An Investigation of the Negotiation Domain for Electronic Commerce Information Systems

To support fully automatic business cycles, information systems for electronic commerce need to be able to conduct negotiation automatically. In recent years, a number of general frameworks for automated negotiation have been proposed. Application of such frameworks in a specific negotiation situation entails selecting the proper framework and adapting it to this situation. This selection and adaptation process is driven by the specific characteristics of the situation. This paper presents a systematic investigation of there characteristics and surveys a number of frameworks for automated negotiation

SOLACE: A framework for electronic negotiations

Copyright @ 2011 Walter de Gruyter GmbHMost existing frameworks for electronic negotiations today are tied to specific negotiation systems for which they were developed, preventing them from being applied to other negotiation scenarios. Thus, the evaluation of electronic negotiation systems is difficult as each one is based on a different framework. Additionally, each developer has to design a new framework for any system to be developed, leading to a ‘reinvention of the wheel’. This paper presents SOLACE—a generic framework for multi-issue negotiations, which can be applied to a variety of negotiation scenarios. In contrast with other frameworks for electronic negotiations, SOLACE supports hybrid systems in which the negotiation participants can be humans, agents or a combination of the two. By recognizing the importance of strategies in negotiations and incorporating a time attribute in negotiation proposals, SOLACE enhances existing approaches and provides a foundation for the flexible electronic negotiation systems of the future

KEMNAD: A Knowledge Engineering Methodology for Negotiating Agent Development

Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents(especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system

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

Efficient Methods for Automated Multi-Issue Negotiation: Negotiating over a Two-Part Tariff

In this article, we consider the novel approach of a seller and customer negotiating bilaterally about a two-part tariff, using autonomous software agents. An advantage of this approach is that win-win opportunities can be generated while keeping the problem of preference elicitation as simple as possible. We develop bargaining strategies that software agents can use to conduct the actual bilateral negotiation on behalf of their owners. We present a decomposition of bargaining strategies into concession strategies and Pareto-efficient-search methods: Concession and Pareto-search strategies focus on the conceding and win-win aspect of bargaining, respectively. An important technical contribution of this article lies in the development of two Pareto-search methods. Computer experiments show, for various concession strategies, that the respective use of these two Pareto-search methods by the two negotiators results in very efficient bargaining outcomes while negotiators concede the amount specified by their concession strategy

Does Resorting to Online Dispute Resolution Promote Agreements? Experimental Evidence

This paper presents the results of an experiment performed to test the properties of an innovative bargaining mechanism (called automated negotiation) used to resolve disputes arising from Internet-based transactions. Automated negotiation is an online sealed-bid process in which an automated algorithm evaluates bids from the parties and settles the case if the offers are within a prescribed range. The observed individual behavior, based on 40 rounds of bargaining, is shown to be drastically affected by the design of automated negotiation. The settlement rule encourages disputants to behave strategically by adopting aggressive bargaining positions, which implies that the mechanism is not able to promote agreements and generate efficiency. This conclusion is consistent with the experimental results on arbitration and the well-known chilling effect: Automated negotiation tends to "chill" bargaining as it creates incentives for individuals to misrepresent their true valuations and discourage them to converge on their own. However, this perverse effect induced by the settlement rule depends strongly on the conflict situation. When the threat that a disagreement occurs is more credible, the strategic effect is reduced since defendants are more interested in maximizing the efficiency of a settlement than their own expected profit.Online Dispute Resolution, Arbitration, Experimental Economics, Electronic Commerce, Bargaining

Does Resorting to Online Dispute Resolution Promote Agreements? Experimental Evidence

This paper presents an experiment performed to test the properties of an innovativebargaining mechanism (called automated negotiation) used to resolve disputes arising fromInternet-based transactions. The main result shows that the settlement rule tends to chillbargaining as it creates incentives for individuals to misrepresent their true valuations, whichimplies that automated negotiation is not able to promote agreements. However, this perverseeffect depends strongly on the conflict situation. When the threat that a disagreement occurs ismore credible, the strategic effect is reduced since defendants are more interested inmaximizing the efficiency of a settlement than their own expected profit. The implications ofthese results are then used to discuss the potential role of public regulation and reputationmechanisms in Cyberspace: Online Dispute Resolution, Electronic Commerce, Bargaining, Arbitration,Experimental Economics