A compilation of reviews of the book Thinking, Fast and SlowDaniel Kahneman

1. Jeran Binning DAU San Diego

2.  http://www.nytimes.com/pages/books/review/index.html
Copyright 2011 The New York Times Company
Illustration by David Plunkert

3.  Expertise can be learnt by prolonged exposure to
situations that are “sufficiently regular to be
predictable”, and in which the expert gets quick and
decisive feedback on whether he did the right or the
wrong thing.
 Experts can thus train their unconscious “pattern
recognition” mechanism to produce the right answer
quickly.
 So this certainly applies to chess, and it certainly does
not apply to predicting the course of Middle East
politics.

4.  Kahneman’s career has had three major phases

5.  In the first, he and Tversky did a series of
ingenious experiments that revealed twenty
or so “cognitive biases” — unconscious errors
of reasoning that distort our judgment of the
world.
 Typical of these is the “anchoring effect”:
our tendency to be influenced by irrelevant
numbers that we happen to be exposed to.

6.  Anchoring and adjustment: People who have to
make judgments under uncertainty use this heuristic
by starting with a certain reference point (anchor)
and then adjust it insufficiently to reach a final
conclusion.
 Example: If you have to judge another persons
productivity, the anchor for your final (adjusted)
judgment may be your own level of productivity.
Depending on your own level of productivity you
might therefore underestimate or overestimate the
productivity of this person.

7.  In the second phase, Kahneman and Tversky
showed that people making decisions under
uncertain conditions do not behave in the
way that economic models have traditionally
assumed; they do not “maximize utility.”

8.  The two then developed an alternative
account of decision making, one more
faithful to human psychology, which they
called “prospect theory.” (It was for this
achievement that Kahneman was awarded
the Nobel.)

9. Prospect theory was developed by Daniel Kahneman and Amos Tversky in 1979

10.  Prospect theory was developed by Daniel Kahneman and Amos Tversky in 1979 as a
psychologically realistic alternative to expected utility theory. It allows one to describe how people make
choices in situations where they have to decide between alternatives that involve risk, e.g. in financial
decisions. Starting from empirical evidence, the theory describes how individuals evaluate potential losses
and gains. In the original formulation the term prospect referred to a lottery.
 The theory describes such decision processes as consisting of two stages, editing and evaluation. In the
first, possible outcomes of the decision are ordered following some heuristic. In particular, people decide
which outcomes they see as basically identical and they set a reference point and consider lower outcomes
as losses and larger as gains. In the following evaluation phase, people behave as if they would compute a
value (utility), based on the potential outcomes and their respective probabilities, and then choose the
alternative having a higher utility.
 The formula that Kahneman and Tversky assume for the evaluation phase is (in its simplest form) given by
where are the potential outcomes and their respective probabilities. v is a so-called value function that
assigns a value to an outcome. The value function (sketched in the Figure) which passes through the
reference point is s-shaped and, as its asymmetry implies, given the same variation in absolute
value, there is a bigger impact of losses than of gains (loss aversion). In contrast to Expected Utility
Theory, it measures losses and gains, but not absolute wealth. The function w is called a
probability weighting function and expresses that people tend to
overreact to small probability events, but underreact to medium
and large probabilities
 To see how Prospect Theory (PT) can be applied in an example, consider a decision about buying an
insurance policy. Let us assume the probability of the insured risk is 1%, the potential loss is $1000 and the
premium is $15. If we apply PT, we first need to set a reference point. This could be, e.g., the current
wealth, or the worst case (losing $1000). If we set the frame to the current wealth, the decision would be to
either pay $15 for sure (which gives the PT-utility of v( − 15)) or a lottery with outcomes $0 (probability
99%) or $-1000 (probability 1%) which yields the PT-utility of . These expressions can be computed
numerically. For typical value and weighting functions, the former expression could be larger due to the
convexity of v in losses, and hence the insurance looks unattractive. If we set the frame to $-1000, both
alternatives are set in gains. The concavity of the value function in gains can then lead to a preference
for buying the insurance.
 We see in this example that a strong overweighting of small probabilities can also undo the effect of the
convexity of v in losses: the potential outcome of losing $1000 is overweighted.
 The interplay of overweighting of small probabilities and concavity-convexity of the value function leads to
the so-called four-fold pattern of risk attitudes: risk-averse behavior in gains involving moderate
probabilities and of small probability losses; risk-seeking behavior in losses involving moderate
probabilities and of small probability gains. This is an explanation for the fact that
people, e.g., simultaneously buy lottery tickets and insurances, but
still invest money conservatively.

11.  An important paper in the development of the behavioral finance and
economics fields was written by Kahneman and Tversky in 1979. This
paper, 'Prospect theory: An Analysis of Decision Under Risk', used
cognitive psychological techniques to explain a number of documented
divergences of economic decision making from neo-classical theory.
 Over time many other psychological effects have been incorporated into
behavioral finance, such as overconfidence and the effects of limited
attention.
 Further milestones in the development of the field include a well
attended and diverse conference at the University of Chicago,[4] a
special 1997 edition of the Quarterly Journal of Economics ('In Memory of
Amos Tversky') devoted to the topic of behavioral economics and the
award of the Nobel prize to Daniel Kahneman in 2002 "for having
integrated insights from psychological research into economic science,
especially concerning human judgment and decision-making under
uncertainty"

12.  In the third phase of his career, mainly after
the death of Tversky, Kahneman has delved
into “hedonic psychology”: the science of
happiness, its nature and its causes. His
findings in this area have proved disquieting
— and not just because one of the key
experiments involved a deliberately
prolonged colonoscopy.

13.  Kahneman never grapples philosophically with the
nature of rationality. He does, however, supply a
fascinating account of what might be taken to be its
goal: happiness.
 What does it mean to be happy? When Kahneman
first took up this question, in the mid 1990s, most
happiness research relied on asking people how
satisfied they were with their life on the whole.
 But such retrospective assessments depend on
memory, which is notoriously unreliable.

