In real life auctions, a widely observed phenomenon is the winner's curse -- the winner's high bid implies that the winner often over-estimates the value of the good for sale, resulting in an incurred negative utility. The seminal work of Eyster and Rabin [Econometrica'05] introduced a behavioral model aimed to explain this observed anomaly. We term agents who display this bias "cursed agents". We adopt their model in the interdependent value setting, and aim to devise mechanisms that prevent the cursed agents from obtaining negative utility. We design mechanisms that are cursed ex-post IC, that is, incentivize agents to bid their true signal even though they are cursed, while ensuring that the outcome is individually rational -- the price the agents pay is no more than the agents' true value. Since the agents might over-estimate the good's value, such mechanisms might require the seller to make positive transfers to the agents to prevent agents from over-paying. For revenue maximization, we give the optimal deterministic and anonymous mechanism. For welfare maximization, we require ex-post budget balance (EPBB), as positive transfers might lead to negative revenue. We propose a masking operation that takes any deterministic mechanism, and imposes that the seller would not make positive transfers, enforcing EPBB. We show that in typical settings, EPBB implies that the mechanism cannot make any positive transfers, implying that applying the masking operation on the fully efficient mechanism results in a socially optimal EPBB mechanism. This further implies that if the valuation function is the maximum of agents' signals, the optimal EPBB mechanism obtains zero welfare. In contrast, we show that for sum-concave valuations, which include weighted-sum valuations and l_p-norms, the welfare optimal EPBB mechanism obtains half of the optimal welfare as the number of agents grows large.

Crowdsourcing enables the solicitation of forecasts on a variety of prediction tasks from distributed groups of people. How to aggregate the solicited forecasts, which may vary in quality, into an accurate final prediction remains a challenging yet critical question. Studies have found that weighing expert forecasts more in aggregation can improve the accuracy of the aggregated prediction. However, this approach usually requires access to the historical performance data of the forecasters, which are often not available. In this paper, we study the problem of aggregating forecasts without having historical performance data. We propose using peer prediction methods, a family of mechanisms initially designed to truthfully elicit private information in the absence of ground truth verification, to assess the expertise of forecasters, and then using this assessment to improve forecast aggregation. We evaluate our peer-prediction-aided aggregators on a diverse collection of 14 human forecast datasets. Compared with a variety of existing aggregators, our aggregators achieve a significant and consistent improvement on aggregation accuracy measured by the Brier score and the log score. Our results reveal the effectiveness of identifying experts to improve aggregation even without historical data.

Strictly proper scoring rules (SPSR) are incentive compatible for eliciting information about random variables from strategic agents when the principal can reward agents after the realization of the random variables. They also quantify the quality of elicited information, with more accurate predictions receiving higher scores in expectation. In this paper, we extend such scoring rules to settings where a principal elicits private probabilistic beliefs but only has access to agents' reports. We name our solution Surrogate Scoring Rules (SSR). SSR build on a bias correction step and an error rate estimation procedure for a reference answer defined using agents' reports. We show that, with a single bit of information about the prior distribution of the random variables, SSR in a multi-task setting recover SPSR in expectation, as if having access to the ground truth. Therefore, a salient feature of SSR is that they quantify the quality of information despite the lack of ground truth, just as SPSR do for the setting with ground truth. As a by-product, SSR induce dominant truthfulness in reporting. Our method is verified both theoretically and empirically using data collected from real human forecasters.

Wagering mechanisms are one-shot betting mechanisms that elicit agents’ predictions of an event. For deterministic wagering mechanisms, an existing impossibility result has shown incompatibility of some desirable theoretical properties. In particular, Pareto optimality (no profitable side bet before allocation) can not be achieved together with weak incentive compatibility, weak budget balance and individual rationality. In this paper, we expand the design space of wagering mechanisms to allow randomization and ask whether there are randomized wagering mechanisms that can achieve all previously considered desirable properties, including Pareto optimality. We answer this question positively with two classes of randomized wagering mechanisms: i) one simple randomized lottery-type implementation of existing deterministic wagering mechanisms, and ii) another family of randomized wagering mechanisms, named surrogate wagering mechanisms, which are robust to noisy ground truth. Surrogate wagering mechanisms are inspired by an idea of learning with noisy labels (Natarajan et al. 2013) as well as a recent extension of this idea to the information elicitation without verification setting (Liu and Chen 2018). We show that a broad set of randomized wagering mechanisms satisfy all desirable theoretical properties.

The fast development of mobile networks calls for the massive consumption of materials, land, and energy in building and maintaining infrastructures, which is always intensified by the repetitive constructions of competing network operators. To reduce the resource consumption for social benefit, one business solution, implemented in the Chinese telecom industry, is forming a joint venture responsible for building and maintaining common infrastructures. The novelty of this practice is that the joint venture is shared by the competing operators who also rent infrastructures from the joint venture. We note that such a solution can be potentially generalized to other industries for reducing resource consumption. However, before generalization, an understanding of the pros and cons from the economic perspective of the business model is urgently needed. In this paper, we study this business model from a game theoretic approach. Our results show that if we properly regulate the joint venture, the market can converge to equilibriums with desirable properties which cannot be achieved without the joint venture. Furthermore, we also study the investment reduction in the presence of the joint venture. Our numerical results show that under a moderate user density, the total investment on the infrastructures can be significantly reduced.

As a major driving force for adopting cloud computing, continuous cost reduction has been constantly pursued by cloud users. For a group of users with heterogeneous cloud resource demands, it may be possible for them to buy resources in a collaborative way in order to save the purchase cost, which is known as group-buying in business. While group-buying can benefit cloud users in principle, the question is how to design an implementation scheme to support group-buying on the cloud market. In this paper, we address the question by studying a coalition formation game, aiming to design a way under which the users can form stable coalitions for group-buying. It turns out that group-buying on the cloud market is challenging in that most popular solution concepts may fail to constitute stable coalitions. In order to sustain group-buying for cloud services, we propose a new solution concept, contractually group stable, which is an extension of an existing concept in the literature. We show that this new solution concept can guarantee the existence of stable coalitions, making group-buying always possible on the cloud market. We also develop computing algorithms for solving the coalition formation game under our concept. Computational experiments show that our concept can bring in substantial cost reduction for cloud users.

Cooperative communication is a new fashion to alleviate the low channel utilization and signal fading problems in today's wireless network. The success of cooperative communication heavily depends on the efficient assignment of relay resource. Auction theory has been applied successfully to allocate limited resources in wireless network for decades. However, most of the existing auction mechanisms restricted buyers to use simple bidding language, which greatly lowers the social welfare and relay assignment efficiency. In this paper, we model the relay assignment as a combinatorial auction with flexible bidding language and propose SAIL, which is a Strategy-proof and Approximately effIcient combinatoriaL auction for relay assignment in cooperative communication. We show analytically that SAIL is strategy-proof and achieves approximate efficient social welfare. Furthermore, we present evaluation results to show that SAIL achieves a good system performance in terms of social welfare, buyer satisfaction and relay utilization.