Automated negotiation with Gaussian process-based utility models

Designing agents that can efficiently learn and integrate user's preferences into decision making processes is a key challenge in automated negotiation. While accurate knowledge of user preferences is highly desirable, eliciting the necessary information might be rather costly, since frequent user interactions may cause inconvenience. Therefore, efficient elicitation strategies (minimizing elicitation costs) for inferring relevant information are critical. We introduce a stochastic, inverse-ranking utility model compatible with the Gaussian Process preference learning framework and integrate it into a (belief) Markov Decision Process paradigm which formalizes automated negotiation processes with incomplete information. Our utility model, which naturally maps ordinal preferences (inferred from the user) into (random) utility values (with the randomness reflecting the underlying uncertainty), provides the basic quantitative modeling ingredient for automated (agent-based) negotiation

Bargaining and Incentive Compatibility: A Pareto Frontier Approach.

Two agents negotiate, according to the Nash bargaining solution, over the allocation of a single (divisible) commodity (or multiple commodities with fixed ordinal preferences). It has been shown that in this situation agents find dominant to report their least risk averse utility functions. This result depends crucially on the fact that in this kind of "distortion game", agents have been restricted to report risk-averse utility functions. This paper studies the distortion game originated when agents are also allowed to claim non risk-averse utility functions. Contrasting with previous literature, we find multiple Nash equilibria, multiple payo outcomes and the existence of a first-mover advantage.

Preference Learning in Automated Negotiation Using Gaussian Uncertainty Models

In this paper, we propose a general two-objective Markov Decision Process (MDP) modeling paradigm for automated negotiation with incomplete information, in which preference elicitation alternates with negotiation actions, with the objective to optimize negotiation outcomes. The key ingredient in our MDP framework is a stochastic utility model governed by a Gaussian law, formalizing the agent's belief (uncertainty) over the user's preferences. Our belief model is fairly general and can be updated in real time as new data becomes available, which makes it a fundamental modeling tool

Automated Negotiations under User Preference Uncertainty: A Linear Programming Approach

Autonomous agents negotiating on our behalf find applications in everyday life in many domains such as high frequency trading, cloud computing and the smart grid among others. The agents negotiate with one another to reach the best agreement for the users they represent. An obstacle in the future of automated negotiators is that the agent may not always have a priori information about the preferences of the user it represents. The purpose of this work is to develop an agent that will be able to negotiate given partial information about the user’s preferences. First, we present a new partial information model that is supplied to the agent, which is based on categorical data in the form of pairwise comparisons of outcomes instead of precise utility information. Using this partial information, we develop an estimation model that uses linear optimization and translates the information into utility estimates. We test our methods in a negotiation scenario based on a smart grid cooperative where agents participate in energy trade-offs. The results show that already with very limited information the model becomes accurate quickly and performs well in an actual negotiation setting. Our work provides valuable insight into how uncertainty affects an agent’s negotiation performance, how much information is needed to be able to formulate an accurate user model, and shows a capability of negotiating effectively with minimal user feedback

On the Importance of Equity in International Climate Policy: An Empirical Analysis

Based on unique data from a world-wide survey of agents involved in international climate policy, this paper empirically analyzes the importance of equity in this field. We find that equity issues are considered highly important in international climate negotiations and that the polluter-pays rule and the accompanying poor losers rule are the most widely accepted equity principles. Our econometric analysis shows a strong influence of the economic or emission performance of the agents? country on the importance of equity issues and principles: (i) Equity issues are seen as more important by individuals from G77/China countries or from countries with less current per capita GDP and less future per capita CO2 emissions. (ii) Agents from richer countries are less in favor of incorporating the polluter-pays and the ability-to-pay principle in future international climate agreements. (iii) The poor losers rule is more strongly supported by individuals from G77/China countries or by individuals from countries with less current per capita GDP. While these results are consistent with pure economic self-interest, the support for the egalitarian principle runs contrary to economic intuition: In the long-run, agents from richer countries are more in favor of incorporating the egalitarian principle. Furthermore, the effect of the economic performance variables on the desired degree of incorporating the polluter-pays principle interestingly becomes less significant in the long-run. This indicates that future international climate agreements could possibly be based on a combination of the polluter-pays, the egalitarian, and the poor losers rule. --International Climate Policy,International Environmental Negotiations,Equity Issues,Probit Models

Addressing stability issues in mediated complex contract negotiations for constraint-based, non-monotonic utility spaces

Negotiating contracts with multiple interdependent issues may yield non- monotonic, highly uncorrelated preference spaces for the participating agents. These scenarios are specially challenging because the complexity of the agents’ utility functions makes traditional negotiation mechanisms not applicable. There is a number of recent research lines addressing complex negotiations in uncorrelated utility spaces. However, most of them focus on overcoming the problems imposed by the complexity of the scenario, without analyzing the potential consequences of the strategic behavior of the negotiating agents in the models they propose. Analyzing the dynamics of the negotiation process when agents with different strategies interact is necessary to apply these models to real, competitive environments. Specially problematic are high price of anarchy situations, which imply that individual rationality drives the agents towards strategies which yield low individual and social welfares. In scenarios involving highly uncorrelated utility spaces, “low social welfare” usually means that the negotiations fail, and therefore high price of anarchy situations should be avoided in the negotiation mechanisms. In our previous work, we proposed an auction-based negotiation model designed for negotiations about complex contracts when highly uncorrelated, constraint-based utility spaces are involved. This paper performs a strategy analysis of this model, revealing that the approach raises stability concerns, leading to situations with a high (or even infinite) price of anarchy. In addition, a set of techniques to solve this problem are proposed, and an experimental evaluation is performed to validate the adequacy of the proposed approaches to improve the strategic stability of the negotiation process. Finally, incentive-compatibility of the model is studied.Spain. Ministerio de Educación y Ciencia (grant TIN2008-06739-C04-04

Utility Measurement in Integrative Negotiation

This paper develops an adjustment to utility measurement in integrative negotiation where the negotiation information context is incomplete. The developed function reveals not only win-win outcomes but also deceptive practices where negotiators accept a win-lose deal and then compensate their loss in a deceptive way and greedy practices where negotiators realize their strong competitive position and try to extremely maximize their gains. However, to realize the objective, the utility measurement function literature and theories are reviewed to determine the relevant function structure and the necessary attributes that reveal the desired outcome in an incomplete information context. After examination, relationship measurement is added to the function under two utilities: Decision Utility and Experienced Utility. The foundation of the utility measurement function contributes to revealing satisfying win-win outcomes in an incomplete information negotiation context. Therefore, it develops the negotiation field by designing win-win deals that are beneficial and satisfying in which the advantage is distributed between the negotiators

A non-welfarist solution for two-person bargaining situations.

In this paper we present a non-welfarist solution which is applicable to a broad spectrum of twoagent bargaining problems, such as exchange economies, location problems and division problems. In contrast to welfarist bargaining solutions, it depends only on the agents' preferences. not on their specific utility representation, and takes explicitly into account the underlying space of alternatives. We offer a simple sequential move mechanism, without chance moves, that implements our solution in subgame perfect equilibrium. Moreover, an axiomatic characterization of the solution is provided. It is shown that the solution coincides with the Kalai-Rosenthal bargaining solution after choosing a suitable utility representation of the preferences. When applied to exchange economies with equal initial endowments for both agents, the solution generates envy-free, Pare to efficient egalitarian equivalent allocations.Bargaining; Nash program; Welfarism; Non-welfarism; Exchange economies; Location problems; Implementation;