14.  What if, instead, a person’s actual experience of pleasure or pain
could be sampled from moment to moment, and then summed up
over time? Kahneman calls this “experienced” well-being, as
opposed to the “remembered” well-being that researchers had
relied upon.
 And he found that these two measures of happiness diverge in
surprising ways. What makes the “experiencing self” happy is not
the same as what makes the “remembering self” happy.
 In particular, the remembering self does not care about duration —
how long a pleasant or unpleasant experience lasts. Rather, it
retrospectively rates an experience by the peak level of pain or
pleasure in the course of the experience, and by the way the
experience ends.

15.  Clearly, much remains to be done in hedonic psychology. But
Kahneman’s conceptual innovations have laid the foundation for many of
the empirical findings he reports in this book:
 that while French mothers spend less time with their children than
American mothers, they enjoy it more;
 that headaches are hedonically harder on the poor;
 that women who live alone seem to enjoy the same level of well-being as
women who live with a mate; and
 that a household income of about $75,000 in high-cost areas of the
country is sufficient to maximize happiness.
 Policy makers interested in lowering the misery index of society will find
much to ponder here.

16.  System 2, in Kahneman’s scheme, is our
slow, deliberate, analytical and consciously effortful mode of
reasoning about the world.
 System 1, by contrast, is our fast, automatic, intuitive and largely
unconscious mode.
 It is System 1 that detects hostility in a voice and effortlessly
completes the phrase “bread and. . . . ”
 It is System 2 that swings into action when we have to fill out a tax
form or park a car in a narrow space. (As Kahneman and others
have found, there is an easy way to tell how engaged a person’s
System 2 is during a task: just look into his or her eyes and note
how dilated the pupils are.)

17.  More generally, System 1 uses association and
metaphor to produce a quick and dirty draft of
reality, which System 2 draws on to arrive at explicit
beliefs and reasoned choices.
 System 1 proposes, System 2 disposes.
 So System 2 would seem to be the boss, right?
 In principle, yes. But System 2, in addition to being
more deliberate and rational, is also lazy. And it tires
easily. (The vogue term for this is “ego depletion.”)

18.  “Although System 2 believes itself to be
where the action is,” Kahneman writes, “the
automatic System 1 is the hero of this book.”
System 2 is especially quiescent, it
seems, when your mood is a happy one.

20.  Memory also holds the vast repertory of skills
we have acquired in a lifetime of
practice, which automatically produce
adequate solutions to challenges as they
arise…. The acquisition of skills requires a
regular environment, an adequate
opportunity to practice, and rapid and
unequivocal feedback about the correctness
of thoughts and actions.

21.  When these conditions are fulfilled, skill
eventually develops, and the intuitive
judgments and choices that quickly come to
mind will mostly be accurate. All of this is the
work of System 1, which means it occurs
automatically and fast. A marker of skilled
performance is the ability to deal with vast
amounts of information swiftly and
efficiently. Pp 416 conclusions.

22.  The way to block errors
that originate in system
one us a simple in
principle: recognize
signs that you are in a
cognitive minefield, slow
down and ask for
reinforcement from
System 2.

23.  What can be done about biases? How can we
improve judgments and decisions, both our
own and those of the institutions that we
serve and serve us?

24.  The short answer is that little can be achieved
without considerable investment of effort. As
I know from experience System 1 is not
readily educable. Except for some effects
that I attribute mostly to my age, my intuitive
thinking is just as prone to
overconfidence, extreme predications, and
the planning fallacy as it was before I made a
study of these issues.

25.  I have improved only in my ability to
recognize situations in which errors are likely;
“This number will be an anchor…” “The
decision could be changes if the question was
reframed…” And I have made much more
progress in recognizing the errors of others
than my own.”

26.  By the time I got to the end of “Thinking, Fast and
Slow,” my skeptical frown had long since given way to
a grin of intellectual satisfaction. Appraising the book
by the peak-end rule, I overconfidently urge everyone
to buy and read it. But for those who are merely
interested in Kahneman’s takeaway on the Malcolm
Gladwell question it is this:
 If you’ve had 10,000 hours of training in a
predictable, rapid-feedback environment —
chess, firefighting, anesthesiology — then blink. In all
other cases, think.

27.  Projects fail at a spectacular rate. One reason is that too many
people are reluctant to speak up about their reservations during
the all-important planning phase. By making it safe for dissenters
who are knowledgeable about the undertaking and worried about
its weaknesses to speak up, you can improve a project’s chances of
success.
 Research conducted in 1989 by Deborah J. Mitchell, of the
Wharton School; Jay Russo, of Cornell; and Nancy Pennington, of
the University of Colorado, found that prospective hindsight—
imagining that an event has already occurred—increases the
ability to correctly identify reasons for future outcomes by 30%.
We have used prospective hindsight to devise a method called a
premortem, which helps project teams identify risks at the outset.

28.  Judgment Under Uncertainty:
Heuristics and Biases. Amos Tversky
and Daniel Kahneman
 Science, Volume 185, 1974
 Research for DARPA N00014-73C-0438
monitored by ONR and Research and
Development Authority of Hebrew
University, Jerusalem, Israel.

