Resource Allocation In Decentralized Systems With Strategic Agents

This thesis presents a significant contribution to decentralized resource allocation problems with strategic agents. The study focused on three classes of problems arising in communication networks. (C1). Unicast service provisioning in wired networks. (C2). Multi-rate multicast service provisioning in wired networks. (C3). Power allocation and spectrum sharing in multi-user multi-channel wireless communication systems. Problems in (C1) are market problems; problems in (C2) are a combination of markets and public goods; problems in (C3) are public goods. Dr. Kakhbod developed game forms/mechanisms for unicast and multi-rate multicast service provisioning that possess specific properties. First, the allocations corresponding to all Nash equilibria (NE) of the games induced by the mechanisms are optimal solutions of the corresponding centralized allocation problems, where the objective is the maximization of the sum of the agents' utilities. Second, the strategic agents voluntarily participate in the allocation process. Third, the budget is balanced at the allocations corresponding to all NE of the game induced by the mechanism as well as at all other feasible allocations. For the power allocation and spectrum sharing problem, he developed a game form that possesses the second and third properties as detailed above along with a fourth property: the allocations corresponding to all NE of the game induced by the mechanism are Pareto optimal. The thesis contributes to the state of the art of mechanism design theory. In particular, designing efficient mechanisms for the class of problems that are a combination of markets and public goods, for the first time, have been addressed in this thesis. The exposition, although highly rigorous and technical, is elegant and insightful which makes this thesis work easily accessible to those just entering this field and will also be much appreciated by experts in the field.

Modern engineering and social systems are often too complex to be managed by a centralized agent. Instead, such systems are commonly structured with multiple decentralized agents each responsible for managing a subset of the system, but the resulting system performance depends on the aggregate of the decisions made by decentralized agents. Local agents' decision makings often exhibit selfish behavior as they seek to optimize their own objectives under their localized models, which if left uncoordinated can lead to substantial loss of efficiency compared with the system that can be optimized by a single (hypothetical) centralized agent. In this dissertation, we seek to study the fundamental issues of how to efficiently manage large-scale and multi-agent stochastic dynamic systems, especially on how to device efficient coordination mechanisms that would optimize system performance under various constraints that are unique to decentralized systems. In the first part of this dissertation we study decentralized control of a general class of stochastic dynamic resource allocation problems that have many applications. We consider a stochastic system in which multiple decentralized agents allocate shared system resources in response to customer requests that arrive stochastically over time. Each agent is responsible for a subset of the allocation decisions which it makes according to a dynamic allocation policy obtained by maximizing his own expected profit subject to a potentially mis-specified model of the way in which shared resources are consumed by other agents. We introduce the notion of a transfer contract which specifies how agents compensate one another whenever resources are consumed and establish the existence of contracts under which the decentralized system has no efficiency loss relative to centralized optimality. We also show that this property is insensitive to mis-specification by each agent of the dynamics of resource consumption by others in the system. An explicit characterization of the optimal transfer contract and an iterative decentralized algorithm for computing it is also provided. In the language of duality, contracts are analogous to shadow prices and the iterative algorithm has the favor of a dual update method, but strong duality and convergence of the iterative algorithm to the set of optimal contracts are guaranteed without assumptions of convexity. In the second part of this dissertation we study a class of related decentralized control problems but specialize to portfolio and risk management. Many financial institutions typically trade in multiple correlated markets. While centralized portfolio optimization over all trading decisions is ideal, it is generally not possible due to the complexity of each market, and firms typically adopt a decentralized setup in which trading in each market the responsibility of a particular desk. Decentralized portfolio optimization, however, is complicated by the fact that different agents are commonly only well informed about their own investment universe (proprietary research and forecasts, etc) and prefer to keep this private, and have their own incentives which they optimize on the basis of their limited models. It is well known, however, that the aggregate performance of such a system can be extremely inefficient due to the loss of diversification. In this dissertation, we formulate a multi-agent dynamic portfolio choice problem and study how to improve its efficiency. We show that an internal system of swap contracts, which define internal cash transfers between agents, can be used to facilitate risk sharing and induce agents to choose portfolios that as a collection are optimal for the firm. Conceptually using swap contracts is similar to performance benchmarking that is often employed in the finance literature for decentralized portfolio management, but our new approach offers a significant advantage in that the swap contracts can be constructed in decentralized manner without requiring an all-knowing central agent. We provide an explicit characterization of the optimal swap contracts and an iterative algorithm for computing them that can be implemented without compromising proprietary agent level data. Throughout this dissertation, we also discuss various important issues surrounding decentralized control of stochastic dynamic systems, including but not limited to approximation methods, performance attribution, sensitivity analysis, and fairness issues, etc.

