Sum of Us: Strategyproof Selection from the Selectors

We consider directed graphs over a set of n agents, where an edge (i,j) is taken to mean that agent i supports or trusts agent j. Given such a graph and an integer k\leq n, we wish to select a subset of k agents that maximizes the sum of indegrees, i.e., a subset of k most popular or most trusted agents. At the same time we assume that each individual agent is only interested in being selected, and may misreport its outgoing edges to this end. This problem formulation captures realistic scenarios where agents choose among themselves, which can be found in the context of Internet search, social networks like Twitter, or reputation systems like Epinions. Our goal is to design mechanisms without payments that map each graph to a k-subset of agents to be selected and satisfy the following two constraints: strategyproofness, i.e., agents cannot benefit from misreporting their outgoing edges, and approximate optimality, i.e., the sum of indegrees of the selected subset of agents is always close to optimal. Our first main result is a surprising impossibility: for k \in {1,...,n-1}, no deterministic strategyproof mechanism can provide a finite approximation ratio. Our second main result is a randomized strategyproof mechanism with an approximation ratio that is bounded from above by four for any value of k, and approaches one as k grows

Strategyproof Computing: Systems Infrastructures for Self-Interested Parties

The widespread deployment of high-speed internet access is ushering in a new era of distributed computing, in which parties both contribute to a global pool of shared resources and access the pooled resources to support their own computing needs. We argue that system designers must explicitly address the self-interest of individual parties if these next-generation computing systems are to flourish. We propose strategyproof computing, a vision for an open computing infrastructure in which resource allocation and negotiation schemes are incentive-compatible, and individual parties can treat other resources as their own. In this paper we outline key guiding principles for the vision of strategyproof computing, define the strategyproof computing paradigm, and lay out a systems-related research agenda.Engineering and Applied Science

Computational Mechanism Design: A Call to Arms

Game theory has developed powerful tools for analyzing decision making in systems with multiple autonomous actors. These tools, when tailored to computational settings, provide a foundation for building multiagent software systems. This tailoring gives rise to the field of computational mechanism design, which applies economic principles to computer systems design

PeerNomination : relaxing exactness for increased accuracy in peer selection

In peer selection agents must choose a subset of themselves for an award or a prize. As agents are self-interested, we want to design algorithms that are impartial, so that an individual agent cannot affect their own chance of being selected. This problem has broad application in resource allocation and mechanism design and has received substantial attention in the artificial intelligence literature. Here, we present a novel algorithm for impartial peer selection, PeerNomination, and provide a theoretical analysis of its accuracy. Our algorithm possesses various desirable features. In particular, it does not require an explicit partitioning of the agents, as previous algorithms in the literature. We show empirically that it achieves higher accuracy than the exiting algorithms over several metrics

On the Cost of Participating in a Peer-to-Peer Network

In this paper, we model the cost incurred by each peer participating in a peer-to-peer network. Such a cost model allows to gauge potential disincentives for peers to collaborate, and provides a measure of the ``total cost'' of a network, which is a possible benchmark to distinguish between proposals. We characterize the cost imposed on a node as a function of the experienced load and the node connectivity, and show how our model applies to a few proposed routing geometries for distributed hash tables (DHTs). We further outline a number of open questions this research has raised.Comment: 17 pages, 4 figures. Short version to be published in the Proceedings of the Third International Workshop on Peer-to-Peer Systems (IPTPS'04). San Diego, CA. February 200

Hybrid Transitive Trust Mechanisms

Establishing trust amongst agents is of central importance to the development of well-functioning multi-agent systems. For example, the anonymity of transactions on the Internet can lead to inefficiencies; e.g., a seller on eBay failing to ship a good as promised, or a user free-riding on a file-sharing network. Trust (or reputation) mechanisms can help by aggregating and sharing trust information between agents. Unfortunately these mechanisms can often be manipulated by strategic agents. Existing mechanisms are either very robust to manipulation (i.e., manipulations are not beneficial for strategic agents), or they are very informative (i.e., good at aggregating trust data), but never both. This paper explores this trade-off between these competing desiderata. First, we introduce a metric to evaluate the informativeness of existing trust mechanisms. We then show analytically that trust mechanisms can be combined to generate new hybrid mechanisms with intermediate robustness properties. We establish through simulation that hybrid mechanisms can achieve higher overall efficiency in environments with risky transactions and mixtures of agent types (some cooperative, some malicious, and some strategic) than any previously known mechanism.Engineering and Applied Science