29.  The same goes for all of us, almost all the time. We think we're smart; we're
confident we won't be unconsciously swayed by the high list price of a house.
We're wrong. (Kahneman admits his own inability to counter some of these
effects.) We're also hopelessly subject to the "focusing illusion", which can be
conveyed in one sentence: "Nothing in life is as important as you think it is when
you're thinking about it."
 Whatever we focus on, it bulges in the heat of our attention until we assume its
role in our life as a whole is greater than it is.
 Another systematic error involves "duration neglect" and the "peak-end rule".
Looking back on our experience of pain, we prefer a larger, longer amount to a
shorter, smaller amount, just so long as the closing stages of the greater pain
were easier to bear than the closing stages of the lesser one.
 Galen Strawson
 guardian.co.uk, Tuesday 13 December 2011 06.56 EST

30.  System 2 is slothful, and tires easily (a process called "ego depletion") –
so it usually accepts what System 1 tells it. It's often right to do
so, because System 1 is for the most part pretty good at what it does; it's
highly sensitive to subtle environmental cues, signs of danger, and so on.
 It kept our remote ancestors alive. Système 1 a ses raisons que Système 2
ne connaît point, as Pascal might have said.
 It does, however, pay a high price for speed. It loves to simplify, to assume
WYSIATI ("what you see is all there is"), even as it gossips and embroiders
and confabulates.
 It's hopelessly bad at the kind of statistical thinking often required for good
decisions, it jumps wildly to conclusions and it's subject to a fantastic suite
of irrational biases and interference effects (the halo effect, the "Florida
effect", framing effects, anchoring effects, the confirmation bias, outcome
bias, hindsight bias, availability bias, the focusing illusion, and so on).

31.  Behavioralists, Kahneman included, have been cataloging people’s
systematic mistakes and nonlogical patterns for years. A few of
the examples he cites:
 1. Framing. Test subjects are more likely to opt for surgery if told
that the “survival” rate is 90 percent, rather than that the mortality
rate is 10 percent.
 2. The sunk-cost fallacy. People seek to avoid feelings of regret;
thus, they invest more money and time in a project with dubious
results rather than give it up and admit they were wrong.
 3. Loss aversion. In experiments, most subjects would prefer to
receive a sure $46 than have a 50 percent chance of making $100.

32.  Kahneman is perhaps least persuasive in his treatment of the
business world. Noting that even top performers in business—also
sports—tend eventually to revert to the mean, he attributes
success largely to luck.
 This confuses events that may not be predictable with those that
are determined by chance.
 A high-achieving retail store, to cite one of his examples, is not
lucky—it is well-situated. And if its sales later decline, that is not
necessarily a sign that its prior success was random.
 Business has a self-correcting cycle that fosters mean reversion.
Success attracts competitors.

33.  Mr. Kahneman stresses that he is just as susceptible as the rest of us to
the cognitive illusions he has discovered. He tries to recognize situations
when mistakes are especially likely to occur—such as when he is starting
a big project or making a forecast—and then act to rethink his System 1
inclinations.
 The tendency to underestimate the costs of future projects, he notes, is
susceptible to taking an "outside view": looking at your own project as an
outsider would.
 To avoid overconfidence, Mr. Kahneman recommends an exercise called
the "premortem," developed by the psychologist Gary Klein: Before
finalizing a decision, imagine that, a year after it has been made, it has
turned out horribly, then write a history of how it went wrong and why.
 By CHRISTOPHER F. CHABRIS a psychology professor at Union College and a co-author of "The Invisible Gorilla: And Other Ways Our Intuitions Deceive Us.
 http://online.wsj.com/home-page

34.  A transitional moment linking the positive and the negative aspects of thinking
fast illustrates why the author’s personality – and thus the book – is so engaging.
Kahneman regards even the experts as prone to the mistakes of System 1 listed
above, and cheerfully admits that he is no exception. But he wants to know
whether this view can be reconciled with cases such as that of the firefighting
captain. So he engages one of his vehement critics on this issue and they debate
their way to a joint paper.
 Their answer is that expertise can be learnt by prolonged exposure to situations
that are “sufficiently regular to be predictable”, and in which the expert gets
quick and decisive feedback on whether he did the right or the wrong thing.
 Experts can thus train their unconscious “pattern recognition” mechanism to
produce the right answer quickly.
 So this certainly applies to chess, and it certainly does not apply to predicting the
course of Middle East politics.

35.  One of Kahneman and Tversky’s most famous ideas is
what they call prospect theory: our inclination to fear
possible losses more than we value possible gains.
 Would you take a bet on a one-time coin flip that paid
$200 if you won but cost you $150 if you lost?
 Most people wouldn’t, though it’s tilted in your favor.
 Pro golfers tend to make a higher proportion of their putts
when they’re trying to avoid a bogey (which would result
in losing a stroke) than when they have a chance for a
birdie (for the possible gain of a stroke);
here, too, avoiding a failure is more crucial than achieving
a triumph.

36.  If the brain is a “a machine for jumping to
conclusions,” as Kahneman writes, it’s System 1
that yells “Geronimo!” Autopilot thinking
explains the popular opinion that tornadoes kill
more people than asthma, although in fact
asthma kills 20 times as many:
 We fixate on scenes from TV showing homes
turned to matchsticks and overestimate the
representativeness of such scenes.

37.  The first bit of mental machinery, which Kahneman blandly calls System One, works 24/7 to keep
us out of trouble, while alerting us to fleeting opportunities. Appropriate for a species that is both
predator and prey, System One lives in a world of snap judgments, extensible metaphors, ill-
informed biases, and loosely constructed rules of thumb. We sometimes call this decision making
apparatus intuition. Man’s intuition is sophisticated enough that it has helped us thrive across a
variety of ever changing environments.
 Despite its utility, System One is often wrong, especially if numbers are involved. For a trivial
example, answer quickly: If the sum of the cost of a ball and bat is $1.10 and the bat cost a dollar
more than the ball, what does the ball cost?
 Your System One answer (most likely wrong) is good enough to avoid mistaking a hungry lion for
a tasty chicken. But it’s not good enough to build an airplane or design an effective income tax
code. (The answer is a nickel, not a dime.).
 System Two is associated with enumeration, computation, objective analysis, and complex chains
of logic. It is our rational brain. Kahneman’s work shows that even scientists like himself use
System Two very sparingly, calling on it only when System One asks for help. In addition, in order
to function at the highest levels, System Two requires training, discipline, concentration, skeptical
and impartial evaluation of purported facts, and the ruthless elimination of contradictions.

