Simple Heuristics That Make Us Smart

People aren't computers. Human beings live in a real world of scarcity and constraint. Even though time and information may be scarce, human beings must make high-stakes decisions. Probability and logic offer models for the thought process of choosing between alternatives, but decision makers often do not have enough hours, data and skill to use these sophisticated approaches. Fortunately, some rough and ready cognitive shortcuts perform as well as or better than the most elaborately sophisticated models - at least in the real world context of limited information and time. Working with the ABC Research Group, authors Gerd Gigerenzer and Peter M. Todd explore some of those shortcuts, called "heuristics." They discuss in length and depth a series of experiments that demonstrate the value of heuristics. This is not light reading. It requires a level of comfort with academic style, mathematics and symbolic logic. Readers unfamiliar with cognition literature may find it a struggle - but we believes that those who persevere will find enough new insight to make the effort worthwhile.

Whether you like a book depends on what information you're looking for. i make computer models of human behavior so this book, which is easy to read but filled with concrete solutions and lots of supporting dat, was near-perfect for meAs a note, i'm picky when it comes both to writing and thinking. And i hate most books written by academics. Even the ones with good information (eg, Fodor's Modularity) are hard to read and filled with confusing, field-specific words. Not this book. It's really well written. Written in plain English, very few assumptions, very thorough analysis, lots of self-criticism, lots and lots of data (OK, that part is boring and can be skipped, but it's comforting to know it's there)What's it about? Common AI, psych and economic decision and learning algorithms (decision trees, neural nets, Bayes, multiple linear regression, etc.) are compared to several absurdly simple algorithms the authors believe real humans use. The various approaches are compared and evaluated on the basis of performance, accuracy on training data, accuracy on test data (generalization) and amount of input data required. Tests are on the standard UC Irvine data learning test sets. Comparisions, outcome explanations and relevance to the human mind and the real world are provided. Explanations and analysises are easy to understand and pretty convincingi've decided to use a lot of what was in this book in my software, things that have made my agents more natural and easier to implement. i absolutely love this book

It's really meant for a technical audience since this stuff is so cutting edge, but you shouldn't wait till the results appear in Time magazine. The experiments and writing are very easy to understand, very clear. And you will be amazed by the simple ways in which our brain takes shortcuts in reasoning -- both making it stupid and making it smart. Be careful next time you try to reason using probabilities, you're better off using frequency. My own background is in philosophy, where this type of work has been very important in undermining the assumption that humans are rational. We aren't. You should probably read Kahnemann and Tversky's books before coming to this though, since this work adds an interesting spin to the old irrationality debate: maybe some of it is GOOD for us!

As someone interested in the practice and theory of decision making, I came upon this book via a number of "listmania" lists that reccomend it. The first few chapters got me excited about the subject matter. The authors promised to present a new model for decision making, one that was simple, and one that works.The ensuing pages compare several theoretical models, such as Multiple Linear Regression and Dawes Rule to their own Take the First and Take the Best models.Most of the tests were simulated on a computer. You would feed each decision making model into the computer, and then feed in various data for it to make decisions on. One popular test is "Which is the most populated German City." The computer had data on various German Cities with populations over 100,000. It also had several indicators, such as whether it has a soccer team, or a rail system, or is a state capital. The system would present two cities, with the indicators, and the decision making model would figure out which was the most populous one.Right now I'm in a chapter called "Bayesian Benchmarks for Fast and Frugal Heuristics." It's about halfway through the book, and I'm not sure I'll finish. While the second half sounds interesting, this book is highly academic and the authors are concerned with presenting proofs for everything they say, in detail. Sort of like a victorian novel that starts of by telling you what it's going to tell you, and then tells you several times. I may skim it because I do find the subject matter intereting.I certainly don't regret buying this book, having mathematical models for decision making is certainly handy (as someone interested in AI), but I wouldn't call it light reading, nor would I reccomend it to a manager interested in the decision making process.I found much more interesting "Sources of Power" by Gary Klein. Indeed, I consider Sources of Power to be one of the most informative and most entertaining books I've ever read, and wish more like it existed.In summation, I found this book to be highly academic and theoretical. If you are a human being interested in the decision making process as it is carried out by humans, I reccomend the more hands-on Sources of Power by Gary Klein. If you are interested in simple, statistical models for decision making (the kind you can teach a computer), then pick up this book.

This book introduced me to an exciting new way of thinking about decision making. "Heuristics" is basically just a fancy word for "rules of thumb" and the book shows convincingly how simple rules of thumb can go a very long way.The 18 authors from various academic fields believe that decision rules and the environment in which they are used should always be considered together. Moreover it seems plausible that a simple rule which performs as well as a rule that requires more effort to apply, should be the preferred way of explaining the observed behavior.The authors propose a bunch of simple heuristics for all kinds of problems. One particularly impressive example was the extremely simple "recognition heuristic" which e.g. proved to be quite successful on the stock market. For all heuristics in the book it is shown that they are easy to use, that they require little memory and computational capacity, and that therefore they appear to be very plausible models for explaining human (and animal) behavior.If you are interested in decision making and/or if you are working in the fields of psychology, economics, artificial intelligence or related fields, this book is a "must-have"!