On Choosing an Efficient Service Selection Mechanism in Dynamic Environments

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.