38. It afflicts us all. Because confidence in our own
judgments is part of being human.
By DANIEL KAHNEMAN
Published: October 19, 2011

39.  Decades later, I can see many of the central themes of my thinking
about judgment in that old experience. One of these themes is that
people who face a difficult question often answer an easier one instead,
without realizing it. We were required to predict a soldier’s performance
in officer training and in combat, but we did so by evaluating his behavior
over one hour in an artificial situation. This was a perfect instance of a
general rule that I call WYSIATI, “What you see is all there is.” We had
made up a story from the little we knew but had no way to allow for what
we did not know about the individual’s future, which was almost
everything that would actually matter. When you know as little as we did,
you should not make extreme predictions like “He will be a star.” The
stars we saw on the obstacle field were most likely accidental flickers, in
which a coincidence of random events — like who was near the wall —
largely determined who became a leader. Other events — some of them
also random — would determine later success in training and combat.

40.  We often interact with professionals who exercise their
judgment with evident confidence, sometimes priding
themselves on the power of their intuition. In a world rife
with illusions of validity and skill, can we trust them? How
do we distinguish the justified confidence of experts from
the sincere overconfidence of professionals who do not
know they are out of their depth? We can believe an expert
who admits uncertainty but cannot take expressions of
high confidence at face value. As I first learned on the
obstacle field, people come up with coherent stories and
confident predictions even when they know little or
nothing. Overconfidence arises because people are often
blind to their own blindness.

41.  Some cognitive biases, of course, are flagrantly
exhibited even in the most natural of settings.
Take what Kahneman calls the “planning
fallacy”: our tendency to overestimate benefits
and underestimate costs, and hence foolishly to
take on risky projects.
 In 2002, Americans remodeling their
kitchens, for example, expected the job to cost
$18,658 on average, but they ended up paying
$38,769.

42.  The planning fallacy is “only one of the manifestations of a
pervasive optimistic bias,” Kahneman writes, which “may
well be the most significant of the cognitive biases.”
Now, in one sense, a bias toward optimism is obviously
bad, since it generates false beliefs — like the belief that
we are in control, and not the playthings of luck.
 But without this “illusion of control,” would we even be
able to get out of bed in the morning?
 Optimists are more psychologically resilient, have
stronger immune systems, and live longer on average than
their more reality-based counterparts.

43.  Biases in the evaluation of compound events
are particularly significant in the context of
planning. The successful completion of an
undertaking, such as the development of a
new product, typically has a conjunctive
character: for the undertaking to
succeed, each of a series of events must
occur. Even when each of these events is very
likely, the overall probability of success can
be quite low if the number of events is large.

44.  In a series of experiments, Ellen Langer (1975) demonstrated first the
prevalence of the illusion of control and second, that people were more
likely to behave as if they could exercise control in a chance situation
where “skill cues” were present.
 By skill cues, Langer meant properties of the situation more normally
associated with the exercise of skill, in particular the exercise of
choice, competition, familiarity with the stimulus and involvement in
decisions.
 One simple form of this fallacy is found in casinos: when rolling dice in
craps, it has been shown that people tend to throw harder for high
numbers and softer for low numbers.
 Under some circumstances, experimental subjects have been induced to
believe that they could affect the outcome of a purely random coin toss.
Subjects who guessed a series of coin tosses more successfully began to
believe that they were actually better guessers, and believed that their
guessing performance would be less accurate if they were distracted.

45.  Drawing on a vast body of research, Lears ranges
through the entire sweep of American history as he
uncovers the hidden influence of risk
taking, conjuring, soothsaying, and sheer dumb luck on
our culture, politics, social lives, and economy.
T.J. Jackson Lears “Something for Nothing” (2003)

46.  Moreover, as Kahneman notes, exaggerated
optimism serves to protect both individuals
and organizations from the paralyzing effects
of another bias, “loss aversion”: our tendency
to fear losses more than we value gains.
 It was exaggerated optimism that John
Maynard Keynes had in mind when he talked
of the “animal spirits” that drive capitalism.

47.  "losses loom larger than corresponding gains”
 "In prospect theory, loss aversion refers to the tendency for people to strongly
prefer avoiding losses than acquiring gains. Some studies suggest that losses are
as much as twice as psychologically powerful as gains. Loss aversion was first
convincingly demonstrated by Amos Tversky and Daniel Kahneman.”
 "The principle of loss aversion was first introduced by Kahneman and Tversky
(1979)"
 Tversky and Kahneman (1991) "The central assumption of the theory is that
losses and disadvantages have greater impact on preferences than gains and
advantages.”
 "Numerous studies have shown that people feel losses more deeply than gains of
the same value (Kahneman and Tversky 1979, Tversky and Kahneman
1991)." Goldberg and von Nitzsch (1999) pages 97-98
 "Both the status quo bias and the endowment effect are part of a more general
issue known as loss aversion." (Montier 2007, p. 32)

48.  the inclination to see events that have occurred as more predictable than they in fact were before
they took place. Hindsight bias has been demonstrated experimentally in a variety of
settings, including politics, games and medicine.
 In psychological experiments of hindsight bias, subjects also tend to remember their predictions of
future events as having been stronger than they actually were, in those cases where those predictions
turn out correct.
 Prophecy that is recorded after the fact is an example of hindsight bias, given its own rubric, as
vaticinium ex eventu. foretelling after the event
 One explanation of the bias is the availability heuristic: the event that did occur is more salient in
one's mind than the possible outcomes that did not.
 It has been shown that examining possible alternatives may reduce the effects of this bias.

49.  The framing of alternatives also affects decisions.
 For example, when people (including doctors) who are considering a risky
medical procedure are told that 90 percent survive five years, they are far
more likely to accept the procedure than when they are told that 10 percent
do not survive five years.
 Because framing affects people's behavior, providing more information
cannot remedy matters, unless the information is presented in a fully
neutral fashion.
 In some cases, additional information only increases people's anxiety and
confusion, thereby reducing their welfare.

