This work re-examined the simulation result of game analysis (Joshi et al., 2000) based on an agent-based model, Santa Fe Institute Artificial Stock Market. Allowing for recent research work on this artificial model, ...This work re-examined the simulation result of game analysis (Joshi et al., 2000) based on an agent-based model, Santa Fe Institute Artificial Stock Market. Allowing for recent research work on this artificial model, this paper’s modified game simulations found that the dividend amplitude parameter is a crucial factor and that the original conclusion still holds in a not long period, but only when the dividend amplitude is large enough. Our explanation of this result is that the dividend amplitude pa- rameter is a measurement of market uncertainty. The greater the uncertainty, the greater the price volatility, and so is the risk of investing in the stock market. The greater the risk, the greater the advantage of including technical rules.展开更多
Automated negotiation is the key techniques for reaching agreements in agent-mediated electronic commerce. Current automated negotiation models assume that users know the value of the product or service they want to b...Automated negotiation is the key techniques for reaching agreements in agent-mediated electronic commerce. Current automated negotiation models assume that users know the value of the product or service they want to buy and provide their agents with a reservation price, and the agents make offers and reach agreements with other agents according to this reservation price. However, in real world electronic marketplaces users probably do not know the exact value of the item, which is in terms of price, and the reservation price they set to their agents only means the maximum price they are willing to pay for the item. In this paper, we propose a negotiation model to deal with the valuation problem. The shopping agent in our model can deliberate the market price and the seller agent’s reservation price from public available information and the seller agent’s proposals. Also in order to conform with the real world negotiation conditions, we introduce negotiation features of real world human shopping such as multiple sellers, valuation, ultimatum, and learning from available information, etc. into our model.展开更多
基金Project supported by the Talent Project Foundation of Zhejiang Province, China
文摘This work re-examined the simulation result of game analysis (Joshi et al., 2000) based on an agent-based model, Santa Fe Institute Artificial Stock Market. Allowing for recent research work on this artificial model, this paper’s modified game simulations found that the dividend amplitude parameter is a crucial factor and that the original conclusion still holds in a not long period, but only when the dividend amplitude is large enough. Our explanation of this result is that the dividend amplitude pa- rameter is a measurement of market uncertainty. The greater the uncertainty, the greater the price volatility, and so is the risk of investing in the stock market. The greater the risk, the greater the advantage of including technical rules.
文摘Automated negotiation is the key techniques for reaching agreements in agent-mediated electronic commerce. Current automated negotiation models assume that users know the value of the product or service they want to buy and provide their agents with a reservation price, and the agents make offers and reach agreements with other agents according to this reservation price. However, in real world electronic marketplaces users probably do not know the exact value of the item, which is in terms of price, and the reservation price they set to their agents only means the maximum price they are willing to pay for the item. In this paper, we propose a negotiation model to deal with the valuation problem. The shopping agent in our model can deliberate the market price and the seller agent’s reservation price from public available information and the seller agent’s proposals. Also in order to conform with the real world negotiation conditions, we introduce negotiation features of real world human shopping such as multiple sellers, valuation, ultimatum, and learning from available information, etc. into our model.
