Distributed games: From mechanisms to protocols

ACM Transactions on Economics and Computation, 2013

There is only one technique for prior-free optimal mechanism design that generalizes beyond the structurally benevolent setting of digital goods. This technique uses random sampling to estimate the distribution of agent values and then employs the Bayesian optimal mechanism for this estimated distribution on the remaining players. Though quite general, even for digital goods, this random sampling auction has a complicated analysis and is known to be suboptimal. To overcome these issues we generalize the consensus and profit extraction techniques from Goldberg and Hartline [2003] to structurally rich environments that include, for example, single-minded combinatorial auctions.

1982

Handbook of Applied Algorithms, 2008

Most of the existing and foreseen complex networks, such as the Internet, are operated and built by thousands of large and small entities (autonomous agents), which collaborate to process and deliver end-to-end flows originating from and terminating at any of them. The distributed nature of the Internet implies a lack of coordination among its users. Instead, each user attempts to obtain maximum performance according to his own parameters and objectives.

2000

The purpose of this paper is to analyze a collective decision making problem in an open, dynamic environment, such as the Internet. More specifically, we study a class of mechanism design problems where the designer of a mechanism cannot completely identify the participants (agents) of the mechanism. A typical example of such a situation is Internet auctions. The main contributions of this paper are as follows.

NajEcon Working Paper Reviews, 2007

We investigate the consequences of assuming private communication between the principal and each of his agents in an otherwise standard mechanism design setting. Doing so simplifies optimal mechanisms and institutions. It restores both the continuity of the principal's and the agents' payoffs and that of the optimal mechanism with respect to the information structure. Nevertheless, it still maintains the useful role of correlation to better extract the agents' information rent. We first prove a Revelation Principle with private communication that characterizes the set of allocations implementable under private communication by means of simple non-manipulability constraints. We also demonstrate a Taxation Principle which helps drawing some links between private communication and limited commitment on the principal's side. Equipped with those tools, we derive optimal non-manipulable mechanisms in various environments (separable projects, multi-unit auctions, team productions).

2003

Distributed systems are characterized by having partial observability of the global state during execution. Nevertheless, when these systems comprise cooperative agents, they should attain global objectives. Planning for these decentralized systems is a very complex task. Exchange of local information through communication can alleviate this complexity by allowing the agents to be synchronized from time to time. Due to costs associated with real-world communication, agents may not be able to continuously obtain full observability of the system. We examine mechanisms that result in the decomposition of the global problem into local simpler problems that are applied each time the agents exchange information. The communication policies are computed with respect to a given mechanism and policy of action. This paper presents a framework to study these mechanisms and evaluation criteria to compare them. We also review related work on mechanism design and compare the approaches.

Developing interaction rules/protocols among multiple agents is one of the central research topics in multi-agent systems. For cooperative agents, we need to develop protocols so that agents can achieve some common goal if they follow the protocol. Also, for competitive/selfish agents, we need to design mechanisms/protocols so that some socially desirable outcome can be achieved, even if agents act selfishly. This article presents a brief overview of the authorýs works on this topic over the last five years.

2011

We consider multiple-principal multiple-agent games of incomplete information. In this context, we show that restricting principals to propose truth-telling direct mechanisms is problematic: the best reply of a single principal to a given array of offers proposed by his rivals cannot always be represented through truth-telling mechanisms. We then show that if contracts are exclusive, truth-telling direct mechanisms are able to characterize best replies. This provides a rationale for the use of truth-telling direct mechanisms in the situation in which each agent can contract with at most one principal, that has been postulated in most economic applications.

IISc Lecture Notes Series, 2014

2007

We present a functional framework for automated mechanism design based on a two-stage game model of strategic interaction between the designer and the mechanism participants, and apply it to several classes of two-player infinite games of incomplete information. Our approach yields optimal or nearly optimal mechanisms in two application domains using various objective functions. By comparing our results with known optimal mechanisms, and in some cases improving on the best known mechanisms, we show that ours is a promising approach to parametric design of indirect mechanisms. 1 As a simple example, imagine that the designer's only choice is a first-price sealed-bid auction. Since this auction is not truthful, the revelation principle clearly fails in this restricted design space. Abstractly and generally, we can simply imagine eliminating truthful mechanisms from the design space.

2011

This article studies how a mechanism designer can influence games by promising pay-20 ments to the players depending on their mutual choice of strategies. First, we investigate 21 the cost of implementing a desirable behavior and present algorithms to compute this 22 cost. Whereas a mechanism designer can decide efficiently whether strategy profiles can 23 be implemented at no cost at all our complexity analysis indicates that computing an 24 optimal implementation is generally NP-hard. Second, we introduce and analyze the 25 concept of leverage in a game. The leverage captures the benefits that a benevolent or 26 a malicious mechanism designer can achieve by implementing a certain strategy profile 27 region within economic reason, i.e., by taking the implementation cost into account. 28 Mechanism designers can often manipulate games and change the social welfare by a 29 larger extent than the amount of money invested. Unfortunately, computing the lever-30 age turns out to be intractable as well in the general case. 31

Proceedings of the 30th annual ACM SIGACT-SIGOPS symposium on Principles of distributed computing - PODC '11, 2011

Under many distributed protocols, the prescribed behavior for participants is to behave greedily, that is, to repeatedly "best respond" to the others' actions. We ask the following general question: "When is it in the best interest of a long-sighted participant to adhere to a distributed greedy protocol? ". We take a game-theoretic approach and exhibit a class of games where greedy behavior (i.e., repeated best-response) is incentive compatible for all players. We identify several environments of interest that fall within this class, thus establishing the incentive compatibility of the natural distributed greedy protocol for each. These environments include models of the Border Gateway Protocol (BGP) , that handles routing on the Internet, and of the Transmission Control Protocol (TCP) , and also stable-roommates assignments [2] and cost-sharing , that have been extensively studied in economic theory.

