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1. On the hardness of dominant strategy mechanism design

   https://doi.org/10.1145/3519935.3520013  

   Dobzinski, Shahar; Ron, Shiri; Vondrák, Jan (June 2022, STOC 2022: Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing)

   Stefano Leonardi (Ed.)

   We study the communication complexity of dominant strategy implementations of combinatorial auctions. We start with two domains that are generally considered “easy”: multi-unit auctions with decreasing marginal values and combinatorial auctions with gross substitutes valuations. For both domains we have fast algorithms that find the welfare-maximizing allocation with communication complexity that is poly-logarithmic in the input size. This immediately implies that welfare maximization can be achieved in ex-post equilibrium with no significant communication cost, by using VCG payments. In contrast, we show that in both domains the communication complexity of any dominant strategy implementation that achieves the optimal welfare is polynomial in the input size. We then move on to studying the approximation ratios achievable by dominant strategy mechanisms. For multi-unit auctions with decreasing marginal values, we provide a dominant-strategy communication FPTAS. For combinatorial auctions with general valuations, we show that there is no dominant strategy mechanism that achieves an approximation ratio better than m1−є that uses poly(m,n) bits of communication, where m is the number of items and n is the number of bidders. In contrast, a randomized dominant strategy mechanism that achieves an O(√m) approximation with poly(m,n) communication is known. This proves the first gap between computationally efficient deterministic dominant strategy mechanisms and randomized ones. En route, we answer an open question on the communication cost of implementing dominant strategy mechanisms for more than two players, and also solve some open problems in the area of simultaneous combinatorial auctions. 

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2. Walrasian Equilibrium-Based Pricing Mechanism for Health-Data Crowdsensing Under Information Asymmetry

   https://doi.org/10.1109/TCSS.2022.3171566  

   Guo, Xinxin; Kong, Nan; Wang, Haiyan (May 2022, IEEE Transactions on Computational Social Systems)

   While prior studies have designed incentive mechanisms to attract the public to share their collected data, they tend to ignore information asymmetry between data requesters and collectors. In reality, the sensing costs information (time cost, battery drainage, bandwidth occupation of mobile devices, and so on) is the private information of collectors, which is unknown by the data requester. In this article, we model the strategic interactions between health-data requester and collectors using a bilevel optimization model. Considering that the crowdsensing market is open and the participants are equal, we propose a Walrasian equilibrium-based pricing mechanism to coordinate the interest conflicts between health-data requesters and collectors. Specifically, based on the exchange economic theory, we transform the bilevel optimization problem into a social welfare maximization problem with the constraint condition that the balance between supply and demand, and dual decomposition is then employed to divide the social welfare maximization problem into a set of subproblems that can be solved by health-data requesters and collectors. We prove that the optimal task price is equal to the marginal utility generated by the collector's health data. To avoid obtaining the collector's private information, a distributed iterative algorithm is then designed to obtain the optimal task pricing strategy. Furthermore, we conduct computational experiments to evaluate the performance of the proposed pricing mechanism and analyze the effects of intrinsic rewards, sensing costs on optimal task prices, and collectors' health-data supplies. 

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3. Optimal Signaling Mechanisms in Unobservable Queues with Strategic Customers

   https://doi.org/10.1145/3033274.3085135  

   Lingenbrink, David; Iyer, Krishnamurthy (June 2017, Proceedings of the 2017 ACM Conference on Economics and Computation (EC))

   We study the problem of optimal information sharing in the context of a service system. In particular, we consider an unobservable single server queue offering a service at a fixed price to a Poisson arrival of delay-sensitive customers. The service provider can observe the queue, and may share information about the state of the queue with each arriving customer. The customers are Bayesian and strategic, and incorporate any information provided by the service provider into their prior beliefs about the queue length before making the decision whether to join the queue or leave without obtaining service. We pose the following question: which signaling mechanism and what price should the service provider select to maximize her revenue? We formulate this problem as an instance of Bayesian persuasion in dynamic settings. The underlying dynamics make the problem more difficult because, in contrast to static settings, the signaling mechanism adopted by the service provider affects the customers' prior beliefs about the queue (given by the steady state distribution of the queue length in equilibrium). The core contribution of this work is in characterizing the structure of the optimal signaling mechanism. We summarize our main results as follows. (1) Structural characterization: Using a revelation-principle style argument, we find that it suffices to consider signaling mechanisms where the service provider sends a binary signal of "join" or "leave", and under which the equilibrium strategy of a customer is to follow the service provider's recommended action. (2) Optimality of threshold policies: For a given fixed price for service, we use the structural characterization to show that the optimal signaling mechanism can be obtained as a solution to a linear program with a countable number of variables and constraints. Under some mild technical conditions on the waiting costs, we establish that there exists an optimal signaling mechanism with a threshold structure, where service provider sends the "join" signal if the queue length is below a threshold, and "leave" otherwise. (In addition, at the threshold, the service provider randomizes.) For the special case of linear waiting costs, we derive an analytical expression for the optimal threshold i terms of the two branches of the Lambert-W function. (3) Revenue comparison: Finally, we show that with the optimal choice of the fixed price and using the corresponding optimal signaling mechanism, the service provider can achieve the same revenue as with the optimal state-dependent pricing mechanism in a fully-observable queue. This implies that in settings where state-dependent pricing is not feasible, the service provider can effectively use optimal signaling (with the optimal fixed price) to achieve the same revenue. 

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4. Strategyproof Matching of Roommates and Rooms

   https://doi.org/10.1609/aaai.v39i13.33523  

   Hosseini, Hadi; Narang, Shivika; Roy, Sanjukta (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   We initiate the study of matching roommates and rooms wherein the preferences of agents over other agents and rooms are complementary and represented by Leontief utilities. In this setting, 2n agents must be paired up and assigned to n rooms. Each agent has cardinal valuations over the rooms as well as compatibility values over all other agents. Under Leontief preferences, an agent’s utility for a matching is the minimum of the two values. We focus on the tradeoff between maximizing utilitarian social welfare and strategyproofness. Our main result shows that—in a stark contrast to the additive case— under binary Leontief utilities, there exist strategyproof mechanisms that maximize the social welfare. We further devise a strategyproof mechanism that implements such a welfare maximizing algorithm and is parameterized by the number of agents. Along the way, we highlight several possibility and impossibility results, and give upper bounds and lower bounds for welfare with or without strategyproofness. 

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5. Information Design for Multiple Interdependent Defenders: Work Less, Pay Off More

   https://doi.org/10.3390/g14010012  

   Zhou, Chenghan; Spivey, Andrew; Xu, Haifeng; Nguyen, Thanh H (February 2023, Games)

   This paper studies the problem of information design in a general security game setting in which multiple self-interested defenders attempt to provide protection simultaneously for the same set of important targets against an unknown attacker. A principal, who can be one of the defenders, has access to certain private information (i.e., attacker type), whereas other defenders do not. We investigate the question of how that principal, with additional private information, can influence the decisions of the defenders by partially and strategically revealing her information. In particular, we develop a polynomial time ellipsoid algorithm to compute an optimal private signaling scheme. Our key finding is that the separation oracle in the ellipsoid approach can be carefully reduced to bipartite matching. Furthermore, we introduce a compact representation of any ex ante persuasive signaling schemes by exploiting intrinsic security resource allocation structures, enabling us to compute an optimal scheme significantly faster. Our experiment results show that by strategically revealing private information, the principal can significantly enhance the protection effectiveness for the targets. 

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