This thesis presents a novel market-based method, inspired by retail markets for resource allocation in fully decentralised computational systems where agents are self-interested. The posted offer mechanism used requires no central or regional coordinator or complex negotiation strategies. The stability of outcome allocations, those at equilibrium, is analysed and compared for three buyer behaviour models. The approach is scalable, robust and may be tuned to achieve a range of desired outcome resource allocations. These include a balanced load, allocations reflective of providers differing capabilities and those appropriate to heterogeneous buyer preferences over multiple attributes. The behaviour of the approach is studied both game theoretically and in simulation, where novel evolutionary market agents act on behalf of resource providing nodes to adaptively price their resources over time in response to market conditions. Sellers competitively co-evolve their offers online without any need for global market information. This is shown to lead the system to the game theoretically predicted outcome resource allocation when buyers decision functions degrade gracefully. Additionally, allocations remain stable in the presence of small changes in price and other more disruptive agents. The posted offer model therefore appears to be a useful mechanism for resource allocation in both homogeneous and heterogeneous decentralised computational systems where nodes are self-interested. Furthermore, evolutionary computation is shown to be a potential approach to realising self-interested adaptive pricing behaviour under the assumption of private information present in the posted offer model.

This book constitutes the refereed proceedings of the Second International Working Conference on Active Networks, IWAN 200, held in Tokyo, Japan in October 2000. The 30 revised full papers presented were carefully reviewed and selected from numerous submissions. The book offers topical sections on architecture, multicast, quality of service (QoS), applications, management, service architecture, and mobile IP.

The scientific theme of the book concerns “Manufacturing as a Service (MaaS)” which is developed in a layered cloud networked manufacturing perspective, from the shop floor resource sharing model to the virtual enterprise collaborative model, by distributing the cost of the manufacturing infrastructure - equipment, software, maintenance, networking - across all customers. MaaS is approached in terms of new models of service-oriented, knowledge-based manufacturing systems optimized and reality-aware, that deliver value to customer and manufacturer via Big data analytics, Internet of Things communications, Machine learning and Digital twins embedded in Cyber-Physical System frameworks. From product design to after-sales services, MaaS relies on the servitization of manufacturing operations such as: Design as a Service, Predict as a Service or Maintain as a service. The general scope of the book is to foster innovation in smart and sustainable manufacturing and logistics systems and in this context to promote concepts, methods and solutions for the digital transformation of manufacturing through service orientation in holonic and agent-based control with distributed intelligence. The book’s readership is comprised by researchers and engineers working in the manufacturing value chain area who develop and use digital control solutions in the ‘Industry of the Future’ vision. The book also addresses to master and Ph.D. students enrolled in Engineering Sciences programs.

This book explores the resource allocation process in contemporary private research universities through six richly detailed case studies. It includes an extensive discussion of historical approaches to university resource allocation. The cases are based on in-depth interviews with university presidents, provosts, deans financial officers, and department chairs. The evolution of university resource allocation systems is discussed in relation to institutional history, mission, culture, priorities, leadership, and prevailing financial condition.