| 摘要 |
A model of consumer behavior in a transaction environment such as customers moving around a bank branch, is generated from an artificial life algorithm to create a number of agents. In each agent, a genetically encoded drive, equivalent for example to hunger, is defined so as to correspond to a transaction need such as the need for cash. Interaction rules, such as navigation rules, are set for interaction between the agents and a first representation of an environment, and the program is run and the agents observed, then compared with real human behavior. The best matched agents are selected and the program run again, the steps being repeated until a required level of comparison with real behavior is reached. The model can then be used with different transaction environments to study customer behavior and to select the best branch layout or the like.
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