Transcript Errors versus suboptimal rules

Rischio ed Incertezza nelle decisioni
economiche: un approccio “behavioral”
Massimo Egidi, Luiss University
[email protected]
Comment :The Minsky Moment
by John Cassidy
February 4, 2008 (The New Yorker)
Comment :The Minsky Moment
by John Cassidy
February 4, 2008
Twenty-five years ago, when most economists were extolling
the virtues of financial deregulation and innovation, a
maverick named Hyman P. Minsky maintained a more
negative view of Wall Street; in fact, he noted that bankers,
traders, and other financiers periodically played the role of
arsonists, setting the entire economy ablaze. Wall Street
encouraged businesses and individuals to take on too much
risk, he believed, generating ruinous boom-and-bust cycles.
The only way to break this pattern was for the government
to step in and regulate the moneymen.
Many of Minsky’s colleagues regarded his “financial-instability
hypothesis,” which he first developed in the nineteensixties, as radical, if not crackpot.
by John Cassidy
February 4, 2008
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Today, with the subprime crisis seemingly on the verge of
metamorphosing into a recession, references to it have
become commonplace on financial Web sites and in the
reports of Wall Street analysts. Minsky’s hypothesis is well
worth revisiting. In trying to revive the economy, President
Bush and the House have already agreed on the outlines of
a “stimulus package,” but the first stage in curing any
malady is making a correct diagnosis.
Minsky, who died in 1996, at the age of seventy-seven,
earned a Ph.D. from Harvard and taught at Brown,
Berkeley, and Washington University. He didn’t have
anything against financial institutions—for many years, he
served as a director of the Mark Twain Bank, in St. Louis—
but he knew more about how they worked than most
deskbound economists.
Comment :The Minsky Moment
by John Cassidy
February 4, 2008
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There are basically five stages in Minsky’s model of the credit
cycle: displacement, boom, euphoria, profit taking, and panic. A
displacement occurs when investors get excited about something—
an invention, such as the Internet, or a war, or an abrupt change
of economic policy. The current cycle began in 2003, with the Fed
chief Alan Greenspan’s decision to reduce short-term interest rates
to one per cent, and an unexpected influx of foreign money,
particularly Chinese money, into U.S. Treasury bonds. With the
cost of borrowing—mortgage rates, in particular—at historic lows,
a speculative real-estate boom quickly developed that was much
bigger, in terms of over-all valuation, than the previous bubble in
technology stocks.
As a boom leads to euphoria, Minsky said, banks and other
commercial lenders extend credit to ever more dubious borrowers,
often creating new financial instruments to do the job. During the
nineteen-eighties, junk bonds played that role.
Comment :The Minsky Moment
by John Cassidy
February 4, 2008
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More recently, it was the securitization of mortgages, which
enabled banks to provide home loans without worrying if they
would ever be repaid. (Investors who bought the newfangled
securities would be left to deal with any defaults.) Then, at the top
of the market (in this case, mid-2006), some smart traders start
to cash in their profits.
The onset of panic is usually heralded by a dramatic effect: in July,
two Bear Stearns hedge funds that had invested heavily in
mortgage securities collapsed. Six months and four interest-rate
cuts later, Ben Bernanke and his colleagues at the Fed are
struggling to contain the bust. Despite last week’s rebound, the
outlook remains grim. According to Dean Baker, the co-director of
the Center for Economic and Policy Research, average house prices
are falling nationwide at an annual rate of more than ten per cent,
something not seen since before the Second World War. This
means that American households are getting poorer at a rate of
more than two trillion dollars a year.
 The psychology of Risk and
Uncertainty since Frank Knight
Thaler :Behavioral Finance
 In the traditional framework where agents are rational
and there are no frictions, a security’s price equals its
“fundamental value”. This is the discounted sum of
expected future cash flows, where in forming
expectations, investors correctly process all available
information, and where the discount rate is consistent
with a normatively acceptable preference
specification. The hypothesis that actual prices reflect
fundamental values is the Efficient Markets Hypothesis
(EMH). Put simply, under this hypothesis, “prices are
right”, in that they are set by agents who understand
Bayes’ law and have sensible preferences.
Thaler :Behavioral Finance
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Behavioral finance argues that some features of asset
prices are most plausibly interpreted as deviations from
fundamental value, and that these deviations are brought
about by the presence of traders who are not fully rational.
A long-standing objection to this view that goes back to
Friedman (1953) is that rational traders will quickly undo
any dislocations caused by irrational traders. To illustrate
the argument, suppose that the fundamental value of a
share of Ford is $20. Imagine that a group of irrational
traders becomes excessively pessimistic about Ford’s future
prospects and through its selling, pushes the price to $15.
