Reviews (2)

my humble opinion
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

Reviews (7)

A shocking ignorance of the contributions of JM Keynes
This collection of 42 essays,most of which were first published as articles in academic journals ,demonstrates a shocking amount of ignorance on the part of the essay authors concerning thetechnical contributions made by J M Keynes to decision theory in his 1921 book,A Treatise on Probability(TP).This is most likely due to the great influence on economists and philosophers of the error filled reviews of the TP made by Frank P Ramsey in 1922 and 1926.Ramsey based his review of the TP primarily on chapters 1-4 of the TP plus several pages taken at random from the other four parts of the TP.Ramsey totally misread and misinterpreted Keynes's initial,elementary discussion of the measurement of probabilities in chapter 3 of the TP.Ramsey misinterpreted the meaning of the terms"nonnumerical" and "nonmeasurable" to mean that Keynes was arguing that it was ,in general,not possible to use numbers to quantify the probability relationship.Thus,Ramsey concluded in both essays that Keynes's nonnumerical probabilities were "mysterious"entities that did not satisfy the probability calculus.Exactly the opposite is the case.Keynes warned the reader of chapter 3 of the TP on p.37 that only after Part II of the TP had been covered would Keynes's approach to quantification be complete.In chapters 15 and 17 ,as well as in chapters 20 and 22,Keynes made it quite clear that by nonnumerical he meant not by a single numeral(number).Thus,it took TWO numerals(numbers)to estimate a probability in general.Keynes is the founder of the interval estimate approach to probability.Thus,nonnumerical is defined explicitly to mean"...between numerical limits."on page 160 of the TP.The reader should note that Keynes emphasized the word "between".Keynes called his approach "inexact numerical APPROXIMATION".Keynes is the founder of the interval estimate approach to probability.A reader of this book,however,might not even know that Keynes ever lived.Keynes is the first scholar in history to specifically define an index,w, to measure the weight of the evidence(what Ellsberg called the ambiguity of the evidence and Savage called the vagueness of the evidence).On p.315 of the TP ,w is defined to lie on the unit interval [0,1].Thus,0<=w<=1.This is 40 years before Ellsberg defined rho on the same unit interval.Keynes developed a decision formula that incorporated variables that allowed a decision maker to include w(or rho)and nonlinear probability preferences(nonadditive sub and super probabilities)into a conventional coefficient of weight and risk,c.The use of the c coefficient solves all of the paradoxes of subjective expected utility theory as well as accounting for all of the effects discussed over the years by Kahneman and Tversky,such asthe certainty,reflection,isolation and preference reversal effects.Keynes would certainly agree with essay 11 by M Rabin,titled"Diminishing Marginal Utility of Wealth Cannot Explain Risk Aversion",since this exact conclusion is derived by Keynes on p. 315 of theTP. Despite the glaring ommission of any discussion of Keynes's contributions to decision theory,I still recommend that a specialist buy this book.It contains many valuable articles.

Classic JDM reading
This should be the foundation of anyone's Ph.D. reading list in IO Psych or Judgment and Decision Making.Most of the classics.

SIGNIFICANT ADVANCES IN ECONOMICS THAT LED TO NOBEL PRIZE
Kahneman and Tversky's compilation of articles in this book is an outstanding exposition of recent advances in cognitive psychology, especially advances associated with prospect theory.The work presented in this volume is largely responsible for the authors being awarded the Nobel Prize (Tversky died before receiving it).

The text is somewhat dense at parts, being aimed at economists and psychologists with some mathematical familiarity.However, the portions of the book that require much mathematics can safely be bypassed without losing much of the substance of the text.This text is the most credible presentation of an alternative theory to the rational actor theory usually assumed in economics.For example, some of the articles help explain the magnitude of the equity return premium, or help show how people make choices differently in similar situations based simply on the way the situation is presented.

Reviews (10)

Does not answer "How to make better decisions"
After reading this book, your ability to make better decisions will improve a whopping zero. If you 1)want to learn how to make better decisions 2)like to save money because it is precious 3)never enjoyed that Psych 101 course in college despite upper classmen swearing you into believing you will learn the tricks to get the hottest babes, then do not buy this book. On the other hand, if you were a geek like me and enjoyed the self torture in order to obtain intellectual enlightment, then this book is definitely for you. This book will cites what seems like 2 million case studies to show you that humans cannot make right decisions worth crap. I kept reading this book to find the "how to" section on making right decisions, but there was none, making my decision to buy and read this book a wrong one.

