-sum games where only the column player knows the payoff matrix A of the game. S
We define the notion of Bayes correlated Wardrop equilibrium for general nonatomic games with anonymous players and incomplete information. Bayes correlated Wardrop equilibria describe the set of equilibrium outcomes when a mediator, such as a traffi
We consider extensive games with perfect information with well-founded game trees and study the problems of existence and of characterization of the sets of subgame perfect equilibria in these games. We also provide such characterizations for two cla
The combination of deep reinforcement learning and search at both training and test time is a powerful paradigm that has led to a number of successes in single-agent settings and perfect-information games, best exemplified by AlphaZero. However, prio