Defenders of the EMH argue that rational traders, sensing
an attractive opportunity, will buy the security at its bargain
price and at the same time, hedge their bet by shorting a
“substitute” security, such as General Motors, that has
similar cash flows to Ford in future states of the world. The
buying pressure on Ford shares will then bring their price
back to fundamental value.
Thaler :Behavioral Finance
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Friedman’s line of argument is initially compelling, but it
has not survived careful theoretical scrutiny. In essence, it
is based on two assertions. First, as soon as there is a
deviation from fundamental value – in short, a mispricing –
an attractive investment opportunity is created. Second,
rational traders will immediately snap up the opportunity,
thereby correcting the mispricing. Behavioral finance does
not take issue with the second step in this argument: when
attractive investment opportunities come to light, it is hard
to believe that they are not quickly exploited. Rather, it
disputes the first step. The argument is that even when an
asset is wildly mispriced, strategies designed to correct the
mispricing can be both risky and costly, rendering them
unattractive. As a result, the mispricing can remain
unchallenged.
 In 1907, Royal Dutch and Shell Transport, at the time
completely independent companies, agreed to merge
their interests on a 60:40 basis while remaining
separate entities.
 Shares of Royal Dutch, which are primarily traded in
the USA and in the Netherlands, are a claim to 60% of
the total cash flow of the two companies, while Shell,
which trades primarily in the UK, is a claim to the
remaining 40%. If prices equal fundamental value,
the market value of Royal Dutch equity should always
be 1.5 times the market value of Shell equity.
Remarkably, it isn’t.
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Figure 1, taken from Froot and Dabora’s (1999) analysis of this
case, shows the ratio of Royal Dutch equity value to Shell equity
value relative to the efficient markets benchmark of 1.5. The
picture provides strong evidence of a persistent inefficiency.
Moreover, the deviations are not small. Royal Dutch is sometimes
35% underpriced relative to parity, and sometimes 15%
overpriced.
Expectations
Overconfidence.
Optimism and wishful thinking.
Representativeness
Anchoring.
Linda is 31 years old, single, outspoken, and very
bright. She majored in philosophy. As a student, she
was deeply concerned with issues of discrimination
and socialjustice, and also participated in anti-nuclear
demonstrations.
 When asked which of “Linda is a bank teller”
(statement A) and “Linda is a bank teller and is active
in the feminist movement” (statement B) is more
likely, subjects typically assign greater probability to
B. This is, of course, impossible.
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 « Lorsqu'a la loterie de France un numéro n'est pas sorti depuis
longtemps, la foule s'empresse de le couvrir de mises. Elle juge que le
numéro resté longtemps sans sortir doit, au premier tirage, sortir de
préférence aux autres. Une erreur aussi commune me parait tenir à une
illusion par laquelle on se reporte involontairement à l'origine des
événements. Il est, par exemple, très peu vraisemblable qu'au jeu de
croix ou pile on amènera croix dix fois de suite. Cette invraisemblance
qui nous frappe encore, lorsqu'il est arrivé neuf fois, nous porte à
croire qu'au dixième coup pile arrivera. Cependant le passé, en
indiquant dans la pièce une plus grande pente que pour pile, rend le
premier dé ces événements plus probable que l'autre; il augmente,
comme on 1'a vu, la probabilité d’amener croix au coup suivant. »
(Laplace 1814, introduction, CXIII)
Reihnardt
Selten
 “Modern mainstream economic theory is largely based on an
unrealistic picture of human decision making. Economic agents are
portrayed as fully rational Bayesian maximizers of subjective utility.
 This view of economics is not based on empirical evidence, but rather
on the simultaneous axiomatization of utility and subjective
probability. In the fundamental book of Savage the axioms are
consistency requirements on actions with actions defined as mappings
from states of the world to consequences (Savage 1954).
 One can only admire the imposing structure built by Savage. It has a
strong intellectual appeal as a concept of ideal rationality. However, it
is wrong to assume that human beings conform to this ideal.”
(Reihnardt Selten, 1999)
Maurice
Allais
Maurice Allais pointed to conclusions the reverse of those obtained by
Savage’s approach. He carried out experiments on individual
preferences - using an ingenious falsificationist method - that showed
systematic failures in the theory’s predictions. In 1952, at a
symposium held in Paris, Allais presented two studies in which he
criticized the descriptive and predictive power of the ‘American
School’s’ choice theory, and especially Friedman’s version of it
(Allais, 1953). He submitted experiments in which subjects faced with
alternative choices in conditions of risk systematically violate the
assumptions of the Expected Utility theory.
 Many proposals were put forward, especially from the mid 1970’s
onwards, and all of them based on the attempt of relaxing or slightly
modifying the original axioms of expected utility Theory. Among
others, we have:
- Weighted Utility Theory (Chew and MacCrimmon);
- Regret Theory ( Loomes and Sugden ,1982);
- Disappointment Theory, (Gul ,1991).
