If multi-agent learning is the answer, what is the question?
Artificial Intelligence
What evolutionary game theory tells us about multiagent learning
Artificial Intelligence
An evolutionary model of multi-agent learning with a varying exploration rate
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Vickrey vs. eBay: Why Second-Price Sealed-Bid Auctions Lead to More Realistic Price-Demand Functions
International Journal of Electronic Commerce
A grey-box approach to automated mechanism design
Electronic Commerce Research and Applications
An evolutionary game-theoretic comparison of two double-auction market designs
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
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We analyze the competitive advantage of price signal information for traders in simulated double auctions. Previous work has established that more information about the price development does not guarantee higher performance. In particular, traders with limited information perform below market average and are outperformed by random traders; only insiders beat the market. However, this result has only been shown in markets with a few traders and a uniform distribution over information levels. We present additional simulations of several more realistic information distributions, extending previous findings. In addition, we analyze the market dynamics with an evolutionary model of competing information levels. Results show that the highest information level will dominate if information comes for free. If information is costly, less-informed traders may prevail reflecting a more realistic distribution over information levels.