Multiagent model and mean field theory of complex auction dynamics

Abstract

Recent years have witnessed a growing interest in analyzing a variety of socio-economic phenomena using methods from statistical and nonlinear physics. We study a class of complex systems arising from economics, the lowest unique bid auction (LUBA) systems, which is a recently emerged class of online auction game systems. Through analyzing large, empirical data sets of LUBA, we identify a general feature of the bid price distribution: an inverted J-shaped function with exponential decay in the large bid price region. To account for the distribution, we propose a multi-agent model in which each agent bids stochastically in the field of winner's attractiveness, and develop a theoretical framework to obtain analytic solutions of the model based on mean field analysis. The theory produces bid-price distributions that are in excellent agreement with those from the real data. Our model and theory capture the essential features of human behaviors in the competitive environment as exemplified by LUBA, and may provide significant quantitative insights into complex socio-economic phenomena.

Controlling herding in minority game systems

“…The agents who pick the minority resource are rewarded, and those belonging to the majority group are punished due to limited resources. The MG dynamics has been studied extensively in the past 21 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 .…”