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This book, first published in 2002, compiles psychologists' best attempts to answer important questions about intuitive judgment.
Simple Heuristics That Make Us Smart invites readers to embark on a new journey into a land of rationality that differs from the familiar territory of cognitive science and economics. Traditional views of rationality tend to see decision makers as possessing superhuman powers of reason, limitless knowledge, and all of eternity in which to ponder choices. To understand decisions in the real world, we need a different, more psychologically plausible notion of rationality, and this book provides it. It is about fast and frugal heuristics--simple rules for making decisions when time is pressing and deep thought an unaffordable luxury. These heuristics can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality. But when and how can such fast and frugal heuristics work? Can judgments based simply on one good reason be as accurate as those based on many reasons? Could less knowledge even lead to systematically better predictions than more knowledge? Simple Heuristics explores these questions, developing computational models of heuristics and testing them through experiments and analyses. It shows how fast and frugal heuristics can produce adaptive decisions in situations as varied as choosing a mate, dividing resources among offspring, predicting high school drop out rates, and playing the stock market. As an interdisciplinary work that is both useful and engaging, this book will appeal to a wide audience. It is ideal for researchers in cognitive psychology, evolutionary psychology, and cognitive science, as well as in economics and artificial intelligence. It will also inspire anyone interested in simply making good decisions.
Heuristics are strategies using readily accessible, loosely applicable information to control problem solving. Algorithms, for example, are a type of heuristic. By contrast, Metaheuristics are methods used to design Heuristics and may coordinate the usage of several Heuristics toward the formulation of a single method. GRASP (Greedy Randomized Adaptive Search Procedures) is an example of a Metaheuristic. To the layman, heuristics may be thought of as ‘rules of thumb’ but despite its imprecision, heuristics is a very rich field that refers to experience-based techniques for problem-solving, learning, and discovery. Any given solution/heuristic is not guaranteed to be optimal but heuristic methodologies are used to speed up the process of finding satisfactory solutions where optimal solutions are impractical. The introduction to this Handbook provides an overview of the history of Heuristics along with main issues regarding the methodologies covered. This is followed by Chapters containing various examples of local searches, search strategies and Metaheuristics, leading to an analyses of Heuristics and search algorithms. The reference concludes with numerous illustrations of the highly applicable nature and implementation of Heuristics in our daily life. Each chapter of this work includes an abstract/introduction with a short description of the methodology. Key words are also necessary as part of top-matter to each chapter to enable maximum search engine optimization. Next, chapters will include discussion of the adaptation of this methodology to solve a difficult optimization problem, and experiments on a set of representative problems.
All of use heuristics - that is, we reach conclusions using shorthand cues without utilizing or analyzing all of the available information at hand. Here, Kelman takes a step back from the chaos of competing academic debates to consider the wealth of knowledge that a more expansive use of heuristics can open up.
This introduction to the field of hyper-heuristics presents the required foundations and tools and illustrates some of their applications. The authors organized the 13 chapters into three parts. The first, hyper-heuristic fundamentals and theory, provides an overview of selection constructive, selection perturbative, generation constructive and generation perturbative hyper-heuristics, and then a formal definition of hyper-heuristics. The chapters in the second part of the book examine applications of hyper-heuristics in vehicle routing, nurse rostering, packing and examination timetabling. The third part of the book presents advanced topics and then a summary of the field and future research directions. Finally the appendices offer details of the HyFlex framework and the EvoHyp toolkit, and then the definition, problem model and constraints for the most tested combinatorial optimization problems. The book will be of value to graduate students, researchers, and practitioners.
