Over the last 5 years, the AI community has shown considerable interest in decentralized control of multiple decision makers or "agents" under uncertainty. This problem arises in many application domains, such as multi-robot coordination, manufacturing, information gathering, and load balancing. Such problems must be treated as decentralized decision problems because each agent may have different partial information about the other agents and about the state of the world. It has been shown that these problems are significantly harder than their centralized counterparts, requiring new formal models and algorithms to be developed. Rapid progress in recent years has produced a number of different frameworks, complexity results, and planning algorithms. The objectives of this paper are to provide a comprehensive overview of these results, to compare and contrast the existing frameworks, and to provide a deeper understanding of their relationships with one another, their strengths, and their weaknesses. While we focus on cooperative systems, we do point out important connections with game-theoretic approaches. We analyze five different formal frameworks, three different optimal algorithms, as well as a series of approximation techniques. The paper provides interesting insights into the structure of decentralized problems, the expressiveness of the various models, and the relative advantages and limitations of the different solution techniques. A better understanding of these issues will facilitate further progress in the field and help resolve several open problems that we identify.

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We study financial networks and reveal a new kind of systemic risk arising from what we call default ambiguity-that is, a situation where it is impossible to decide which banks are in default. Specifically, we study the clearing problem: given a network of banks interconnected by financial contracts, determine which banks are in default and what percentage of their liabilities they can pay. Prior work has shown that when banks can only enter into debt contracts with each other, this problem always has a unique maximal solution. We first prove that when banks can also enter into credit default swaps (CDSs), the clearing problem may have no solution or multiple conflicting solutions, thus leading to default ambiguity. We then derive sufficient conditions on the network structure to eliminate these issues. Finally, we discuss policy implications for the CDS market.

Combinatorial auctions (CAs) are used to allocate multiple items among bidders with complex valuations. Since the value space grows exponentially in the number of items, it is impossible for bidders to report their full value function even in medium-sized settings. Prior work has shown that current designs often fail to elicit the most relevant values of the bidders, thus leading to inefficiencies. We address this problem by introducing a machine learning-based elicitation algorithm to identify which values to query from the bidders. Based on this elicitation paradigm we design a new CA mechanism we call PVM, where payments are determined so that bidders’ incentives are aligned with allocative efficiency. We validate PVM experimentally in several spectrum auction domains, and we show that it achieves high allocative efficiency even when only few values are elicited from the bidders.

Cloud computing providers must constantly hold many idle compute instances available (e.g., for maintenance or for users with long-term contracts). A natural idea, which should intuitively increase the provider’s profit, is to sell these idle instances on a secondary market, for example, via a preemptible spot market. However, this ignores possible “market cannibalization” effects that may occur in equilibrium as well as the additional costs the provider experiences due to preemptions. To study the viability of offering a spot market, we model the provider’s profit optimization problem by combining queuing theory and game theory to analyze the equilibria of the resulting queuing system. Our main result is an easy-to-check condition under which a provider can simultaneously achieve a profit increase and create a Pareto improvement for the users by offering a spot market (using idle resources) alongside a fixed-price market. Finally, we illustrate our results numerically to demonstrate the effects that the provider’s costs and her strategy have on her profit. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.