Excellent and insightful.
This is a fascinating book analyzing how we are all far less Cartesian than we think.In other words, a slew of predictable human bias flaws what we feel is our own objective judgment.The author eminently demonstrates this point by forcing the reader to take a 39 questions test at the beginning of the book.This test is stuffed with all the traps that illustrate the human judgment flaws that he analyzes thoroughly in following specific chapters.

You can view the test as a very entertaining IQ test from hell.The questions seem often simple.But, they are not.Other times, they are obviously difficult.I got a bit more than half of them correct.This was mainly because I had some knowledge or experience regarding certain traps the questions presented.I had made the mistake before.So, I learned from that.When I did not have any prior knowledge of a question, my results were very human, meaning not that good.But, learning the correct answer was both fun and educating.

The author touches on several fascinating probability and statistic concepts.One of them being the Bayes theorem, which suggests that medical screen test can be highly unreliable despite being touted as 80% to 90% accurate.In other words, you better understand the Bayes theorem better than the medical specialists who screen you for various diseases.Because, based on the author's study, doctors don't have a clue.Another chapter had an excellent discussion on correlation vs. causation.This includes some tricky nuances that many analysts in the financial industry trip upon.Another interesting probability concept is why it takes only 23 people in a room to have greater than a 50% that two of them share the same birthday.This seems impossible, but it is true.

The book has obviously a lot more than I am letting on here.I am not going to ruin it for you.It is really fun, educating, and interesting to read.You will also learn a whole lot about how you think, how others think, and how people think in groups.You will also understand how tricky it is to ask truly open and objective questions.Also, polls that seem objective are not due to the subjective structure of the question.I think you will enjoy this book, and I strongly recommend it.

Reviews (3)

The logical theory of probability is not the classical th.
This book is badly marred by the author's failure to carefully differentiate between the classical theory of probability,which was founded in the 17th century,and the logical theory of probability,which J M Keynes started early in the 20th century.Rudolf Carnap made additional contributions in the mid-1950's to the logical theory of probability. In this reviewer's opinion,pp.93-103 contain so many errors of both omission and commission that a novice reader will come away from reading this book with serious gaps in his understanding of what probability means and how it is applied in the real world.The fact that Baron is a strong supporter of the Ramsey-de Finetti subjectivist approach does not justify the very misleading appraisels of the relative frequency and logical theories of probability in his book. Contrary to Baron,Carnap's approach makes use of all the available evidence,including statistical data and relative frequencies.Carnap's approach also makes use of the betting quotients technique used by subjectivists.It simply is not true that"Likewise,the logical view is ordinarily useless as a justification for making probability judgments,except in textbook(or casino)cases of"fair coins"....".The second major draw back of this book is the author's strong belief that all probabilities are precise,exact,definite point estimates.Baron apparently believes that there is always sufficient information to represent the probability by a unique single number . (see Baron,pp.94-96). Baron appears to be completely ignorant of J M Keynes's interval valued approach or of the work of Walley ,Levi,Kyburg and The Society for Interval Valued Probability Theory and Application(SIPTA) in reviving Keynes's long dormant approach.

Decisions, decisions, decisions
This is a _very_ impressive book. I can't imagine a more thorough overview of the study of human judgment, decisionmaking, and rationality.

It's hard to find a good single volume in this field. For the psychology of judgment and decisionmaking, there's Scott Plous's excellent book of that title. For the heuristics on which we seem to rely and the biases they seem to generate, there's the modern classic _Judgment Under Uncertainty_ by (the late) Amos Tversky and (recent Nobel prize winner) Daniel Kahneman. And there are other books devoted to special topics and subtopics.

But so far as I know, this is the only broad, general introduction to the entire field of thinking, how we do it, how we probably ought to do it, and the sorts of things we do it about.

The book is divided into three broad sections. "Thinking In General" covers just that: what thinking is, and the nature of rationality and logic. "Probability and Belief" introduces not only probability theory(including, importantly, Bayes's Theorem) but the various theories about what probability _is_, as well as hypothesis testing, correlation, and "actively open-minded thinking." The largest section, "Decisions and Plans," is eleven chapters long and covers everything else: uncertainty, utility, decision analysis, theories of morality fairness, and justice, and risk.