Experts in law, psychology, and economics explore the power of "fast and frugal" heuristics in the creation and implementation of law In recent decades, the economists' concept of rational choice has dominated legal reasoning. And yet, in practical terms, neither the lawbreakers the law addresses nor officers of the law behave as the hyperrational beings postulated by rational choice. Critics of rational choice and believers in "fast and frugal heuristics" propose another approach: using certain formulations or general principles (heuristics) to help navigate in an environment that is not a well-ordered setting with an occasional disturbance, as described in the language of rational choice, but instead is fundamentally uncertain or characterized by an unmanageable degree of complexity. This is the intuition behind behavioral law and economics. In Heuristics and the Law, experts in law, psychology, and economics explore the conceptual and practical power of the heuristics approach in law. They discuss legal theory; modeling and predicting the problems the law purports to solve; the process of making law, in the legislature or in the courtroom; the application of existing law in the courts, particularly regarding the law of evidence; and implementation of the law and the impact of law on behavior. Contributors Ronald J. Allen, Hal R. Arkes, Peter Ayton, Susanne Baer, Martin Beckenkamp, Robert Cooter, Leda Cosmides, Mandeep K. Dhami, Robert C. Ellickson, Christoph Engel, Richard A. Epstein, Wolfgang Fikentscher, Axel Flessner, Robert H. Frank, Bruno S. Frey, Gerd Gigerenzer, Paul W. Glimcher, Daniel G. Goldstein, Chris Guthrie, Jonathan Haidt, Reid Hastie, Ralph Hertwig, Eric J. Johnson, Jonathan J. Koehler, Russell Korobkin, Stephanie Kurzenhäuser, Douglas A. Kysar, Donald C. Langevoort, Richard Lempert, Stefan Magen, Callia Piperides, Jeffrey J. Rachlinski, Clara Sattler de Sousa e Brito, Joachim Schulz, Victoria A. Shaffer, Indra Spiecker genannt Döhmann, John Tooby, Gerhard Wagner, Elke U. Weber, Bernd Wittenbrink
This tutorial-based approach, born out of the author's extensive experience developing software, teaching thousands of students, and critiquing designs in a variety of domains, allows you to apply the guidelines in a personalized manner.
Most textbooks on modern heuristics provide the reader with detailed descriptions of the functionality of single examples like genetic algorithms, genetic programming, tabu search, simulated annealing, and others, but fail to teach the underlying concepts behind these different approaches. The author takes a different approach in this textbook by focusing on the users' needs and answering three fundamental questions: First, he tells us which problems modern heuristics are expected to perform well on, and which should be left to traditional optimization methods. Second, he teaches us to systematically design the "right" modern heuristic for a particular problem by providing a coherent view on design elements and working principles. Third, he shows how we can make use of problem-specific knowledge for the design of efficient and effective modern heuristics that solve not only small toy problems but also perform well on large real-world problems. This book is written in an easy-to-read style and it is aimed at students and practitioners in computer science, operations research and information systems who want to understand modern heuristics and are interested in a guide to their systematic design and use. This book is written in an easy-to-read style and it is aimed at students and practitioners in computer science, operations research and information systems who want to understand modern heuristics and are interested in a guide to their systematic design and use. This book is written in an easy-to-read style and it is aimed at students and practitioners in computer science, operations research and information systems who want to understand modern heuristics and are interested in a guide to their systematic design and use.
Thirty-five chapters describe various judgmental heuristics and the biases they produce, not only in laboratory experiments, but in important social, medical, and political situations as well. Most review multiple studies or entire subareas rather than describing single experimental studies.
Creative solutions are easily recognizable, after they have been created. But how to attain them? This book is about a promising approach to creative problem solving - the use of heuristics. The main purpose of an heuristic is to make problem solving more efficient, by making past experience - which could guide the generation of new solutions - promptly available. The heuristic approach is widely used in TRIZ (the Theory of Inventive Problem Solving), which is becoming increasingly popular worldwide. Successful results of using heuristics have been reported by companies such as ABB, Bosch, General Motors, Ford, Mitsubishi, Philips, Siemens, among others. With this book, the reader will be able to: - Understand the 121 Heuristics for problem solving, both from their descriptions and from selected examples; - Find the more promising Heuristic(s) for the solution of his/her problems; - Apply the heuristics and find creative solutions to his/her problems.