Judgment Under Uncertainty: Heuristics and Biases

This is a compendium of the classic works that popularized and grew the field of judgment and decision making and behavioral economics. A must-read for graduate students and serious analysts.

I purchased and read this about five years ago. It is probably the most influential book I have ever read. It spawned an interest in a number of related books and on thinking in general. It is scholarly articles, but I found it to be quite readable--just a lot of detail. I agree with the reviewer who wished for a version aimed toward high school students.

If you read only one book on behavioral decision making, this is it. Thinking Fast and Slow is the abbreviated version. This is the original.

The essays contained in this book show convincingly that the standard decision theoretic model taught world wide since the mid 1940's,the subjective expected utility model based on the subjective approach to probability of Ramsey,De Finetti,and Savage,is not supported by the experimental evidence.The essays successfully show how the use of Prospect Theory accounts for the underweighting(subadditive-subproportional) and overweighting(superadditive-superproportional)of decision weights(non linear "probabilities").Three basic judgmental heuristic operations are preformed by decision makers in the real world.These operations are intuitive and based on the perceptions of the decision maker.The first heuristic that decision makers use in making probability evaluations is called representativeness.Judgments of probability are based on what is perceived as similar.The second heuristic is the availability heuristic.It,like the third heuristic,specifies that decision makers concentrate only on that evidence,upon which the probabilities will be estimated,that is most easily obtained or is immediately available.The third heuristic is called the anchoring heuristic.Decision makers use only that evidence that comes first.Tversky and Kahneman,as well as all of the other essay authors,argue that their experimental evidence demonstrates or shows that decision makers do not understand the mathematical laws of probability(additivity of probabilities,addition principle,multiplication principle,marginal probability,conditional probability,joint probability).They also do not understand basic statistical concepts(regression to the mean of a probability distribution). In 1921,in his A Treatise on Probability(TP),J M Keynes pointed out that the purely mathematical conception of probability was a very small subset of what he called the logical theory of probability.In order to apply the purely mathematical laws of probability correctly,a decision maker had to have a complete sample space of all possible outcomes specified in advance.An equivalent assumption is that the decision maker knows for certain what the particular probability distribution is.Secondly,probability preferences would have to be specified by a complete order that was linear or proportional.Any decision situation that did not satisfy these conditions had a weight of evidence less than one.Keynes specified a variable,w,called the weight of the evidence,that measured the completeness of the relevant,potential evidence that was available to the decision maker.It was defined on the unit interval between 0 and 1,just like Ellsberg's rho variable that would serve as a measure of the ambiguity of the evidence.The existence of ambiguity automaticaly will lead to violations of the purely mathematical laws of probability.Contrary to Kahneman and Tversky,Ellsberg,like Keynes before him,argued that these calculations are not erroneous and the decision makers are not irrational or biased.The claims made by Tversky, Kahneman and their many followers(Shiller,for example),that the subjects in their experiments are probabilistically and statistically illiterate,makes no sense because the problems that are presented to the experimental subjects do not allow the subjects to unambiguously define a unique probability distribution or a complete sample space of all possible outcomes(some examples are the blue-green taxi cab problem,the rare Asian disease problem,the battlefield problem,the Linda-bankteller problem,and the lawyer-engineer problem).Let us now turn to the representativeness heuristic.The representativeness heuristic turns out to be none other than Keynes's degree of similarity or likeness or resemblance discussed by Keynes in chapter 3 and Part III of the TP.The anchoring and availability heuristics are identical to the statement that the weight of the evidence is less than 1 for a real world decision maker.Keynes showed that decision makers would usually be able to use interval estimates(upper-lower probabilities) only.You automatically will violate the mathematical laws of probability,which only hold in the limiting case where w=1,given linear probability preferences.Keynes also showed this in his examples of his conventional coefficient of weight and risk,c.The answers obtained when one applies the c coefficient will be sub and super additive.Keynes ,however,would argue that these are not biases or errors,but correct calculations obtained with incomplete information.The vast majority of decision makers are attempting to reason probabilistically without the benefit of knowing a unique probability distribution,a complete sample space,or being able to specify a complete order over all outcomes.They are rational.Tversky and Kahneman are requiring "SUPERRATIONALITY".Every calculation of a probability estimate that does not have a weight of 1 or violates Carnap's rule of total evidence will violate the mathematical laws of probability.L J Cohen,repeating Keynes's argument,spent 20 years trying to get this point across to Kahneman and Tversky in the journal Brain and Behavioral Science(1975-1994) .Tversky's support theory is a belated attempt to remedy their omission,but it has not been successfuly integrated into Prospect theory(1979)or Cumulative Prospect theory(1992),where the weighting function is still a function of a single variable representing probability,although additional parameters have been incorporated into the model.One could argue that it is Tversky and Kahneman who are irrationally insisting that decision makers use the mathematical laws of probability in situations where those laws are not applicable.

Good book.

...and especially for the non-thinking person! This book, a popular classic for the past 25 years, opened my eyes to the prevalence and scope of biases in our thinking. The authors take great care to identify and define biases using rigorous, scientific measurements. Their experiments to uncover various types of biases are as fascinating as the end results. There were many occasions when I remembered or referenced this book in the last two decades. I have also recommended this book many times to anyone who expressed even a whiff of interest in decision-making, the workings of the human mind, psychology, etc.In fact, I think an abbreviated version of this would be perfect at the high school level. Some of these experiments (or a carefully modified version of them) can be performed by high school students, and it would be invaluable in teaching them (a) how to design experiments to uncover even abstract and subjective thinking patterns, (b) the prevalence and scope of biases, and (c) the power of the scientific method in general.Our schools most likely will not follow my recommendation (probability = 0.98). So, if you have a child in junior year or older and is 'intellectual' in outlook, or has expressed any interest in philosophy, psychology, law, etc., I strongly recommend that you give this book as a gift.

Of course, my humble opinion relative to Nobel award committee will hold little intrinsic value, other than a layman's interpretation and application.An economist myself, I found this book very interesting and educational to read. Although the book is quite verbose, the fluidity and organization of the content facilitates a smooth read - not a bludgeoning of the mind.I found this book particularly applicable to research in market behavior, systemic analysis (because this book outlines the individuals and how they act within the system); even policy development (uncertainty).I would recommend this book to anyone interested in psychology, social psychology, economics, policy, and politics.Regards,Tyler Markowsky

The lessons here are so profound, so sweeping, and so upsetting that even progressive, radical and philanthopic movements generally only vaguely recognize them.With detailed studies, this book spells out real trouble for humankind, because it demonstrates that even experts -- in a variety of fields -- don't understand quite simple issues about probability and risk.Even as someone who spent years managing and running projects in a NASA research center, the incapacity wasn't apparent to me, because we invest too much faith in experts' mastery of their field, and not enough questioning the essential abilities that they found their opinions upon.Potentially one of the most important books ever written.