2007

We describe here a structured system for distributed mechanism design appropriate for both Intranet and Internet applications. In our approach the players dynamically form a network in which they know neither their neighbours nor the size of the network and interact to jointly take decisions. The only assumption concerning the underlying communication layer is that for each pair of processes there is a path of neighbours connecting them. This allows us to deal with arbitrary network topologies. We also discuss the implementation of this system which consists of a sequence of layers. The lower layers deal with the operations that implement the basic primitives of distributed computing, namely low level communication and distributed termination, while the upper layers use these primitives to implement high level communication among players, including broadcasting and multicasting, and distributed decision making. This yields a highly flexible distributed system whose specific applications are realized as instances of its top layer. This design is implemented in Java. The system supports at various levels fault-tolerance and includes a provision for distributed policing the purpose of which is to exclude 'dishonest' players. Also, it can be used for repeated creation of dynamically formed networks of players interested in a joint decision making implemented by means of a tax-based mechanism. We illustrate its flexibility by discussing a number of implemented examples. 1

In many real-world settings, strategic agents are instructed to follow best-reply dynamics. Indeed, many computational protocols are based on such repeated greedy interactions. Such settings give rise to a natural question, that has received very little attention: Is it in the best interest of the strategic agents to follow best-reply dynamics? That is, is it true that a player cannot improve his final outcome by not behaving myopically? Surprisingly, we find that in many interesting cases the answer to this question is Yes. This enables us to design incentive-compatible algorithms for many environments, both old and new. We initiate the study of best-reply dynamics in the context of mechanism design. We first describe a structural property of games that implies that best-reply dynamics not only converge to a pure Nash equilibrium, but are also incentive-compatible. We prove that all the follow-ing examples have this helpful structure: Internet protocols (TCP-inspired congestion-con...

IEEE Transactions on Communications, 2011

This paper discusses a special type of multiuser communication scenario, in which users' utilities are linearly impacted by their competitors' actions. First, we explicitly characterize the Nash equilibrium and Pareto boundary of the achievable utility region. Second, the price of anarchy incurred by the non-collaborative Nash strategy is quantified. Third, to improve the performance in the non-cooperative scenarios, we investigate the properties of an alternative solution concept named conjectural equilibrium, in which individual users compensate for their lack of information by forming internal beliefs about their competitors. The global convergence of the best response and Jacobi update dynamics that achieve various conjectural equilibria are analyzed. It is shown that the Pareto boundaries of the investigated linearly coupled games can be sustained as stable conjectural equilibria if the belief functions are properly initialized. The investigated models apply to a variety of realistic applications encountered in the multiple access design, including wireless random access and flow control. Index Terms Nash equilibrium, Pareto-optimality, conjectural equilibrium, non-cooperative games. I. INTRODUCTION Game theory provides a formal framework for studying the interactions of strategic agents. Recently, there has been a surge in research activities that employ game theory to model and analyze a wide range of application scenarios in modern communication networks [1]-[4]. In communication networks, any action taken by a single user usually affects the utilities of the other users sharing the same resources. Depending on the characteristics of different applications, numerous game-theoretical models and solution concepts have been proposed to describe the multiuser interactions and optimize the users' decisions in communication networks. Roughly speaking, the existing multiuser research can be categorized into two types, non-cooperative games and cooperative games. Various game theoretic solutions were developed 2 to characterize the resulting performance of the multiuser interaction, including the Nash Equilibrium (NE) and the Pareto-optimality [18]. Non-cooperative approaches generally assume that the participating users simply choose actions to selfishly maximize their individual utility functions. It is well-known that if devices operate in a noncooperative manner, this will generally limit their performance as well as that of the whole system, because the available resources are not always efficiently exploited due to the conflicts of interest occurring among users [5]. Most non-cooperative approaches are devoted to investigating the existence and properties of the NE. In particular, several non-cooperative game models, such as S-modular games, congestion games, and potential games, have been extensively applied in various communication scenarios [6]-[9]. The price of anarchy, a measure of how good the system performance is when users play selfishly and reach the NE instead of playing to achieve the social optimum, has also been addressed in several communication network applications [10] [11]. On the other hand, cooperative approaches in communication theory usually focus on studying how users can jointly improve their performance when they cooperate. For example, the users may optimize a common objective function, which represents the Pareto-optimal social welfare allocation rule based on which the system-wide resource allocation is performed [12] [13]. A profile of actions is Pareto-optimal if there is no other profile of actions that makes every player at least as well off and at least one player strictly better off. Allocation rules, e.g. network utility maximization, can provide reasonable allocation outcomes by considering the trade-off between fairness and efficiency. Most cooperative approaches focus on studying how to efficiently find the optimum joint policy. It is worth mentioning that information exchanges among users is generally required to enable users to coordinate in order to achieve and sustain Pareto-efficient outcomes. In this paper, we present a game model for a particular type of non-cooperative multiuser communication scenario. We name it linearly coupled communication games, because users' utilities are linearly impacted by their competitors' actions. In particular, the main contributions of this paper are as follows. First, based on the assumptions that we make about the properties of users' utility, we characterize the inherent structures of the utility functions for the linearly coupled games. Furthermore, based on the derived utility forms, we explicitly quantify the NE and Pareto boundary for the linearly coupled communication games. The price of anarchy incurred by the selfish users playing the Nash strategy is quantified. In addition, to improve the performance in the non-cooperative scenarios, we investigate an alternative solution: conjectural equilibrium (CE). Using this approach, individual users are modeled as belief-forming agents that develop internal beliefs about their competitors and behave optimally with