Hutchinson John M C, Gigerenzer Gerd
Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany.
Behav Processes. 2005 May 31;69(2):97-124. doi: 10.1016/j.beproc.2005.02.019.
The Centre for Adaptive Behaviour and Cognition (ABC) has hypothesised that much human decision-making can be described by simple algorithmic process models (heuristics). This paper explains this approach and relates it to research in biology on rules of thumb, which we also review. As an example of a simple heuristic, consider the lexicographic strategy of Take The Best for choosing between two alternatives: cues are searched in turn until one discriminates, then search stops and all other cues are ignored. Heuristics consist of building blocks, and building blocks exploit evolved or learned abilities such as recognition memory; it is the complexity of these abilities that allows the heuristics to be simple. Simple heuristics have an advantage in making decisions fast and with little information, and in avoiding overfitting. Furthermore, humans are observed to use simple heuristics. Simulations show that the statistical structures of different environments affect which heuristics perform better, a relationship referred to as ecological rationality. We contrast ecological rationality with the stronger claim of adaptation. Rules of thumb from biology provide clearer examples of adaptation because animals can be studied in the environments in which they evolved. The range of examples is also much more diverse. To investigate them, biologists have sometimes used similar simulation techniques to ABC, but many examples depend on empirically driven approaches. ABC's theoretical framework can be useful in connecting some of these examples, particularly the scattered literature on how information from different cues is integrated. Optimality modelling is usually used to explain less detailed aspects of behaviour but might more often be redirected to investigate rules of thumb.
适应性行为与认知中心(ABC)提出假设,认为许多人类决策可用简单的算法过程模型(启发法)来描述。本文解释了这种方法,并将其与生物学中关于经验法则的研究联系起来,我们也会对该研究进行综述。作为一个简单启发法的例子,考虑“取最佳”的词典编纂策略,即在两个选项之间进行选择时:依次搜索线索,直到找到一个能区分两者的线索,然后停止搜索并忽略所有其他线索。启发法由构建模块组成,而构建模块利用了诸如识别记忆等进化或习得的能力;正是这些能力的复杂性使得启发法能够简单。简单启发法在快速做出决策、只需少量信息以及避免过度拟合方面具有优势。此外,观察发现人类会使用简单启发法。模拟表明,不同环境的统计结构会影响哪种启发法表现得更好,这种关系被称为生态合理性。我们将生态合理性与更强的适应性主张进行对比。生物学中的经验法则提供了更清晰的适应性例子,因为可以在动物进化的环境中对它们进行研究。例子的范围也更加多样。为了研究这些例子,生物学家有时会使用与ABC类似的模拟技术,但许多例子依赖于实证驱动的方法。ABC的理论框架在连接其中一些例子方面可能会很有用,特别是关于如何整合来自不同线索的信息的零散文献。最优性建模通常用于解释行为中不太详细的方面,但可能更常被重新导向以研究经验法则。