In this paper, we propose and analyze a distributed negotiation strategy for a multi-agent, multi-attribute negotiation in which the agents have no information about the utility functions of other agents. We analytically prove that, if the zone of agreement is nonempty and the agents concede up to their reservation utilities, agents generating offers using our offer-generation strategy, namely the sequential projection strategy, will converge to an agreement acceptable to all the agents; the convergence property does not depend on the specific concession strategy. In considering agents’ incentive to concede during the negotiation, we propose and analyze a reactive concession strategy. Through computational experiments, we demonstrate that our distributed negotiation strategy yields performance sufficiently close to the Nash bargaining solution and that our algorithms are robust to potential deviation strategies. Methodologically, our paper advances the state of the art of alternating projection algorithms, in that we establish the convergence for the case of multiple, moving sets (as opposed to two static sets in the current literature). Our paper introduces a new analytical foundation for a broad class of computational group decision and negotiation problems.

No abstract

Due to the lack of structured knowledge applied in learning distributed representation of categories, existing work cannot incorporate category hierarchies into entity information. We propose a framework that embeds entities and categories into a semantic space by integrating structured knowledge and taxonomy hierarchy from large knowledge bases. The framework allows to compute meaningful semantic relatedness between entities and categories. Our framework can handle both single-word concepts and multiple-word concepts with superior performance on concept categorization and yield state of the art results on dataless hierarchical classification.

A key challenge in creating a sustainable and energy-efficient society is to make consumer demand adaptive to the supply of energy, especially to the renewable supply. In this article, we propose a partially-centralized organization of consumers (or agents), namely, a consumer cooperative that purchases electricity from the market. In the cooperative, a central coordinator buys the electricity for the whole group. The technical challenge is that consumers make their own demand decisions, based on their private demand constraints and preferences, which they do not share with the coordinator or other agents. We propose a novel multiagent coordination algorithm, to shape the energy demand of the cooperative. To coordinate individual consumers under incomplete information, the coordinator determines virtual price signals that it sends to the consumers to induce them to shift their demands when required. We prove that this algorithm converges to the central optimal solution and minimizes the electric energy cost of the cooperative. Additionally, we present results on the time complexity of the iterative algorithm and its implications for agents' incentive compatibility. Furthermore, we perform simulations based on real world consumption data to (a) characterize the convergence properties of our algorithm and (b) understand the effect of differing demand characteristics of participants as well as of different price functions on the cost reduction. The results show that the convergence time scales linearly with the agent population size and length of the optimization horizon. Finally, we observe that as participants' flexibility of shifting their demands increases, cost reduction increases and that the cost reduction is not sensitive to variation in consumption patterns of the consumers.

The ongoing shortage of organs for transplantation has generated an expanding literature on efficient and equitable allocation of the donated cadaveric organs. By contrast, organ donation has been little explored. In this paper, we develop a parsimonious model of organ donation to analyze the welfare consequences of introducing the donor-priority rule, which grants registered organ donors priority in receiving organs should they need transplants in the future. We model an individual’s decision to join the donor registry, which entails a trade-off between abundance of supply, exclusivity of priority, and cost of donating (e.g., psychological burden). Assuming heterogeneity in the cost of donating only, we find the introduction of the donor-priority rule leads to improved social welfare. By incorporating heterogeneity in the likelihood of requiring an organ transplant and in organ quality, we show that, in contrast to the literature, introducing the donor-priority rule can lower social welfare because of unbalanced incentives across different types of individuals. In view of the potentially undesirable social-welfare consequences, we consider a freeze-period remedy, under which an individual is not entitled to a higher queueing priority until after having been on the organ-donor registry for a specified period of time. We show this additional market friction helps rebalance the incentive structure, and in conjunction with the donor-priority rule, can guarantee an increase in social welfare by boosting organ supply without compromising organ quality or inducing excessively high costs of donating. This paper was accepted by Gad Allon, operations management.