As you might imagine, Baron doesn't leave too many stones unturned. Heck, his bibliography alone is thirty-odd pages long.

The book is eminently readable. The third edition is aimed mainly at graduate students but I gather Baron still uses it in his undergraduate courses. If you're a lay reader interested in this field, you'll be able to read it on your own with no difficulty.

Reviews (2)

closer than life.
a book which should be a part of our daily educatio

Reviews (2)

A Review of Decision Theory as Philosophy
Decision Theory as Philosophy is good as far as it goes but
like so much of utility theory and conventional economics it
assumes that utility is a scalar. In fact human values can
not be described by a single scalar quantity (see The Sources
of Value by S. C. Pepper, U. Calif. Press, 1958).Values and
utility are at least vector quantities (see Multiple Criteria
Optimization by R. E. Steuer, Wiley and Sons) having multiple
roughly orthogonal components. This is why capitalism is at
odds with real human values.

Reviews (4)

The Standard First Text To Begin Studying Bayesian Methods
This is an extremely nice introduction to Bayesian statistical methods.It takes you from the very basics - even who Thomas Bayes was (who happens to be buried in Bunhill Fields cemetery in London with William Blake (Songs of Innocence and Experience, Jerusalem), Daniel Defoe (Robinson Crusoe), John Bunyan (Pilgrim's Progress)).

Its chapters are divided into sections forming an Introduction, Foundations, Generalizations, Modeling, Inference, and Remodeling.There is also a section summarizing the basic formulae and alternative non-Bayesian approaches.A rich reference list, subject index, and author index are also provided.

If you are familiar with the math of undergraduate statistics you should not have a problem with the math notation in this book.This really is the standard text you find on most shelves of folks who are familiar with this subject.There are many books to read beyond this one, but this is a fine place to start.

the new bible for Bayesian inference
Recently there have been a wealth of good books published on Bayesian methods and the Markov chain Monte Carlo approach to its implementation.For the beginner Berry's introductory book is a good place to start.

Bernardo and Smith are experts in the field who have participated in many of the Bayesian conferences held in Valencia and much of that lterature is contained in this book.They originally wrote the book in 1993 (with a publication date of January 1994).This paperback edition is not a revision but rather a reprinting with corrections.The original hardcover edition was very expensive and this paperback edition makes the text more affordable and should greatly expand the list of Bayesian specialists and other statisticians and practitioners that read it.

The authors intent was to extend the classical work of Bruno deFinetti who popularized the Bayesian approach with his two classic probability books.One of the authors was involved in translating deFinetti's books into English and they are both well familiar with it.In this book they offer an extension to the area of statistical inference.

The beauty of deFinetti is the logical and systematic nature of the presentation but he did not extend this to statistical practice.These authors maintain the systematic approach and review the probability axioms but then go on to cover statistical modelling including how models are approached through concepts of exchangeability, invariance, sufficencyand partial exchangeability.The chapter on inference covers the Bayesian paradigm, the use of conjugate families, asymptotic methods, multiparameter problems and the thorny issues with nuisance parameters. It also includes a number of methods of numerical approximation including Markov chain Monte Carlo (MCMC) methods.

The authors deliberately left the coverage of computational methods brief as they planned a second volume to cover it in detail.But in the preface to the new paperback edition they admit that they have abandon this plan due to the evolution of MCMC methods as the dominant numerical approach and the wealth of new texts that adequately cover the topic.

I suggest that this text is the new bible for Bayesian statistics because I think it replaces the old bibles, Lindley's two volumes (some may argue for Savage's book).This is fitting as both authors attest to being students and disciples of Dennis Lindley.The reason I think it is worthy of bible status is because it covers the foundations in systematic detail, is current and very complete.The text contains references from 1763 (Bayes' original treatise) to 1993 covering an incredible 66 pages of the text.With 20 plus references per page that means over 1320 references!

This is an intermediate level text that requires advanced calculus but not measure theory.Emphasis is on concepts and not mathematical proofs.The authors also provide an overview of the non-Bayesian forms of statistical inference in Appendix B.The authors confront the controversial issues in each chapter.Bayesian statistical methods are treated in the framework of decision theory and ideas from information theory take on a central role.