50.  A heuristic (hyu̇-ris-tik) is a method to help solve a
problem, commonly informal. It is particularly used for
a method that often rapidly leads to a solution that is
usually reasonably close to the best possible answer.
 Heuristics are "rules of thumb” educated
guesses, intuitive judgments or simply common sense.
 In more precise terms, heuristics stand for strategies
using readily accessible, though loosely
applicable, information to control problem-solving in
human beings and machines.

51. “There is always a well-known solution to every human
problem – neat, plausible, and wrong.”
 H. L. Mencken, Prejudices: Second Series, 1920
Tailors' Rule of Thumb. This is the fictional rule described by Jonathan Swift in his satirical novel Gulliver's Travels:
Then they measured my right Thumb, and desired
no more; for by a mathematical Computation, that
twice round the Thumb is once around the
Wrist, and so on to the Neck and Waist, and by the
help of my old Shirt, which I displayed on the
Ground before them for a Pattern, they fitted me
exactly."

52. Rules of Thumb
 People rely on a limited number of heuristic
(essentially rules of thumbs) principles
which reduce the complex tasks of
assessing probabilities and predicting
values to simpler judgmental operations.
 In general, these heuristics are quite
useful, but sometimes they lead to severe
and systematic errors.
 Daniel Kahneman

53.  Financial - Rule of 72 A rule of thumb for exponential growth at a constant rate. An
approximation of the "doubling time" formula used in population growth, which says divide 70 by
the percent growth rate (the actual number is 69.3147181 from the natural logarithm of 2, if the
percent growth is much much less than 1%). In terms of money, it is frequently easier to use 72
(rather than 70) because it works better in the 4%-10% range where interest rates often lie.
Therefore, divide 72 by the percent interest rate to determine the approximate amount of time to
double your money in an investment. For example, at 8% interest, your money will double in
approximately 9 years (72/8 = 9).
 Tailors' Rule of Thumb A simple approximation that was used by tailors to determine the
wrist, neck, and waist circumferences of a person through one single measurement of the
circumference of that person's thumb. The rule states, typically, that twice the circumference of a
person's thumb is the circumference of their wrist, twice the circumference of the wrist is the
circumference of the neck, and twice around the neck is the person's waist. For example, if the
circumference of the thumb is 4 inches, then the wrist circumference is 8 inches, the neck is 16
and the waist is 32. An interesting consequence of this is that — for those to whom the rule applies
— this simple method can be used to determine if trousers will fit: the trousers are wrapped
around the neck, and if the two ends barely touch, then they will fit. Any overlap or lack thereof
corresponds to the trousers being too loose or tight, respectively.
 Marine Navigation A ship's captain should navigate to keep the ship more than a thumb's width
from the shore, as shown on the nautical chart being used. Thus, with a coarse scale chart, that
provides few details of near shore hazards such as rocks, a thumb's width would represent a great
distance, and the ship would be steered far from shore; whereas on a fine scale chart, in which
more detail is provided, a ship could be brought closer to shore.[1]
 Statistics The Statistical Rule of Thumb says that for most large data sets, 68% of data points will
occur within one standard deviation from the mean, and 95% will occur within two standard
deviations. Chebyshev's inequality is a more general rule along these same lines and applies to all
data sets.

54.  In an effort that spanned several years, we attempted
to answer one basic question:
 Under what conditions are the intuitions of
professionals worthy of trust?
 We do not claim that the conclusions we reached are
surprising (many were anticipated by
Shanteau, 1992, Hogarth, 2001, and
Myers, 2002, among others), but we believe that they
add up to a coherent view of expert intuition, which is
more than we expected to achieve when we began.

55.  The NDM approach, which focuses on the successes of expert intuition, grew out
of early research on master chess players conducted by deGroot (1946/1978) and
later by Chase and Simon (1973). DeGroot showed that chess grand masters were
generally able to identify the most promising moves rapidly, while mediocre
chess players often did not even consider the best moves.
 The chess grand masters mainly differed from weaker players in their unusual
ability to appreciate the dynamics of complex positions and quickly judge a line
of play as promising or fruitless.
 Chase and Simon (1973) described the performance of chess experts as a form of
perceptual skill in which complex patterns are recognized. They estimated that
chess masters acquire a repertoire of 50,000 to 100,000 immediately
recognizable patterns, and that this repertoire enables them to identify a good
move without having to calculate all possible contingencies.
 Strong players need a decade of serious play to assemble this large collection of
basic patterns, but of course they achieve impressive levels of skill even earlier.
 On the basis of this work, Simon defined intuition as the recognition of patterns
stored in memory.

56.  Kahneman read Meehl’s book in 1955 while serving in the Psychological Research
Unit of the Israel Defense Forces, and the book helped him make sense of his
own encounters with the difficulties of clinical judgment.
 One of Kahneman’s duties was to assess candidates for officer training, using
field tests and other observations as well as a personal interview.
 Kahneman (2003) described the powerful sense of getting to know each
candidate and the accompanying conviction that he could foretell how well the
candidate would do in further training and eventually in combat.
 The subjective conviction of understanding each case in isolation was not
diminished by the statistical feedback from officer training school, which
indicated that the validity of the assessments was negligible.
 Kahneman coined the term illusion of validity for the unjustified sense of
confidence that often comes with clinical judgment. His early experience with the
fallibility of intuitive impressions could hardly be more different from Klein’s
formative encounter with the successful decision making of fire-fighting ground
commanders.

57.  Our starting point is that intuitive judgments
can arise from genuine skill—the focus of the
Naturalistic Decision Making (NDM)
approach—but that they can also arise from
inappropriate application of the heuristic
processes on which students of the Heuristics
Based tradition have focused.

58.  Skilled judges are often unaware of the cues
that guide them, and individuals whose
intuitions are not skilled are even less likely to
know where their judgments come from.

59.  True experts, it is said, know when they don’t
know.
 However, non-experts (whether or not they
think they are) certainly do not know when
they don’t know.
 Subjective confidence is therefore an
unreliable indication of the validity of
intuitive judgments and decisions.

