In Adaptive Thinking, Gerd Gigerenzer follows up on his earlier book, The Empire of Chance. Part of his new book, Adaptive Thinking, is a continuation of his earlier discussion. Gigerenzer reacts against the dominance of significance testing, and looks closely at how it has shaped psychological theories. In particular, Gigerenzer places the "cognitive illusions" of Tversky, Khanneman, and many other behavioral scientists under very close scrutiny.

Gigerenzer's main thrust is that humans did not evolve in the psychology laboratory, with good command of probability theory to help them work on word problems. Instead, he argues, humans evolved in environments with lots of noise, and had to use regular features of the world to develop simple and effective rules of action. In this, he echoes and extends the work by economist Herbert Simon in the 1950s.

Take one of his examples: You live in Detroit. 1 in 100 new cars of brand X break down. 10 in 100 cars of brand Y break down. Your friend has car X, and it just broke down yesterday. Which should you buy? Well, clearly if you're "rational" you buy brand X. But consider:

You live in a jungle. 1 in 100 children is eaten by a crocodile while swimming in the river. 10 in 100 falls to their death while playing in the tree. Just yesterday, little Bobby was swimming and got eaten by a crocodile. Where should you let your kid play?

According to Tversky, Khanneman, and other modern cognitive scientists, you would be "irrational" to fear the river, since the long term probability of dying there is still only 2 out of 100.

If we evolved in the jungle situation, is it any wonder that most people rely on the advice of their friend in the car situation? Does this make them "irrational?"

Gigerenzer looks at the history of decision research, and offers a concrete and predictive program for the study of human rationality. The book is fairly short, very interesting, and casts serious doubt on many aspects of contemporary cognitive research. I recommend it to anyone with an interest in psychology or decision making, even non professionals.

Suppose we wanted to predict how an expert billiards player would hit a certain shot. We would measure the angles and distances, get the coefficients of elasticity of the balls and the bumpers, and we would solve a set of differential equations. But is that how the billiards player figures out what to do? Of course not! We don't know exactly what he would do, but if the authors of this book had their way, we'd give up on the differential equations (optimization theory) and find the "fast and frugal heuristic" actually used by the billiards player.

This book is the product of a conference of experts in the field. It includes wonderful contributions by the editors and their coworkers on how decisions are actually made, and argues persuasively that fast and frugal is almost as good as full optimization, and at much lower cost.

But the volume is a lot broader than that. It includes contributions on the role of emotions in decision-making (Dan Fessler), learning in animal societies (Keven Laland) and social insects (Thomas Seeley), and a lot of material on the role of culture in human societies (Boyd, Richerson, McCabe, Smith, Henrich, and others). This is important new material, very up to date.

Gigerenzter and Selten go to great lengths to cast aspersions on the old-fashioned "optimization subject to constraints" perspective, but their arguments are not persuasive. They make a category error: they maintain that models that use optimization assume that the agents the models describe use optimization. This is just silly. Just as the billiards player does not solve differential equations, decision-makers do not do complete optimization, even though we may use such models to describe their behavior.

The editors believe that optimization subject to constraints is dead in behavioral theory, but they're dead wrong. That's in fact what they are doing, but they prefer to call it "bounded rationality."

Finally, I should note that the work of Eduardo Zambrano (look up his home page) shows that the SEU (Subjective Expected Utility model---the enemy of all bounded rationalers) actually is behaviorally universal, in the sense that one can always find a set of Bayesian priors for which an observed set of behaviors is optimal.

But don't let these petty methodological issues get you down. The book is a great collection by the authors of major work in behavioral theory.

This is perhaps the best book at simply explaining the statistics of risk and uncertainty I have run across. I have even used what the author calls the illusion of certainty in analyzing the highest and best use of real estate. This book shows how medical experts and criminologists can be misled, not so much by innumeracy, as by what might better be called an illusion of expertise. Experts in any field may find this book useful in view of the U.S. Supreme Court's Daubert Rule that expert courtroom testimony must follow the scientific method.
A couple of caveats are in order however, and they are, shall we say, doozies. Gigerenzer states that there is ample evidence that smoking causes lung cancer. But he fails to consider why people from Asian and Pacific-Island cultures have some of the highest smoking rates in the world, but some of the lowest cancer rates. Any why do longitudinal studies show that people from these same cultures have much higher rates of cancer once they migrate to modern countries? Is it diet, smoking, a combination of the two, or something else that "causes" cancer? Likewise, Gigerenzer states that there is strong evidence that secondhand smoke is harmful to health. But he fails to mention the cardinal rule of toxicology: the dose, or concentration of a substance, makes the poison, not the substance itself. It is only in modern energy efficient air-tight buildings that smoke can be sufficiently concentrated so as to become an irritant, let alone a perceived health hazard. Thus, it may not be secondhand smoke, but the environment of tight buildings that is the source of the problem.
Thus, Gigerenzer fails to point out that all statistics and numbers must be actively interpreted and are relative in meaning to the interpreter. This involves a social filtering process not discussed in the book. Also, government may legitimate some health and crime statistics, when they may be bogus. As an aficionado of Gigerenzer's books, maybe he will write a sequel on the interpretation, misinterpretation, and social and political construction of statistics.

Gerd Gigerenzer has written several books dealing with "bounded rationality"--how humans use their brains to understand the world around them, make decisions, and determine the risks associated with a given course of action. This book is easily his most accessible. It is clear and easy to read, with most(but not all)the examples drawn from the field of personal health.

Gigerenzer provides the simple mental tools that allow anyone to make sense of the statistics that bombard us daily in the media. It is exactly his point that one does not need to be a rocket scientist (or professional statistician) to understand the numbers used by professionals, from personal physicians to DNA experts, that affect our lives and livelihoods.

If I could recommend only one book to address "numerical illiteracy," this would be it. You will learn some essential skills in a clearly informative and entertaining way.

Heading for a medical exam? Wonder if those uncomfortable, expensive tests really make a difference? Skip the medical libraries and talk to the statisticians.

Gigerenzer bares the truth that doctors conceal because of ignorance or greed. Every woman should read his chapter on the risks and benefits of mammograms. The rate of false positives for mammograms is a whopping ninety percent. The cost is not measured by x-ray charges alone (although a radiologist huffed out of a meeting with a gynecologist who stopped recommending mammograms -- they make big bucks from those tests!). Think of the unnecessary biopsies -- and the unnecessary surgery because biopsies have error rates too.

Cancer tests do not cure or prevent cancer. They may reduce the risk of death, although a comparison between screened and unscreened populations shows that very few lives are actually saved this way. And there is no risk reduction unless early detection affords access to a cure.

AIDS tests also carry risks. The rate of false positives among a healthy, "safe-sex" population is about fifty percent. The author describes horror stories of disease-free people who were mis-diagnosed. They lost jobs, homes and friends; some sued for recovery but at least one committed suicide.

Our health care system spends millions on tests because both patients and doctors are ill-informed. We demand a cure and the medical system finds a way to give us the illusion of progress.

It's not just the US. The author found ignorance of false positives for AIDS tests in Germany. When I lived in Canada, the provincial health system bombarded us with propaganda for mammograms.

Gigerenzer has done the world a great service by writing this book and presenting data in a reader-friendly fashion. I suspect there is a human tendency to look for certainty and today's medical tests seems to be the equivalent of divining rods and astrology of three hundred years ago. Now I wish he'd take a look at academic and career tests, most of which also give a form of "false positives." We'd like a yes or no in this world, but alas, mostly we have to learn to live with the maybes.

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"!