Reviews (4)

Cox understood Keynes better than Ramsey and de Finetti but
Cox understood Keynes's logical approach to probability better than either Ramsey or de Finetti but ultimately fails to improve substantially on Keynes's work due to his overlooking what the fundamental nature of probability is in Keynes's general system.For Keynes probabilities are generally not precise single number answers.Probabilities are indeterminate,inexact,imprecise or indefinite interval(set)estimates that are represented by intervals.These intervals require two numbers to specify the probability relation.Single number answers require symmetry of evidence or the Principle of Indifference(POI).Contrary to Cox ,the POI is not a latent frequentist principle.Cox also fails to recognize that Keynes's primary axiomatic treatment on pages 135-138 of the A Treatise on Probability(1921) of addition and multiplication is an attempt by Keynes to provide an AXIOMATIC TREATMENT THAT WOULD HOLD FOR BOTH PRECISE AND IMPRECISE PROBABILITIES.In chapter 8 of the TP,Keynes provides an axiomatic treatment for the relative frequency interpretation of probability,while in chapter 15,Keynes provides additional axioms needed to treat probability as precise estimates(single numbers) only.Finally,Cox has overlooked Keynes's extension of his discussion of the logical concept of the weight of the arguments,in chapter 6,to Keynes's mathematical counterpart in chapter 26,which Keynes calledthe weight of the evidence.Keynes then defined an index for weight of evidence and incorporated it into his decision rule c,called a conventional coefficient of weight and risk.Keynes's approach is superior to the maximum entropy approach since this approach only applies to statistical evidence alone.(See Cox,ft.17,pp.104-105).

Degrees of belief as an extension of Boolean logic
This is a great, great book that I'm absolutely ecstatic to see back in print. I was introduced to it when I was in graduate school (mathematics) and rooming in the house of a physics professor who swore by Richard Threlkeld Cox's account of subjective probability. I haven't had a copy of it in my hand for nearly twenty years; I happened across this page today and ordered it at once. So pardon me while I gush:

What Cox accomplishes in this deceptively slim volume is amazing. He places Bayesian probability theory on an axiomatic foundation, as a natural extension of Boolean logic, identifying probabilities with degrees of subjective belief in propositions rather than directly with frequencies of events (though he also argues that the subjectivist interpretation accords with the frequentist interpretation whenever the latter makes sense at all).

Essentially, he shows that the ordinary laws of probability theory are normative laws of thought that apply to degrees of belief in propositions, and that we have to conform to them if we want to think consistently.

If you like math and logic books, you'll find this one eminently readable; I haven't seen it in years and yet I still remember the stunning clarity of Cox's rigorous exposition.

This is the book that originally sold me on Bayesianism. If you have any interest in this subject at all, grab this one while it's available.

Like a ten-pound textbook, in only 130 pages
This book contains the fundamental argument justifying Laplace's original theory of probability. Laplace justified his theory by basic intuitive considerations, which left it open to attack on philosophical grounds. Here, R. T. Cox shows how Laplace's theory is the logical consequence of two very simple, almost unavoidable axioms.

Cox begins the book by discussing his axioms, and then expressing them as functional equations. The solution of these functional equations develops the theory to the point at which Laplace began his own development.

(In general, the probability of a proposition is conditional on the truth of some other proposition. An item of particular interest here is that while most Bayesian expositions call this a priori true proposition "prior information", Cox calls this proposition the "hypothesis". This term seems to me to be more sensible, because we are rarely absolutely certain about our prior information. We take our "prior information" to be true, not because we are certain it is true, but as a conjectural point of departure for the subsequent calculation.)

Cox continues the development of the theory by relating the notion of probability to information entropy. He gives a definition for systems of propositions and shows how entropy is related to the uncertainty as to which of the propositions in the defining set of the system is true. (By hypothesis, at least one proposition in the defining set is true.)

Cox finishes the book with a section on expectation. He shows here how the theory he has developed encompasses all of the standard results of expectations found in other theories of probability.

