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Dengji Zhao
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Mechanism design has traditionally assumed that the set of participants are fixed and known to the mechanism (the market owner) in advance. However, in practice, the market owner can only directly reach a small number of participants (her neighbours). Hence the owner often needs costly promotions to recruit more participants in order to get desirable outcomes such as social welfare or revenue maximization. In this paper, we propose to incentivize existing participants to invite their neighbours to attract more participants. However, they would not invite each other if they are competitors. We discuss how to utilize the conflict of interest between the participants to incentivize them to invite each other to form larger markets. We will highlight the early solutions and open the floor for discussing the fundamental open questions in the settings of auctions, coalitional games, matching and voting.
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We study the problem of repeatedly auctioning off an item to one of $k$ bidders where: a) bidders have a per-round individual rationality constraint, b) bidders may leave the mechanism at any point, and c) the bidders valuations are adversarially chosen (the prior-free setting). Without these constraints, the auctioneer can run a second-price auction to sell the business and receive the second highest total value for the entire stream of items. We show that under these constraints, the auctioneer can attain a constant fraction of the sell the business benchmark, but no more than $2/e$ of this benchmark. In the course of doing so, we design mechanisms for a single bidder problem of independent interest: how should you repeatedly sell an item to a (per-round IR) buyer with adversarial valuations if you know their total value over all rounds is $V$ but not how their value changes over time? We demonstrate a mechanism that achieves revenue $V/e$ and show that this is tight.
Wireless power transfer (WPT) is a promising technology to prolong the lifetime of the sensors and communication devices, i.e., workers, in completing crowdsourcing tasks by providing continuous and cost-effective energy supplies. In this paper, we propose a wireless powered spatial crowdsourcing framework which consists of two mutually dependent phases: task allocation phase and data crowdsourcing phase. In the task allocation phase, we propose a Stackelberg game based mechanism for the spatial crowdsourcing platform to efficiently allocate spatial tasks and wireless charging power to each worker. In the data crowdsourcing phase, the workers may have an incentive to misreport its real working location to improve its utility, which causes adverse effects to the spatial crowdsourcing platform. To address this issue, we present three strategyproof deployment mechanisms for the spatial crowdsourcing platform to place a mobile base station, e.g., vehicle or robot, which is responsible for transferring the wireless power and collecting the crowdsourced data. As the benchmark, we first apply the classical median mechanism and evaluate its worst-case performance. Then, we design a conventional strategyproof deployment mechanism to improve the expected utility of the spatial crowdsourcing platform under the condition that the workers locations follow a known geographical distribution. For a more general case with only the historical location data available, we propose a deep learning based strategyproof deployment mechanism to maximize the spatial crowdsourcing platforms utility. Extensive experimental results based on synthetic and real-world datasets reveal the effectiveness of the proposed framework in allocating tasks and charging power to workers while avoiding the dishonest workers manipulation.
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 traffic information system, provides information to the players. We relate this notion to Bayes Wardrop equilibrium. Then, we provide conditions -- existence of a convex potential and complete information -- under which mediation does not improve equilibrium outcomes. We then study full implementation and, finally, information design in anonymous games with a finite set of players, when the number of players tends to infinity.
We study contests where the designers objective is an extension of the widely studied objective of maximizing the total output: The designer gets zero marginal utility from a players output if the output of the player is very low or very high. We model this using two objective functions: binary threshold, where a players contribution to the designers utility is 1 if her output is above a certain threshold, and 0 otherwise; and linear threshold, where a players contribution is linear if her output is between a lower and an upper threshold, and becomes constant below the lower and above the upper threshold. For both of these objectives, we study (1) rank-order allocation contests that use only the ranking of the players to assign prizes and (2) general contests that may use the numerical values of the players outputs to assign prizes. We characterize the optimal contests that maximize the designers objective and indicate techniques to efficiently compute them. We also prove that for the linear threshold objective, a contest that distributes the prize equally among a fixed number of top-ranked players offers a factor-2 approximation to the optimal rank-order allocation contest.
Game theory is often used as a tool to analyze decentralized systems and their properties, in particular, blockchains. In this note, we take the opposite view. We argue that blockchains can and should be used to implement economic mechanisms because they can help to overcome problems that occur if trust in the mechanism designer cannot be assumed. Mechanism design deals with the allocation of resources to agents, often by extracting private information from them. Some mechanisms are immune to early information disclosure, while others may heavily depend on it. Some mechanisms have to randomize to achieve fairness and efficiency. Both issues, information disclosure, and randomness require trust in the mechanism designer. If there is no trust, mechanisms can be manipulated. We claim that mechanisms that use randomness or sequential information disclosure are much harder, if not impossible, to audit. Therefore, centralized implementation is often not a good solution. We consider some of the most frequently used mechanisms in practice and identify circumstances under which manipulation is possible. We propose a decentralized implementation of such mechanisms, that can be, in practical terms, realized by blockchain technology. Moreover, we argue in which environments a decentralized implementation of a mechanism brings a significant advantage.
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