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Most of the past studies about dealership market involved only one monoplistic market-maker. The paper aims to build an artificial financial market based on multiple competitive market makers on ANYLOGIC platform, in which one market-maker adopts BAYES learning rule to estimate the fundamental value and the other employs a rough method. In order to validate the effectiveness of dealers’ quotes, we carried out two group simulation experiment. The results show that the quote of each dealer can converge to the fundamental value with certain deviation. What’s more, the deviation of the market-maker with learning ability is smaller while the converging speed slower.

One-switch utility function is used to describe how the risk attitude of a decision maker changes with his wealth level. In this paper additive decision rule is used for the aggregation of decision member’s utility which is represented by one-switch utility function. Based on Markov decision processes (MDP) and group utility, a dynamic, multi-stages and risk sensitive group decision model is proposed. The proposed model augments the state of MDP with wealth level, so the policy of the model is defined as an action executed in a state and a wealth level interval. A backward-induction algorithm is given to solve the optimal policy for the model. Numerical examples show that personal risk attitude has a great influence on group decision-making when personal risk attitudes of members are different, while the weights of members play a critical role when personal risk attitudes of members are similar.