Reviews (2)

No,Keynes never changed his mind about partial prob. orders
Jeffreys(J)does an excellent job in laying down a foundation for statistical inference based on a logical theory of probability.However,(J)appears not to have carefully read Keynes's A Treatise on Probability(1921),especially chapters 8,29,30,31 and 32.J appears not to have understood the generality of Keynes's approach.In the physical and life sciences,where one is dealing with evidence which is homogeneous and where every particle,electron,cell,molecule,gene or chromesome,etc.,is identical or practically identical to every other particle,electron,etc.,the only relevant evidence is statistical or frequency evidence.In such cases,Keynes's logical approach will give the same answer as Jeffreys would give.Keynes gave two other useful suggestions that were overlooked by J.The first was that the data pass a Lexis Q test for stability(satisfy the law of large numbers strictly).The second was Keynes's recommendation about using the Chebyshev Inequality as a lower bound on statistical estimates if the required assumptions necessary to assume anormal probability distribution were not met.J never understood that the very general axiomatic foundation that Keynes laid out in Chapter 12 of the TP applied to both precise and imprecise(partially ordered )probabilities.Keynes never claimed that the probabilities of scientific endeavor were partially ordered.Keynes did recognize,however, that the probabilities of the social sciences,liberal arts,economics,business,education and every day practical decision making were,in general,partially ordered.Finally ,J's claim ,in his introduction ,that Keynes withdrew his claim that most probabilities are inexact and indefinite,requiring two real numbers to specify the probability relation instead of one,in a 1931 New Statesman and Nation article reviewing Ramsey's collected essays,does not have a shred of evidence to support it.This type of offhand commentmakes no sense as it would require Keynes to give up his general logical theory of probability in order to accept Ramsey's very special and narrow theory of probability.

Reviews (2)

L J Savage was never able to deal technically with ambiguity
John Maynard Keynes's A Treatise on Probability(1921;TP)was given a five star rating by this reviewer because he dealt clearly and succinctly with what Ellsberg later called "ambiguity".Keynes did not use the terminology of Ellsberg.In the TP Keynes analyzed the problem of ambiguity using the term "weight of the evidence".In the General Theory(GT)Keynes used the term"uncertainty".Keynes defined an index to measure the weight of the evidence,denoted by the variable w.By definition,0<=w<=1(See p.315,TP,chapter 26).Keynes then incorporated w into a decision rule which he called a conventional coefficient of weight and risk c.Given outcome A,the decisionmaker's goal is to maximize cA,as opposed to pA or pU(A),where p equals a probability(p+q=1)and U(A)is a utility function.c=p(1/(1+q)[2w/1+w].Savage did discuss the problem of ambiguity or uncertainty or lack of evidential weight using the term vagueness.Unfortunately,after discussing the problem of vagueness,Savage decided not to deal with this problem technically.Savage's technical analysis of decision making under risk is certainly very interesting.However,his failure to deal with the problem that Keynes had already solved in his TP means that I can only give Savage four stars.

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Reviews (5)

Bayes' Theorem made simple
This is an excellent tutorial for the both the beginner (undergraduate) and more advanced scientist.Sivia takes the reader through several examples with simple and conciseexplanations.I have used many of the examples discussed in the book as starting points for problems that I have encountered in my work.I would recommend giving it a try...

Learn what it means to be a "Bayesian"
For years I listened to people present "Bayesian" solutions to problems without appreciating the subtler implications of the term.Bayes' theorem is one of the first topics taught in freshman-level probability and statistics.It's taught, and it's used, but it isn't a central part of the teaching of modern statistics.

Bayesians make it central.Sivia does a masterful job of deriving most of statistics from judicious applications of Bayes' theorem.He can do this, in part, because the visible universe is finite.Infinities and limit theorems can be bypassed, and previously impossible functional forms become workable.

The book is a tutorial; you have to think.But it's well worth it.

Reviews (6)

Good writing style
I am in my first semester of an MBA program.The required text for my OR/MS class (An Introduction to Management Science, 10th ed., by Anderson, Sweeney, Williams) left something to be desired.I work with the SAS programming language, and happened to be discussing the SAS/OR product with a SAS technical support rep.He recommended this Winston text.I think it explains concepts much better than my Anderson text and has helped me a lot in my OR/MS class.

From Deterministic to Stochastic
This is one of the excellent industrial engineering books. From deterministic and simple linear programming to stochastic systems such as queueing models or dynamic programming, you may read and "learn" lots of things about OR. However, for stochastic cases you will need extra help for its matematics. But if you want to learn something about industrial engineering or something related with it, that is the book you must buy.

