Searching for joint gains in automated negotiations based on multi-criteria decision making theory

It is well established by conflict theorists and others that successful negotiation should incorporate "creating value" as well as "claiming value." Joint improvements that bring benefits to all parties can be realised by (i) identifying attributes that are not of direct conflict between the parties, (ii) tradeoffs on attributes that are valued differently by different parties, and (iii) searching for values within attributes that could bring more gains to one party while not incurring too much loss on the other party. In this paper we propose an approach for maximising joint gains in automated negotiations by formulating the negotiation problem as a multi-criteria decision making problem and taking advantage of several optimisation techniques introduced by operations researchers and conflict theorists. We use a mediator to protect the negotiating parties from unnecessary disclosure of information to their opponent, while also allowing an objective calculation of maximum joint gains. We separate out attributes that take a finite set of values (simple attributes) from those with continuous values, and we show that for simple attributes, the mediator can determine the Pareto-optimal values. In addition we show that if none of the simple attributes strongly dominates the other simple attributes, then truth telling is an equilibrium strategy for negotiators during the optimisation of simple attributes. We also describe an approach for improving joint gains on non-simple attributes, by moving the parties in a series of steps, towards the Pareto-optimal frontier

On the Actual Inefficiency of Efficient Negotiation Methods

In this contribution we analyze the effect that mutual information has on the actual performance of efficient negotiation methods. Specifically, we start by proposing the theoretical notion of Abstract Negotiation Method (ANM) as a map from the negotiation domain in itself, for any utility profile of the parties. ANM can face both direct and iterative negotiations, since we show that ANM class is closed under the limit operation. The generality of ANM is proven by showing that it captures a large class of well known in literature negotiation methods. Hence we show that if mutual information is assumed then any Pareto efficient ANM is manipulable by one single party or by a collusion of few of them. We concern about the efficiency of the resulting manipulation. Thus we find necessarily and sufficient conditions those make manipulability equivalent to actual inefficiency, meaning that the manipulation implies a change of the efficient frontier so the Pareto efficient ANM converges to a different, hence actually inefficient, frontier. In particular we distinguish between strong and weak actual inefficiency. Where, the strong actual inefficiency is a drawback which is not possible to overcome of the ANMs, like the Pareto invariant one, so its negotiation result is invariant for any two profiles of utility sharing the same Pareto frontier, we present. While the weak actual inefficiency is a drawback of any mathematical theorization on rational agents which constrain in a particular way their space of utility functions. For the weak actual inefficiency we state a principle of Result's Inconsistency by showing that to falsify theoretical hypotheses is rational for any agent which is informed about the preference of the other, even if the theoretical assumptions, which constrain the space of agents' utilities, are exact in the reality, i.e. the preferences of each single agent are well modeled

Automated Negotiation for Provisioning Virtual Private Networks Using FIPA-Compliant Agents

This paper describes the design and implementation of negotiating agents for the task of provisioning virtual private networks. The agents and their interactions comply with the FIPA specification and they are implemented using the FIPA-OS agent framework. Particular attention is focused on the design and implementation of the negotiation algorithms

An Evolutionary Learning Approach for Adaptive Negotiation Agents

Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications

A Secure and Fair Protocol that Addresses Weaknesses of the Nash Bargaining Solution in Nonlinear Negotiation

Negotiation with multiple interdependent issues is an important problem since much of real-world negotiation falls into this category. This paper examines the problem that, in such domains, agent utility functions are nonlinear, and thereby can create nonconvex Pareto frontiers. This in turn implies that the Nash Bargaining Solution, which has been viewed as the gold standard for identifying a unique optimal negotiation outcome, does not serve that role in nonlinear domains. In nonlinear domains, unlike linear ones, there can be multiple Nash Bargaining Solutions, and all can be sub-optimal with respect to social welfare and fairness. In this paper, we propose a novel negotiation protocol called SFMP (the Secure and Fair Mediator Protocol) that addresses this challenge, enabling secure multilateral negotiations with fair and pareto-optimal outcomes in nonlinear domains. The protocol works by (1) using nonlinear optimization, combined with a Multi-Party protocol, to find the Pareto front without revealing agent’s private utility information, and (2) selecting the agreement from the Pareto set that maximizes a fair division criterion we call approximated fairness. We demonstrate that SFMP is able to find agreements that maximize fairness and social welfare in nonlinear domains, and out-performs (in terms of outcomes and scalability) previously developed nonlinear negotiation protocols

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

Multiple Issue Action and Market Algorithms for the World Wide Web

The Internet is quickly changing the way business-to-consumer and business-to-business commerce is conducted in the world. The Electronic Revolution has also spawned a trend of price wars and, in some instances, chaos because of the zero-sum nature of the electronic channel. The technology has created an opportunity to get beyond the lose-lose nature of single issue price wars by determining sellers' and buyers' preferences across multiple issues and encouraging negotiations, thereby creating possible joint gains for all parties. We develop simple multiple issue algorithms and heuristics that could be used in electronic auctions and electronic markets, to match business to business and consumers based on dovetailing underlying interests and preferences. We provide arguments that such dovetailed matches should help stabilize markets and make them more efficient