Todd P M, Gigerenzer G
Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, 14195 Berlin, Germany.
Behav Brain Sci. 2000 Oct;23(5):727-41; discussion 742-80. doi: 10.1017/s0140525x00003447.
How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we explore fast and frugal heuristics--simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data--that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program.
在一个知识有限、时间紧迫且深入思考往往是遥不可及的奢侈品的世界里，人怎么可能保持理性呢？认知科学、经济学和动物行为学中传统的无限理性和最优化模型倾向于将决策者视为拥有超凡的理性能力、无限的知识和无尽的时间。但要理解现实世界中的决策，需要一个在心理学上更合理的有限理性概念。在《使我们变聪明的简单启发式》（吉仁泽等人，1999年）一书中，我们探讨了快速节俭启发式——大脑适应性工具箱中利用现实心理资源进行决策的简单规则。这些启发式能够使生物体和人工系统通过利用特定环境中信息的结构方式，快速且以最少的信息做出明智的选择。在这篇概述中，我们展示了控制信息搜索、停止搜索和做出决策的简单构建块如何组合在一起形成各类启发式，包括：用于选择的基于无知和单一理由的决策、用于分类的排除模型以及用于序列搜索的满意启发式。这些简单启发式的表现与更复杂的算法相当，尤其是在推广到新数据时——也就是说，简单性带来稳健性。我们给出了关于人们何时使用简单启发式的证据，并描述了该研究项目需要解决的挑战。
