DSS negotiation application

One important application of the DSS is in the field of negotiation. This does not refer to what is normally thought of as the concept of negotiation, although there are many DSS systems used in negotiation, such as in the settlement of divorce cases. A negotiation support in a business application can be a supply, production and delivery system which must negotiate logistic factors when coming to the necessary decisions. Negotiation support through DSS has grown in importance with applications for many and varied purposes. Researchers are engaged in studying related issues from various fields such as machine learning, social sciences and game theory. It is also being applied in other fields, such as agent technology, economics, psychology and negotiation support systems (Lao and Zhong, 2010).

The main objective of the research described in (Bellucci and Zeleznikow, 1998) is to build negotiation support tools to develop the most up-to-date versions of smart negotiation support systems. They point out that when the system based on agent-based systems and reasoning as well as machine learning and argumentation, it does not give the domain enough depth to retrieve optimal solutions (Gonzalez, Lerch and Lebiere, 2003). Therefore, they recommend mathematical ways of finding these solutions, such as fuzzy set and probability theories. They have built four systems and applied it to Australian family law (including divorce law).

It is necessary to determine which cases are involved in a negotiation. In some ways, it is important to consider whether the cases are dependent, semi-dependent or independent. Thus, it is imperative to focus on the relationships between cases to establish the best method of obtaining a satisfactory settlement (Bellucci and Zeleznikow, 1998). However, there are difficulties in describing these relationships, because of the subjectivity of the parameters and the interrelation of factors. The researcher was not convinced that their work can be independently verified because of the skewed nature of the input.

This study examined the facilities for interactive and active learning by using the joint gains Negotiation Support Systems (NSS) from a web site. The Joint Gains is a web-based facility for carrying out interactive negotiations, and is used to solve negotiation problems online. It has been successfully applied in a Lake-River regulation policy problem. They foresee that potential users of the proposed site could include experts and students in e-business. Moreover, it could be useful for the training of professionals involved in environmental and political decision making. These aims are taken into account when choosing the examples and cases presented in the material. For instance, the problem of the commons provides insights into the challenges of environmental decision making, and the ‘buyer-seller’ case in this study features a problem emerging frequently, in one form or another, between customers and their suppliers in e-business and supply paths (Ehtamo, Hamalainen and Koskinen, 2004).

One way of introducing Negotiation Support Systems (NSS) to real-life applications is that researchers carry out their use in role-playing procedures and then present the changing role process to real participators. The researchers want to make their methods close to real-life situations by using the NSSs with real limitations. Therefore, this study’s e-learning samples contain theory sections developed with proper references to the literature, as well as quizzes, assignments and case studies. They are developed with multimedia projects, such as video clips, animations and colourful graphics, where necessary. Facilities are provided for interactive and active learning using the joint gains NSS (Ehtamo, Hamalainen and Koskinen, 2004). The Joint Gains has been successfully applied to the problem of water resource management in a Lake- River regulation policy (Ehtamo, Hamalainen and Koskinen, 2004).

Negotiation is an important part of commodity trading and business. It can be defined as the process in which two or more parties conduct talks so as to reach a trading agreement. Traditional negotiations focus on humans who, based on their specific interests, negotiate and finally reach a

conclusion which is agreeable to all parties. E-commerce has become an important part of the national economy, as well as social information (Bellucci and Zeleznikow, 1998). With the development of e-commerce and globalisation, the complexity of negotiations has increased and people have to react to market changes more quickly. This kind of negotiation has the features of a long period before reaching agreement and a slow response to environmental change. Therefore, the traditional human-based negotiation has not adapted well to the global e-commerce market (Lao and Zhong, 2010).

E-commerce oriented automatic negotiation systems have aroused great interest, and technology is driving and continually evolving the study of automatic negotiation. The maturity of semantic web and ontology technology solves the problem of multi-agents in the interaction process (Hanratty et al., 2007). Also, the huge potential demand for applications in the e-commerce market is attracting technicians and engineers into deeper study of automatic negotiation. The automated negotiation system is realised using agent technology. Agent technology simulates the actual behaviour of individuals using a computer program which makes use of artificial intelligence. Using agents instead of supply or demand to negotiate will greatly improve the efficiency of remote business operations. In the complex and uncertain environment of e- commerce, a good negotiation system not only saves time but also achieves a good deal (Hanratty et al., 2007). Therefore, the application of DSS to negotiation is useful in the field of decision- making.