This book contains a collection of essays discussing how intelligent creatures actually make decisions. The book presents a dichotomy of formal/standard approaches (e.g., multiple linear regression) and fast & frugal heuristics. The following comparison is a little unfair, but it should be useful for the less technical readers: traditional AI approaches are like being "book smart" and the heuristics discussed and defended in this book are like being "street smart."
Let me elaborate. Many standard approaches are radically different from how we, as intelligent beings, think. Many (most) formal decision making algorithms are based on machine learning approaches, which simply process the data, ignoring the data collection task. They are data hungry. Give them the perfect data set and they can break records (or beat players at the game called Go).
Alternatively, the fast and frugal heuristics take into account that information might be hard to come by in the first place. Moreover, these heuristics are much easier to follow in real life. They are based on three simple principles:
(1) Simple search rules,
(2) Simple stopping rules, and
(3) Simple decision rules.
The primary reasons one should care about fast and frugal heuristics is that they:
(A) Are ecologically rational, meaning they can be implemented with limited time, mental effort, and other resources,
(B) In many cases perform as well as more sophisticated algorithms, and
(C) Provide insight into real life intelligences.
The heuristics presented here are not necessarily going to win in top Artificial Intelligence (AI) competitions, but are still surprisingly useful. These heuristics are more biological-like AI.
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