Peer prediction aims to incentivize truthful reports from agents whose reports cannot be assessed with any objective ground truthful information. In the multi-task setting where each agent is asked multiple questions, a sequence of mechanisms have been proposed which are truthful — truth-telling is guaranteed to be an equilibrium, or even better, informed truthful — truth-telling is guaranteed to be one of the best-paid equilibria. However, these guarantees assume agents’ strategies are restricted to be task-independent: an agent’s report on a task is not affected by her information about other tasks.
We provide the first discussion on how to design (informed) truthful mechanisms for task-dependent strategies, which allows the agents to report based on all her information on the assigned tasks. We call such stronger mechanisms (informed) omni-truthful. In particular, we propose the joint-disjoint task framework, a new paradigm which builds upon the previous penalty-bonus task framework. First, we show a natural reduction from mechanisms in the penalty-bonus task framework to mechanisms in the joint-disjoint task framework that maps every truthful mechanism to an omni-truthful mechanism. Such a reduction is non-trivial as we show that current penalty-bonus task mechanisms are not, in general, omni-truthful. Second, for a stronger truthful guarantee, we design the matching agreement (MA) mechanism which is informed omni-truthful. Finally, for the MA mechanism in the detail-free setting where no prior knowledge is assumed, we show how many tasks are required to (approximately) retain the truthful guarantees.
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This content will become publicly available on March 25, 2025
Project-Fair and Truthful Mechanisms for Budget Aggregation
We study the budget aggregation problem in which a set of strategic voters must split a finite divisible resource (such as money or time) among a set of competing projects. Our goal is twofold: We seek truthful mechanisms that provide fairness guarantees to the projects. For the first objective, we focus on the class of moving phantom mechanisms, which are -- to this day -- essentially the only known truthful mechanisms in this setting. For project fairness, we consider the mean division as a fair baseline, and bound the maximum difference between the funding received by any project and this baseline. We propose a novel and simple moving phantom mechanism that provides optimal project fairness guarantees. As a corollary of our results, we show that our new mechanism minimizes the L1 distance to the mean for three projects and gives the first non-trivial bounds on this quantity for more than three projects.
more » « less- Award ID(s):
- 1928930
- PAR ID:
- 10514094
- Publisher / Repository:
- AAAI
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 38
- Issue:
- 9
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 9704 to 9712
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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