An assessment of strategies for choosing between competitive marketplaces
Traders that operate in markets with multiple competing marketplaces must often choose with which marketplace they will trade. These choices encourage marketplaces to seek competitive advantages against each other by adjusting various parameters, such as the price they charge, or how they match buyers and sellers. Traders can take advantage of this competition to improve utility. However, appropriate strategies must be used to decide with which marketplace a trader should shout. In this paper, we assess several different solutions to the problem of marketplace selection by running simulations of double auctions using the JCAT platform. The parameter spaces of these strategies are explored to find the best performing strategies. Results indicate that the softmax strategy is the most successful at maximising trader profit and global allocative efficiency in both adaptive and non-adaptive markets. The epsilon-decreasing strategy performs well in adaptive markets, while also showing greater stability in its parameter space than softmax. All marketplace selection strategies outperform the random marketplace selection strategy. (C) 2011 Elsevier B.V. All rights reserved.
marketplace selection double auction jcat mechanism design cat game reinforcement learning learning