Abstract
Due to the virtuality of online trade, e-market has an information asymmetrical system, and confirmation of sellers’ behavior and goods’ attributes mostly depend on the assessment of buyers. So, in a virtual environment, how the buyers utilize all existing information and choose the right sellers to guarantee the utility of a deal is of vital importance. This paper proposes an agent mediated e-market methodology that aims to help a buyer agent in selecting a seller that offers a good with the highest expected value. Due to the need to assess multiple attributes of a good in the virtual environment, this problem belongs to the class of fuzzy multi-attribute decision making. This problem is fuzzy in nature due to the lack of precision in assessing the relative importance of different attributes and the performance rating of goods from different sellers with respect to each attribute. The proposed methodology addresses these issues by first computing the expected value of a good being offered by different sellers using fuzzy set theory and then selecting a seller that offers the good with the highest expected value. The proposed methodology is relatively dynamic as it is sensitive to the changing experience of buyer agents in the e-market. This ensures that after sufficiently large number of transactions by the same buyer for a particular good, instead of incurring the overhead of computing the subjective weights, buyer can utilize the attribute weight information from the previous transactions.
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Gaur, V., Sharma, N.K. (2011). A Dynamic Seller Selection Model for an Agent Mediated e-Market. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22714-1_30
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DOI: https://doi.org/10.1007/978-3-642-22714-1_30
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