Ronghuo Zheng

Automated Bilateral Multiple-issue Negotiation with No Information About Opponent

In this paper, we investigate offer generation methods for automated negotiation on multiple issues with no information about the opponents utility function. In existing negotiation literature, it is usually assumed that an agent has full information or probabilistic beliefs about the other agents utility function. However, it is usually not possible for agents to have complete information about the other agents preference or accurate probability distributions. We prove that using an alternating projection strategy, it is possible to reach an agreement in general automated multi-attribute negotiation, where the agents have nonlinear utility functions and no information about the utility function of the other agent. We also prove that rational agents do not have any incentive to deviate from the proposed strategy. We further present simulation results to demonstrate that the solution obtained from our protocol is quite close to the Nash bargaining solution.

Automated Multilateral Negotiation on Multiple Issues with Private Information

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.

Compensation Duration, Shareholder Governance, and Managerial Short-Termism

In this paper, I investigate the interaction between the duration of executive compensation and shareholder governance. I show that short-term compensation can elicit shareholder intervention and thus enhance firm value. The central mechanism is that the use of short-term incentives enables informed incumbent shareholders to commit to using their private information to intervene (voice) instead of selling their shares (exit). Without a commitment to voice, incumbent shareholders might find, ex post, that exit is more appealing than voice if they privately observe that a firms type is bad. Short-term incentives encourage a good firm to take actions that reveal its type early on, which reduces the information advantage of the incumbent shareholders and their ability to profit from exit. Effectively, short-term compensation serves as a commitment device for value-enhancing intervention.

Demand Management with Energy Generation and Storage in Collectives

In this paper, we focus on demand side management in consumer collectives with community owned renewable energy generation and storage facilities for effective integration of renewable energy with the existing fossil fuel-based power supply system. The collective buys energy as a group through a central coordinator who also decides about the storage and usage of renewable energy produced by the collective. Our objective is to design coordination algorithms to minimize the cost of electricity consumption of the consumer collective while allowing the consumers to make their own consumption decisions based on their private consumption constraints and preferences. Minimizing the cost is not only of interest to the consumers but is also socially desirable because it reduces the consumption at times of peak demand. We develop an iterative coordination algorithm in which the coordinator makes the storage decision and shapes the demands of the consumers by designing a virtual price signal for the agents. We prove that our algorithm converges, and it achieves the optimal solution under realistic conditions. We also present simulation results based on real world consumption data to quantify the performance of our algorithm.