Online mechanism design (OMD) addresses the problem of sequential decision making in a stochastic environment with multiple self-interested agents. The goal in OMD is to make value-maximizing decisions despite this self-interest. In previous work we presented a Markov decision process (MDP)-based approach to OMD in large-scale problem domains. In practice the underlying MDP needed to solve OMD ...

We introduce the concept of a trust network—a decentralized payment infrastructure in which payments are routed as IOUs between trusted entities. The trust network has directed links between pairs of agents, with capacities that are related to the credit an agent is willing to extend another; payments may be routed between any two agents that are connected by a path in the network. The network ...

Since their introduction by Dung over a decade ago, abstract argumentation frameworks have received increasing interest in artificial intelligence as a convenient model for reasoning about general characteristics of argument. Such a framework consists of a set of arguments and a binary defeat relation among them. Various semantic and computational approaches have been developed to characterise ...

We revisit the classic problem of fair division from a mechanism design perspective, using Proportional Fairness as a benchmark. In particular, we aim to allocate a collection of divisible items to a set of agents while incentivizing the agents to be truthful in reporting their valuations. For the very large class of homogeneous valuations, we design a truthful mechanism that provides every age...

The curse of the Revelation Principle is that it leads us to focus on unrealistic mechanisms in which agents report all private information to the principal, who then makes all decisions centrally. This is not true when communication costs are introduced. Then partial communication of information, sequential back-and-forth conversations, and decentralization of decisions become part of optimal ...

We study mechanism design in non-Bayesian settings of incomplete information, when the designer has no information about the players, and the players have arbitrary, heterogeneous, first-order, and possibilistic beliefs about their opponents’ payoff types. Using such beliefs, in auctions of a single good, we • define a revenue benchmark at least as high as the second-highest valuation, and some...

This paper will demonstrate heuristics for solving revenue-maximizing single parameter mechanism design problems. The bidders in each environment will share the same relative valuations for goods, and their utility functions will be linear with respect to what they are allocated. We will apply these heuristics for three auctions: k-Vickrey, sponsored search, and knapsack. Formalizing all of the...

Team formation is a core problem in AI. Remarkably, little prior work has addressed the problem of mechanism design for team formation, accounting for the need to elicit agents’ preferences over potential teammates. Coalition formation in the related hedonic games has received much attention, but only from the perspective of coalition stability, with little emphasis on the mechanism design obje...

Suppose a principal cannot commit to a centralized grand-mechanism with all his privately informed agents but can only sign public bilateral contracts with each of them. The principal can manipulate what he learns by contracting with an agent when dealing with others. Introducing this possibility for manipulations may simplify significantly optimal mechanisms. It restores both the continuity of...