Book description<br>How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? In our book, "Simple Heuristics That Make Us Smart," we invite readers to embark on a new journey into a land of rationality that differs from the familiar territory of cognitive science and economics. Traditional models of rationality in these fields have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and an eternity in which to make choices. But to understand decisions in the real world, we need a different, more psychologically plausible notion of rationality. This book provides such a view. It is about fast and frugal heuristics-simple rules for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices, judgments, and predictions by employing bounded rationality.<br><br>But when and how can such fast and frugal heuristics work? What heuristics are in the mind's "adaptive toolbox," and what building blocks compose them? Can judgments based simply on a single reason be as accurate as those based on many reasons? Could having less knowledge even lead to systematically better predictions than having more knowledge? We explore these questions by developing computational models of heuristics and testing them through theoretical analysis and practical experiments with people. We show how fast and frugal heuristics can yield adaptive decisions in situations as varied as choosing a mate, dividing resources among offspring, predicting high-school drop-out rates, and profiting from the stock market.<br><br>We have worked to create an interdisciplinary book that is both useful and engaging and will appeal to a wide audience. It is intended for readers interested in cognitive psychology, evolutionary psychology, and cognitive science, as well as in economics and artificial intelligence. We hope that it will also inspire anyone who simply wants to make good decisions.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education