We propose an evolutionary approach for studying the dynamics of interaction of strategic agents that interact in a marketplace. The goal is to learn which agent strategies are most suited by observing the distribution of the agents that survive in the market over extended periods of time. We present experimental results from a simulated market, where multiple service providers compete for customers using different deployment and pricing schemes. The results show that heterogeneous strategies evolve and co-exist in the same market.

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Keywords business intelligence, dutch flower auctions (dfa), decision support systems, learning agents, smart business networks, teaching case
Persistent URL hdl.handle.net/1765/115512
Ketter, W, van Heck, E, & Zuidwijk, R.A. (2010). Intelligent Personalized Trading Agents that facilitate Real-time Decision-making for Auctioneers and Buyers in the Dutch Flower Auctions. Retrieved from http://hdl.handle.net/1765/115512