60.  The determination of whether intuitive
judgments can be trusted requires an
examination of the environment in which the
judgment is made and of the opportunity
that the judge has had to learn the
regularities of that environment.

61.  We describe task environments as “high-validity” if there
are stable relationships between objectively identifiable
cues and subsequent events or between cues and the
outcomes of possible actions.
 Medicine and firefighting are practiced in environments of
fairly high validity.
 In contrast, outcomes are effectively unpredictable in
zero-validity environments.
 To a good approximation, predictions of the future value
of individual stocks and long-term forecasts of political
events are made in a zero-validity environment.

62.  Validity and uncertainty are not
incompatible. Some environments are both
highly valid and substantially uncertain.
 Poker and warfare are examples. The best
moves in such situations reliably increase the
potential for success.

63.  An environment of high validity is a necessary
condition for the development of skilled intuitions.
 Other necessary conditions include adequate
opportunities for learning the environment
(prolonged practice and feedback that is both rapid
and unequivocal).
 If an environment provides valid cues and good
feedback, skill and expert intuition will eventually
develop in individuals of sufficient talent.

64.  Although true skill cannot develop in irregular or
unpredictable environments, individuals will
sometimes make judgments and decisions that
are successful by chance.
 These “lucky” individuals will be susceptible to
an illusion of skill and to overconfidence (Arkes,
2001).
 The financial industry is a rich source of
examples.

65.  The situation that we have labeled fractionation of skill is
another source of overconfidence. Professionals who have
expertise in some tasks are sometimes called upon to
make judgments in areas in which they have no real skill.
 (For example, financial analysts may be skilled at
evaluating the likely commercial success of a firm, but this
skill does not extend to the judgment of whether the stock
of that firm is underpriced.)
 It is difficult both for the professionals and for those who
observe them to determine the boundaries of their true
expertise.

66.  We agree that the weak regularities available in
low-validity situations can sometimes support
the development of algorithms that do better
than chance. These algorithms only achieve
limited accuracy, but they outperform humans
because of their advantage of consistency.
 However, the introduction of algorithms to
replace human judgment is likely to evoke
substantial resistance and sometimes has
undesirable side effects.

67. The Drunkard’s Walk
– Functional magnetic resonance imaging, for
example, shows that risk and reward are assessed by
parts of the dopaminergic system.
– A brain-reward circuit important for motivational and
emotional processes
• The images show, too, that the amygdala, which is
also linked to our emotional state, especially
fear, is activated when we make decisions
couched in uncertainty.

68. The Drunkard’s Walk
• Fortune is fair in potentialities, she is not fair in outcomes.
• pp 13
• When we look at extraordinary accomplishments in sports--
-or elsewhere—we should keep in mind that
extraordinary events can happen without extraordinary
causes.
• Random events often look like nonrandom
events, and in interpreting human affairs we must take
care not to confuse the two. pp20

69. Selection Bias
• This bias makes us miscompute the odds and wrongly ascribe skills. If you
funded 1,000,000 unemployed people endowed with no more than the
ability to say "buy" or "sell", odds are that you will break-even in the
aggregate, minus transaction costs, but a few will hit the jackpot, simply
because the base cohort is very large. It will be almost impossible not to
have small Warren Buffets by luck alone. After the fact they will be very
visible and will derive precise and well-sounding explanations about why
they made it. It is difficult to argue with them; "nothing succeeds like
success". All these retrospective explanations are pervasive, but there are
scientific methods to correct for the bias. This has not filtered through to
researchers have
the business world or the news media;
evidence that professional fund managers are
just no better than random and cost money to society (the
total revenues from these transaction costs is in the hundreds of billion of
dollars) but the public will remain convinced that "some" of these
investors have skills.

70. False Discovery Rate
• July 13, 2008
• STRATEGIES
• The Prescient Are Few
• By MARK HLBERT
• HOW many mutual fund managers can consistently pick stocks that outperform the broad stock market averages — as opposed to just being lucky now and then?
• Countless studies have addressed this question, and have concluded that very few managers have the ability to beat the market over the long term. Nevertheless, researchers
have been unable to agree on how small that minority really is, and on whether it makes sense for investors to try to beat the market by buying shares of actively managed
mutual funds.
• A new study builds on this research by applying a sensitive statistical test borrowed from outside the investment world. It comes to a rather sad conclusion: There was once a
small number of fund managers with genuine market-beating abilities, as judged by having past performance so good that their records could not be attributed to luck alone.
But virtually none remain today. Index funds are the only rational alternative for almost all mutual fund investors, according to the study’s findings.
• The study, “False Discoveries in Mutual Fund Performance: Measuring Luck in Estimating Alphas,” has been circulating for over a year in academic circles. Its authors are
Laurent Barras, a visiting researcher at Imperial College’s Tanaka Business School in London; Olivier Scaillet, a professor of financial econometrics at the University of Geneva
and the Swiss Finance Institute; and Russ Wermers, a finance professor at the University of Maryland.
• The statistical test featured in the study is known as the “False Discovery Rate,” and is used in fields as diverse as computational biology and astronomy. In effect, the method
is designed to simultaneously avoid false positives and false negatives — in other words, conclusions that something is statistically significant when it is entirely random, and
the reverse.
• Both of those problems have plagued previous studies of mutual funds, Professor Wermers said. The researchers applied the method to a database of actively managed
domestic equity mutual funds from the beginning of 1975 through 2006. To ensure that their results were not biased by excluding funds that have gone out of business over
the years, they included both active and defunct funds. They excluded any fund with less than five years of performance history. All told, the database contained almost 2,100
funds.
• The researchers found a marked decline over the last two decades in the number of fund managers able to pass the False Discovery Rate test. If they had focused only on
managers running funds in 1990 and their records through that year, for example, the researchers would have concluded that 14.4 percent of managers had genuine stock-
picking ability. But when analyzing their entire fund sample, with records through 2006, this proportion was just 0.6 percent — statistically indistinguishable from
zero, according to the researchers.
• This doesn’t mean that no mutual funds have beaten the market in recent years, Professor Wermers said. Some have done so repeatedly over periods as short as a year or
two. But, he added, “the number of funds that have beaten the market over their entire histories is so small that the False Discovery Rate test can’t eliminate the
possibility that the few that did were merely false positives” — just lucky, in other words.
• Professor Wermers says he was surprised by how rare stock-picking skill has become. He had “generally been positive about the existence of fund manager ability,” he said, but
these new results have been a “real shocker.”
• WHY the decline? Professor Wermers says he and his co-authors suspect various causes. One is high fees and expenses. The researchers’ tests found that, on a pre-expense
basis, 9.6 percent of mutual fund managers in 2006 showed genuine market-beating ability — far higher than the 0.6 percent after expenses were taken into account.
This suggests that one in 10 managers may still have market-
beating ability. It’s just that they can’t come out ahead after all
their funds’ fees and expenses are paid.
• Another possible factor is that many skilled managers have gone to the hedge fund world. Yet a third potential reason is that the market has become more efficient, so it’s
harder to identify undervalued or overvalued stocks. Whatever the causes, the investment implications of the study are the same: buy and hold an index fund benchmarked
to the broad stock market.
• Professor Wermers says his advice has evolved significantly as a result of this study. Until now, he says, he wouldn’t have tried to discourage a sophisticated investor from
trying to pick a mutual fund that would outperform the market. Now, he says, “it seems almost hopeless.”
• Mark Hulbert is editor of The Hulbert Financial Digest, a service of MarketWatch. E-mail: strategy@nytimes.com.
•

