Abstract
Consumers use service selection mechanisms to decide on a service provider to interact with. Although there are various service selection mechanisms, each mechanism has different strengths and weaknesses for different settings. In this paper, we propose a novel approach for consumers to learn how to choose the most useful service selection mechanism among different alternatives in dynamic environments. In this approach, consumers continuously observe outcomes of different service selection mechanisms. Using their observations and a reinforcement learning algorithm, consumers learn to choose the most useful service selection mechanism with respect to their trade-offs. Through the simulations, we show that not only the consumers choose the most useful service selection mechanism using the proposed approach, but also the performance of the proposed approach does not go below the lower-bound defined by the tradeoffs of the consumers.
This research has been partially supported by Boğaziçi University Research Fund under grant BAP07A102 and The Scientific and Technological Research Council of Turkey by a CAREER Award under grant 105E073.
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Şensoy, M., Yolum, P. (2008). On Choosing an Efficient Service Selection Mechanism in Dynamic Environments. In: Collins, J., et al. Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis. AMEC TADA 2007 2007. Lecture Notes in Business Information Processing, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88713-3_8
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DOI: https://doi.org/10.1007/978-3-540-88713-3_8
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