Mechanism design for distributed task and resource allocation among self-interested agents in virtual organizations

The aggregate power of all resources on the Internet is enormous. The Internet can be viewed as a massive virtual organization that holds tremendous amounts of information and resources with different ownerships. However, little is known about how to run this organization efficiently. This dissertation studies the problems of distributed task and resource allocation among self-interested agents in virtual organizations. The developed solutions are not allocation mechanisms that can be imposed by a centralized designer, but decentralized interaction mechanisms that provide incentives to self-interested agents to behave cooperatively. These mechanisms also take computational tractability into consideration due to the inherent complexity of distributed task and resource allocation problems. Targeted allocation mechanisms can achieve global task allocation efficiency in a virtual organization and establish stable resource-sharing communities based on agentsâÃÂàown decisions about whether or not to behave cooperatively. This high level goal requires solving the following problems: synthetic task allocation, decentralized coalition formation and automated multiparty negotiation. For synthetic task allocation, in which each task needs to be accomplished by a virtual team composed of self-interested agents from different real organizations, my approach is to formalize the synthetic task allocation problem as an algorithmic mechanism design optimization problem. I have developed two approximation mechanisms that I prove are incentive compatible for a synthetic task allocation problem. This dissertation also develops a decentralized coalition formation mechanism, which is based on explicit negotiation among self-interested agents. Each agent makes its own decisions about whether or not to join a candidate coalition. The resulting coalitions are stable in the core in terms of coalition rationality. I have applied this mechanism to form resource sharing coalitions in computational grids and buyer coalitions in electronic markets. The developed negotiation mechanism in the decentralized coalition formation mechanism realizes automated multilateral negotiation among self-interested agents who have symmetric authority (i.e., no mediator exists and agents are peers). In combination, the decentralized allocation mechanisms presented in this dissertation lay a foundation for realizing automated resource management in open and scalable virtual organizations

Mechanism Design for Team Formation

Team formation is a core problem in AI. Remarkably, little prior work has addressed the problem of mechanism design for team formation, accounting for the need to elicit agents' preferences over potential teammates. Coalition formation in the related hedonic games has received much attention, but only from the perspective of coalition stability, with little emphasis on the mechanism design objectives of true preference elicitation, social welfare, and equity. We present the first formal mechanism design framework for team formation, building on recent combinatorial matching market design literature. We exhibit four mechanisms for this problem, two novel, two simple extensions of known mechanisms from other domains. Two of these (one new, one known) have desirable theoretical properties. However, we use extensive experiments to show our second novel mechanism, despite having no theoretical guarantees, empirically achieves good incentive compatibility, welfare, and fairness.Comment: 12 page

Value Creation through Co-Opetition in Service Networks

Well-defined interfaces and standardization allow for the composition of single Web services into value-added complex services. Such complex Web Services are increasingly traded via agile marketplaces, facilitating flexible recombination of service modules to meet heterogeneous customer demands. In order to coordinate participants, this work introduces a mechanism design approach - the co-opetition mechanism - that is tailored to requirements imposed by a networked and co-opetitive environment

A Framework to Support Coalition Formation in Supply Chain Collaboration

This paper proposes a framework for agents in globally collaborative supply chain by applying the concept of coalition formation, a cooperation game in game theory. This framework provides mutual benefits to every party involved buyers, sellers and logistics providers. It provides a common gateway that allows individual parties to locate the right partners, negotiate with them, and form coalition in the best possible ways. The framework is applicable to real world e-business models, including B2C, B2B, supply chain and logistics, SME, etc. We firstly discuss common needs existing in today e-business. We then discuss about our framework, i.e., negotiation protocol and decision mechanism

Coordination of Supply Webs Based on Dispositive Protocols

A lot of curricula in information systems, also at master level, exists today. However, the strong need in new approaches and new curricula still exists, especially, in European area. The paper discusses the modern curriculum in information systems at master level that is currently under development in the Socrates/Erasmus project MOCURIS. The curriculum is oriented to the students of engineering schools of technical universities. The proposed approach takes into account integration trends in European area as well as the transformation of industrial economics into knowledge-based digital economics The paper presents main characteristics of the proposed curriculum, discuses curriculum development techniques used in the project MOCURIS, describes the architecture of the proposed curriculum and the body of knowledge provided by it

The Semantic Grid: A future e-Science infrastructure

e-Science offers a promising vision of how computer and communication technology can support and enhance the scientific process. It does this by enabling scientists to generate, analyse, share and discuss their insights, experiments and results in an effective manner. The underlying computer infrastructure that provides these facilities is commonly referred to as the Grid. At this time, there are a number of grid applications being developed and there is a whole raft of computer technologies that provide fragments of the necessary functionality. However there is currently a major gap between these endeavours and the vision of e-Science in which there is a high degree of easy-to-use and seamless automation and in which there are flexible collaborations and computations on a global scale. To bridge this practice–aspiration divide, this paper presents a research agenda whose aim is to move from the current state of the art in e-Science infrastructure, to the future infrastructure that is needed to support the full richness of the e-Science vision. Here the future e-Science research infrastructure is termed the Semantic Grid (Semantic Grid to Grid is meant to connote a similar relationship to the one that exists between the Semantic Web and the Web). In particular, we present a conceptual architecture for the Semantic Grid. This architecture adopts a service-oriented perspective in which distinct stakeholders in the scientific process, represented as software agents, provide services to one another, under various service level agreements, in various forms of marketplace. We then focus predominantly on the issues concerned with the way that knowledge is acquired and used in such environments since we believe this is the key differentiator between current grid endeavours and those envisioned for the Semantic Grid

Rate of Price Discovery in Iterative Combinatorial Auctions

We study a class of iterative combinatorial auctions which can be viewed as subgradient descent methods for the problem of pricing bundles to balance supply and demand. We provide concrete convergence rates for auctions in this class, bounding the number of auction rounds needed to reach clearing prices. Our analysis allows for a variety of pricing schemes, including item, bundle, and polynomial pricing, and the respective convergence rates confirm that more expressive pricing schemes come at the cost of slower convergence. We consider two models of bidder behavior. In the first model, bidders behave stochastically according to a random utility model, which includes standard best-response bidding as a special case. In the second model, bidders behave arbitrarily (even adversarially), and meaningful convergence relies on properly designed activity rules