71.  Bent Flyvbjerg
 Nils Bruzelius
 Werner Rothengatter

72.  To begin, the book identifies a common feature of the
conventional megaprojects development, that
is, despite the overwhelming costs overrun, below-
projection revenue, and strikingly poor performance
records in terms of economy, environment and public
support, megaprojects grow continuously in number
and scale around the world, forming the so-called
megaprojects paradox.
 Understanding of this problem and its consequences
are explored in the first six chapters, which document
the cost overruns, the demand over forecasts, and
viability inflation of major megaprojects.

73.  In 9 out of 10 transport infrastructure projects, costs are underestimated, resulting in cost overrun;
 For rail, actual costs are, on the average, 45% higher than estimated costs (standard deviation, S.D. = 38);
 For fixed links (tunnels and bridges), actual costs are, on the average, 34% higher than estimated costs (S.D. = 62);
 For roads, actual costs are, on the average, 20% higher than estimated costs (S.D. = 30);
 For all project types, actual costs are, on the average, 28% higher than estimated costs (S.D. = 39);
 Cost underestimation and overrun exist across 20 nations and five continents; it appears to be a global phenomenon;
 Cost underestimation and overrun appear to be more pronounced in developing nations than in North America and
Europe (data for rail only);
 Cost underestimation and overrun have not decreased over the past 70 years. No learning seems to take place;
 Cost underestimation and overrun cannot be explained by error and seem to be best explained by strategic
misrepresentation, namely, lying, with a view to getting projects started [5].

74.  The authors cite as a core factor, underlying all of these
dreadful situations with megaprojects, the lack of
transparency in decision making and the weak
involvement of the civil society, or what they call a
"democracy deficit."
 They make a big point of the failure to pay attention to
risks and the lack of accountability in the project decision-
making processes as a main source of difficulties.
 The authors' concept of risk is almost entirely limited to
financial risk.

75.  Heuristic algorithms are often employed because they may be seen to "work" without
having been mathematically proven to meet a given set of requirements.
 Great care must be given when employing a heuristic algorithm. One common pitfall
in implementing a heuristic method to meet a requirement comes when the engineer
or designer fails to realize that the current data set doesn't necessarily represent
future system states.
 While the existing data can be pored over and an algorithm can be devised to
successfully handle the current data, it is imperative to ensure that the
heuristic method employed is capable of handling future data
sets.
 This means that the engineer or designer must fully understand the rules that
generate the data and develop the algorithm to meet those requirements and not
just address the current data sets.
 A simple example of how heuristics can fail is to answer the question "What is the
next number in this sequence: 1, 2, 4?". One heuristic algorithm might say that the
next number is 8 because the numbers are doubling - leading to a sequence like
1, 2, 4, 8, 16, 32... Another, equally valid, heuristic would say that the next number is 7
because each number is being raised by one higher interval than the one before -
leading to a series that looks like 1, 2, 4, 7, 11, 16...
 Statistical analysis must be conducted when employing heuristics to ensure that
enough data points are utilized to make incorrect outcomes statistically
insignificant

77.  A major problem with expert estimates is overconfidence.
To overcome this, Hubbard advocates using calibrated
probability assessments to quantify analysts’ abilities to
make estimates. Calibration assessments involve getting
analysts to answer trivia questions and eliciting confidence
intervals for each answer. The confidence intervals are
then checked against the proportion of correct
answers. Essentially, this assesses experts’ abilities to
estimates by tracking how often they are right. It has been
found that people can improve their ability to make
subjective estimates through calibration training – i.e.
repeated calibration testing followed by feedback.

78.  The Dunning–Kruger effect is a cognitive bias in which unskilled people make
poor decisions and reach erroneous conclusions, but their incompetence
denies them the metacognitive ability to recognize their mistakes.[1]
 The unskilled therefore suffer from illusory superiority, rating their ability as
above average, much higher than it actually is, while the highly skilled
underrate their own abilities, suffering from illusory inferiority.
 Actual competence may weaken self-confidence, as competent individuals
may falsely assume that others have an equivalent understanding.
 As Kruger and Dunning conclude, "the miscalibration of the incompetent
stems from an error about the self, whereas the miscalibration of the highly
competent stems from an error about others" (p. 1127).[2] The effect is about
paradoxical defects in cognitive ability, both in oneself and as one compares
oneself to others.

