Concession-making in multi-attribute auctions and multi-bilateral negotiations: Theory and experiments

Concession-making behavior is an essential process in negotiations and auctions and has critical impact on the outcomes of an exchange. In auctions, concessions relate to deciding on the next bid by bid-makers, while in negotiations they involve proposing next offers by both parties. The purpose of this paper is twofold: (1) present a theory of concessions which could be applied to both auctions and negotiations and (2) provide experimental verification of the theory. The concession model identifies nine types of concessions derived from the preference structures. The occurrence of all nine types has been confirmed in experiments. The model also identifies two concession categories which are associated with the process transparency. The theory-based assessment of the concession-making in multi-attribute auctions and multi-issue multi-bilateral negotiations allows for their comparison. To this end, two systems implemented on the Invite e-negotiation platform are used. One of the major findings suggests that auctions induce concession-making that is more likely to lead to Pareto-optimal agreements. This study also suggests that greater transparency in negotiations is likely to lead to better agreements.     

Multi‐agent distributed intelligent system based on fuzzy decision making

Generally decision making for solving ill‐structured problems in DSS takes place in uncertain situations. The main drawbacks of existing traditional DSS are inefficiencies associated with dealing with complex models and large databases. Usually a fuzzy DSS has many input variables and, hence, its knowledge base, containing the totality of fuzzy rules, is very large. Large rule base leads to disadvantages in speed, reliability, and complexity of DSS. This paper introduces an alternative concept for designing fuzzy DSS based on multi‐agent distributed artificial intelligent technology and fuzzy decision making. The main idea of the proposed DSS is based on granulation of the overall system intelligence between cooperative autonomous intelligent agents capable of competing and cooperating with each other in order to propose a total solution to the problem and organization (combining individual solutions) of the proposed solution into the final solution. It is supposed that every agent in DSS is characterized by a set of fuzzy criteria of unequal importance and definition of a “winner” agent is based on multi‐criteria fuzzy decision making involving unequal objectives. © 2000 John Wiley & Sons, Inc.     

Genetic algorithms-based fuzzy regression analysis

 This paper describes the concept of fuzzy regression analysis based on genetic algorithms. It is shown that the performance of fuzzy regression models may be improved and fuzzy modeling technique can be simplified by incorporating genetic algorithms into regression analysis procedure. The effectiveness of the proposed approach is illustrated through simulation of fuzzy linear regression model obtained by other authors and comparison of the results. The paper further demonstrates the applications of the approach to the manufacturing and business problems.     

Experimental Assessment of Agent-Supported Electronic Negotiations

This article presents the results of experimental evaluation of the effectiveness of agent support in e-negotiations. The agent-enhanced e-negotiation system eAgora has been used in the experiments. The system features an agent that assists the user in generating candidate offers, evaluating and critiquing incoming offers, and critiquing counteroffers. The work investigates the effects of agent support and task complexity on negotiation performance and perceived measures of usefulness, satisfaction, ease of use, and confidence. Overall, the results support the expectation that use of an agent leads to higher level of negotiation effectiveness, in particular for higher complexity tasks.     

Intermediating user-DSS interaction with autonomous agents

The use of advanced decision support system (DSS) capabilities is hampered by the inadequacy of a "toolbox" organization of DSS from the user's perspective. In such a setup, the user is assumed to have all the knowledge and skills necessary to appropriately use the tools provided by the system in the decision-making process. This paper proposes a model for the use of autonomous agents as intermediaries between the users and the system. The model is organized around the human problem-solving process. The paper elaborates on the types of intermediary agents and the architecture for a DSS. The approach is illustrated using the prototype for an investment DSS.