In this paper we design protocols for exchange of information between a sequence of markets along a single supply chain. These protocols allow each of these markets to function separately, while the information exchanged guarantees efficient global behavior across the supply chain. Each market form a link in the supply chain operates as a double auction, where the bids on one side of the double auction come from bidders in the corresponding segment of the industry, and the bids on the other side are synthetically generated by the protocol to express the combined information from all other links in the chain. The double auctions in each of the markets can be of several types, and we study several variants of incentive compatible double auctions, comparing them in terms of their efficiency and of the market revenue.

Intuitively, one might expect that a seller’s revenue from an auction weakly increases as the number of bidders grows, as this increases competition. However, it is known that for combinatorial auctions that use the VCG mechanism, a seller can sometimes increase revenue by dropping bidders. In this paper we investigate the extent to which this problem can occur under other dominant-strategy combinatorial auction mechanisms. Our main result is that such failures of “revenue monotonicity ” are not limited to mechanisms that achieve efficient allocations. Instead, they can occur under any dominant-strategy direct mechanism that sets prices using critical values, and that always chooses an allocation that cannot be augmented to make some bidder better off, while making none worse off.

Market-driven dynamic spectrum auctions can drastically improve the spectrum availability for wireless networks struggling to obtain additional spectrum. However, they face significant challenges due to the fear of market manipulation. A truthful or strategy-proof spectrum auction eliminates the fear by enforcing players to bid their true valuations of the spectrum. Hence bidders can avoid the expensive overhead of strategizing over others and the auctioneer can maximize its revenue by assigning spectrum to bidders who value it the most. Conventional truthful designs, however, either fail or become computationally intractable when applied to spectrum auctions. In this paper, we propose VERITAS, a truthful and computationally-efficient spectrum auction to support an eBay-like dynamic spectrum market. VERITAS makes an important contribution of maintaining truthfulness while maximizing spectrum utilization. We show analytically that VERITAS is truthful, efficient, and has a polynomial complexity of O(n 3 k) when n bidders compete for k spectrum bands. Simulation results show that VERITAS outperforms the extensions of conventional truthful designs by up to 200 % in spectrum utilization. Finally, VERITAS supports diverse bidding formats and enables the auctioneer to reconfigure allocations for multiple market objectives.

Collective choice settings are the heart of society. Game theory provides a basis for engineering the incentives into the interaction mechanism (e.g., rules of an election or auction) so that a desirable system-wide outcome (e.g., president, resource allocation, or task allocation) is chosen even though every agent acts based on self-interest. However, there are a host of computer science issues not traditionally addressed in game theory that have to be addressed in order to make mechanisms work in the real world. Those computing, communication, and privacy issues are deeply intertwined with the economic incentive issues. For example, the fact that agents have limited computational capabilities to determine their own (and others') preferences ruins the incentive properties of established auction mechanisms, and gives rise to new issues. On the positive side, computational complexity can be used as a barrier to strategic behavior in settings where economic mechanism design falls short. Novel computational approaches also enable new economic institutions. For example, market clearing technology with specialized search algorithms is enabling a form of interaction that I call expressive competition. As another example, selective incremental preference elicitation can determine the optimal outcome while requiring the agents to determine and reveal only a small portion of their preferences. Furthermore, automated mechanism design can yield better mechanisms than the best known to date.

The problem of allocating discrete computational resources motivates interest in general multi-unit combinatorial exchanges. This paper considers the problem of computing optimal (surplus-maximizing) allocations, assuming unrestricted quasi-linear preferences. We present a solver whose pseudo-polynomial time and memory requirements are linear in three of four natural measures of problem size: number of agents, length of bids, and units of each resource. In applications where the number of resource types is inherently a small constant, e.g., computational resource allocation, such a solver offers advantages over more elaborate approaches developed for high-dimensional problems.

We propose novel solutions for unicast routing in wireless networks consisted of selfish terminals: in order to alleviate the inevitable over-payment problem (and thus economic inefficiency) of the VCG (Vickrey-Clark-Groves) mechanism, we design a mechanism that results in Nash equilibria rather than the traditional strategyproofness (using weakly dominant strategy). In addition, we systematically study the unicast routing system in which both the relay terminals and the service requestor (either the source or the destination nodes or both) could be selfish. To the best of our knowledge, this is the first paper that presents social efficient unicast routing systems with proved performance guarantee. Thus, we call the proposed systems: Optimal Unicast Routing Systems (OURS). Our main contributions of OURS are as follows. (1) For the principal model where the service requestor is not selfish, we propose a

In the digital goods setting we prove that for any randomized auction which is truthful in expectation, there exists an equivalent randomized auction which randomizes over truthful deterministic auctions. By equivalent auctions we mean auctions in which the probability of winning and the expected price offered are the same for all bidders for all bid values. We also prove an approximate equivalence proof in the case where the bids come from a discrete space. Finally, we consider the computational issue of finding an efficient equivalent auction.

Spectrum is a critical yet scarce resource and it has been shown that dynamic spectrum access can significantly improve spectrum utilization. To achieve this, it is important to incentivize the primary license holders to open up their under-utilized spectrum for sharing. In this paper we present a secondary spectrum market where a primary license holder can sell access to its unused or under-used spectrum resources in the form of certain fine-grained spectrumspace-time unit. Secondary wireless service providers can purchase such contracts to deploy new service, enhance their existing service, or deploy ad hoc service to meet flash crowds demand. Within the context of this market, we investigate how to use auction mechanisms to allocate and price spectrum resources so that the primary license holder’s revenue is maximized. We begin by classifying a number of alternative auction formats in terms of spectrum demand. We then study a specific auction format where secondary wireless service providers have demands for fixed locations (cells). We propose an optimal auction based on the concept of virtual valuation. Assuming the knowledge of valuation distributions, the optimal auction uses the Vickrey-Clarke-Groves (VCG) mechanism to maximize the expected revenue while enforcing truthfulness. To reduce the computational complexity, we further design a truthful suboptimal auction with polynomial time complexity. It uses a monotone allocation and critical value payment to enforce truthfulness. Simulation results show that this suboptimal auction can generate stable expected revenue.

The VCG mechanism is the canonical method for motivating bidders in combinatorial auctions and exchanges to bid truthfully. We study two related problems concerning the VCG mechanism: the problem of revenue guarantees, and that of collusion. The existence of these problems even in one-item settings is well-known; in this paper, we lay out their full extent in multi-item settings. We study four settings: combinatorial forward auctions with free disposal, combinatorial reverse auctions with free disposal, combinatorial forward (or reverse) auctions without free disposal, and combinatorial exchanges. In each setting, we give an example of how additional bidders (colluders) can make the outcome much worse (less revenue or higher cost) under the VCG mechanism (but not under a first price mechanism); derive necessary and sufficient conditions for such an effective collusion to be possible under the VCG mechanism; and (when nontrivial) study the computational complexity of deciding whether these conditions hold.

In recent years, certain formalizations of combinatorial negotiation settings, most notably combinatorial auctions, have become an important research topic in the AI community. A pervasive assumption has been that of no externalities: the agents deciding on a variable (such as whether a trade takes place between them) are the only ones affected by how this variable is set. To date, there has been no widely studied formalization of combinatorial negotiation settings with externalities.