Isbn: 0486638316
Sales Rank: 438838
Subjects:  1. Game Theory    2. Mathematics    3. Probability & Statistics - General    4. Statistical decision    5. Mathematics / General   

Reviews (3)

This was the textbook used by John Nash......
in his course in Game Theory (M711!) at MIT in the late 1950's.
I took that course; while Nash was unquestionably brilliant, he was getting to be pretty hard to follow at that point.The lecture hall was always jammed to overflowing, because even on a bad day Nash was really something!Nevertheless, the book was subsequently very useful, with lots of ideas about game-theoretic approaches to real-world problems.

Nash didn't think too highly of this book (too much non-mathematical stuff), but thought it the best available at the time not written by his arch-enemy, Von Neumann!

A Great Read
This overview of game theory and decisions is a great into the problems and ideas behind game theory.I expect that this book will be most appreciated by non-math Ph.D.'s or grad students.For a math person, VonNeumann and Morgenstern's classic title is perhaps a better place to start. This book is one of those that can be read on a range of levels.I workin a trading and risk management environment and I find this book veryuseful.

Reviews (2)

An Obvious Classic But . . .
Let me start out by acknowledging that this is THE BOOK that started Game Theory as it exists today.While Bayesian statistics are an obvious precursor, everyone agrees that von Neumann's and Morgenstern's work was ground breaking.

That said, this is not the best written Game Theory text out there.Like all seminal works, it suffers from the basic fact that we've learned a lot of new things since the time it was written.Many people have gone on to build and expand on the insights contained in this book, especially in the area of bargaining and cooperative game theory.

This is a very impressive book to keep on your shelf, and the discussion of poker and the role of bluffing is very interesting, but, owing largely to the 60+ years that have passed since its initial publication, it's not the best reference work or study material available.

Another word of warning:The review below is correct that the level of math that you must understand to fully appreciate this book is quite substantial.This book is more for the mathematically sophisticated who want to develop an appreciation for the origins of game theory.

Landmark work but heavy going

I'm not even sure I'm qualified to pass judgement on this book, butwhat I understand, I give 5 stars without hesitation. The authors discussalmost every class of game (2-person, 3-person, zero-sum, non-zero-sum,etc.) and even a very simplified version of poker.

Reviews (1)

State of the Art on Behavioral Choice Theory
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.

Reviews (4)

Well, i liked it anyway
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 me

As 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 convincing

i'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

Great book about cognitive pitfalls
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!

Statistical, Mathematical, Academic
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.

Reviews (14)

Very Uneven
I had high hopes for this book and after reading the intro and first couple paragraphs assigned it as an extra credit/optional text for a course on critical thinking and logic.As I continued reading however, I realized that I had made a mistake.The level of sophistication that is expected of the reader is extremely uneven.Sometimes concepts that are simple and easily understood are explained in greater detail than necessary.More frequently, in the middle and later chapters (the real meat of the book), complicated concepts and issues that ought to be introduced and explained are not; instead the author seems to assume that the reader knows all about them--which is a bad assumption for a popularization/intro-to-the-layperson book to make.

What I wanted for my students was a book that discussed empirical findings about how humans actually reason to motivate their learning about how we ought to reason.This book looked like a good option, but disappointed me.I found a better option in Ken Manktelow's _Reasoning and Thinking_.However, perhaps I should learn my lesson from my experience with _Inevitable Illusions_ and _finish_ reading the Manktelow book before recommending it.

A Good Lay Intro to the Cognitive Biases Literature
The negative reviews (especially by the evolutionary psych devotee) are grossly unfair. This book is not written for those already familiar with the cognitive biases literature, especially not for graduates students in psychology. Instead it is an expansion of a popular article that appeared in Bostonia magazine in 1991, written for intelligent laypeople. As such it is effective, more accessible than anything else I've found, and excellent supplemental reading for basic classes in logic or statistics. The author's lack of appreciation of evolutionary psych in no way detracts from the book's value in making people aware of cognitive illusions. It does fall down however in lacking adequate discussion of methods for avoiding these illusions, such as the natural frequencies approach, and needs to be supplemented (e.g., with one of the popular books by Gigerenzer).