82. Jeran Binning

83.  Daniel Kahneman,1 Alan B. Krueger,1,2* David Schkade,3 Norbert Schwarz,4 Arthur A. Stone5
 The belief that high income is associated with good mood is widespread but
mostly illusory.
 People with above-average income are relatively satisfied with their lives but are
barely happier than others in moment-to-moment experience, tend to be more
tense, and do not spend more time in particularly enjoyable activities.
 Moreover, the effect of income on life satisfaction seems to be transient. We
argue that people exaggerate the contribution of income to happiness because
they focus, in part, on conventional achievements when evaluating their life or
the lives of others.
 1 Princeton University, Princeton, NJ 08544, USA.
2 National Bureau of Economic Research, Cambridge, MA 02138, USA.
3 Rady School of Management, University of California, San Diego, San Diego, CA 92093, USA.
4 Department of Psychology, University of Michigan, Ann Arbor, MI 48106, USA.
5 Stony Brook University, Stony Brook, NY, 11794, USA.

84.  Proceedings of the National Academy of Sciences
 Preferences and choices can affect long-term
happiness.
 German Study
▪ Marry someone who is not neurotic
▪ Focus more on friends, less on material goods
▪ Get involved with making the world a better place
▪ Have a job but also enough time for leisure
▪ Stay physically active
▪ For men, don’t be underweight. For women, don’t be obese.

85.  Don't worry, be happy" may be more than just a wishful mantra. A new
study finds that people's happiness levels can change substantially over
their lifetimes, suggesting that happiness isn't predetermined by genes
or personality.
 Psychologists have long argued that people have a "set point" for
happiness. Regardless of what life brings, the set-point theory
goes, happiness levels tend to be stable. A big life event could create a
boost of joy or a crush of sorrow, but within a few years, people return to
a predetermined level of life satisfaction, according to the theory.
 The new study, which used a nationally representative sample of almost
150,000 German adults, finds the opposite. People's long-term life
satisfaction can change, the researchers report today (Oct. 4) in the
online early edition of the Proceedings of the National Academy of
Sciences. In fact, a substantial number of people followed over 25 years
saw their happiness levels shift by one-third or more.

86.  The study also echoed previous happiness research in finding that
money doesn't buy happiness.
 "People with a lot of money are more satisfied with their lives...
but mainly due to the more interesting and challenging jobs they
have," study author Gert Wagner, a researcher at the Max Planck
Institute for Human Development in Germany, told LiveScience.
"Money is simply a byproduct of good and satisfying jobs. If you
want to be satisfied with your life, you must spend time with your
friends and your family."
 Wagner said that previous work suggests findings on happiness
from one developed country, like Germany, should also hold true
for another, such as the United States. In fact, a study in May
found that in the United States. happiness tends to increase with
age.

87. Happiness Study
 I'm happier than you
 The researchers used data from a study of German adults spanning from 1984 to 2008. Each year, the participants answered
questions on their life satisfaction, life goals and other measures like how much they exercise and socialize.
 By averaging life-satisfaction responses to even out any short-term effects, the researchers plotted out the respondents' happiness
by percentiles. Someone in the 99th percentile, for example, would be happier than 99 percent of the study participants.
 People shifted in the rankings — and thus in their levels of happiness — quite a bit. Just over 38 percent changed their position in the
distribution by 25 percentiles or more during the study period. About 25 percent changed by 33.3 percentiles or more, and 11.8
percent changed by 50 percentiles.
 Feel-good factors
 So what contributed to long-term happiness? The researchers found several correlations between life choices and life satisfaction:
 Marry well: The personality traits of partners influenced people's happiness. Neuroticism, or a tendency toward anxiety, emotional
instability and depression, was most influential. People who married or partnered with neurotic people were less likely to be happy
than people who married non-neurotic types.
 Focus on the family: People who assigned relatively high value to altruistic and family goals compared with career goals were
happier. Women were also happier when their male partners ranked family goals high.
 Go to church: People who went to church more often were happier, though the study can't determine whether the happiness is
related to religious views or to the social circle religious organizations offer.
 Work, but not too much (or too little): People's happiness matched how well they felt their work hours matched their desired work
hours. In other words, people who worked more or fewer hours than they preferred were less happy. Working less or being
unemployed was worse than working too much, presumably because underemployment is a financial blow, the researchers wrote.
 Get social, and get moving: Social interaction and exercise were both associated with happiness. Working out made people happier
regardless of body weight. The only correlation between body weight and happiness was that underweight men and obese women
were more likely to be unhappy.

88. Happiness Study
 Mysteries of happiness
 "In its extreme form, set-point theory was never credible," Daniel Kahneman, an emeritus professor of psychology at
Princeton University and the winner of the 2002 Nobel Prize in Economic Sciences, told LiveScience. "If it was taken to
mean that the only factor that determines happiness or life satisfaction is genetic, so that people always come back to
exactly to the same point, this was utterly incredible."
 The current study is a useful demonstration that life changes can influence people's life satisfaction, said Kahneman, who
was not involved in the research. However, the correlations between certain goals and traits and happiness doesn't
necessarily answer the nature-versus-nurture question.
 "They're suggesting that the goals are chosen. But the goals may be part of personality," and thus partially genetic, he
said. "The fact that goals matter, like altruism and materialism, that really doesn't help us distinguish between personality
and circumstances."
 More studies are needed that track large populations of people after influential changes, like the enactment of new laws,
said Andrew Oswald, a professor of behavioral science at the University of Warwick who studies happiness but was not
involved in the current study. By comparing people who lived under, say, a new state tax law that affected income to those
who lived in a nearby state without the law, researchers could begin to look at happiness in a more experimental way, he
said.
 "The key thing is that life events good and bad do shape happiness over long periods," Oswald said. "We are, in part, the
product of our experiences. It's not all born into us."
 7 Ways the Mind and Body Change with Age
 Happiness is Being Old, Male and Republican
 10 Ways to Keep Your Mind Sharp