 None of them had a statistical confirmation over the full domain of
applicability (Tversky and Kahnemann, 1987, p.88).
 Therefore this response to Allais’ criticism did not prove successful.
Only gradually economists came to recognize the systematic
discrepancy between the predictions of expected utility theory and
economic behavior; this opened a dramatic and still unsolved
question: how to model in a more realistic way human behavior in
economics.
The “Classical” Experiment - Framing Effect
 Problem 1
Assume to be 300 $ richer than you are today. Choose between:
- A the certainty of earning 100$
- B 50% probability of winning 200$ and 50% of not winning
anything
 Problem 2
Assume you are 500 $ richer than today. Choose between:
- C A sure loss of 100$
- D 50% chance of not losing anything and 50% chance of
200$
losing
From Kahneman’s Nobel Lecture
From Camerer,Loewenstein,Prelec “Neuroeconomics….”
 On the basis of this function, an immediate loss is given
a more negative evaluation than the positive evaluation
of an immediate gain to the same amount. Moreover,
given the non-linearity of the subjective value function,
losses or gains with the same expected value are
assessed differently.
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 …. behavior emerges from the interplay between controlled and
automatic systems on the one hand, and between cognitive and
affective systems on the other. Moreover, many behaviors that are
clearly established to be caused by automatic or affective systems are
interpreted by human subjects, spuriously, as the product of cognitive
deliberation (Wolford, Miller and Gazzaniga 2000). The deliberative
system, which is the system that is responsible for making sense of
behavior, does not have perfect access to the output of the other
systems, and exaggerates the importance of processes it understands
when it attempts to make sense of the body’s behavior. ( Camerer
Loewenstein Prelec )
3 Automatic and deliberate thinking : interferences
 While not denying that deliberation is always an option for human
decision making, neuroscience research points to two generic
inadequacies of this approach. First, much of the brain is constructed
to support ‘automatic’ processes (Bargh, Chaiken, Raymond and
Hymes 1996; Bargh and Chartrand 1999; Schneider and Shiffrin 1977;
Shiffrin and Schneider 1977), which are faster than conscious
deliberations and which occur with little or no awareness or feeling of
effort. Because the person has little or no introspective access to, or
volitional control over them, the behavior these processes generate
need not conform to normative axioms of inference and choice (and
hence cannot be adequately represented by the usual maximization
models). (Camerer Loewenstein Prelec)
4 Brain’s regions involved in automatic / in deliberate / thinking
Automatic and controlled processes can be very roughly distinguished
by where they occur in the brain (Lieberman, Gaunt, Gilbert and
Trope 2002). Regions that support cognitive automatic activity are
concentrated in the back (occipital), top (parietal) and side (temporal)
parts of the brain (see Figure 1). The amygdala, buried below the
cortex, is responsible for many important automatic affective
responses, especially fear. Controlled processes occur mainly in the
front (orbital and prefrontal) parts of the brain. The prefrontal cortex
(pFC) is sometimes called the "executive" region, because it draws
inputs from almost all other regions, integrates them to form near and
long-term goals, and plans actions that take these goals into account
(Shallice and Burgess, 1998). The prefrontal area is the region that has
grown the most in the course of human evolution and which,
therefore, most sharply differentiates us from our closest primate
relatives (Manuck, Flory, Muldoon and Ferrell 2003). (Camerer
Loewenstein Prelec)
5 Modelling the interferences between automatic and
deliberate thinking : Modularity and specialization
 Specialization: In a process that is not well understood, the brain
figures out how to do the tasks it is assigned, efficiently, using the
modules it has at its disposal. When the brain is confronted with a new
problem it initially draws heavily on diverse modules, including,
often, the prefrontal cortex. But over time, activity becomes more
streamlined, concentrating in modules that specialized in processing
relevant to the task. In one study, subjects' brains were imaged as they
played the computer game Tetris, which requires rapid hand-eye
coordination and spatial reasoning. When subjects began playing, they
were highly aroused and many parts of the brain were active. As they
got better at the game, overall blood-flow to the brain decreased
markedly, and activity became localized in only a few brain regions.
(Camerer Loewenstein Prelec)
Computer game Tetris
Interactions between automatic and deliberate thinking
 In one now famous study, Gobet and Simon (1996) tested memory
for configurations of chess pieces positioned on a chess board. They
found that expert chess players were able to store the positions of
players almost instantly – but only if they were in positions
corresponding to a plausible game. For randomly arranged chess
pieces, the experts were not much better than novices. More recent
research in decision making suggests that this is a far more general
phenomenon; much decision making takes the form of pattern
matching rather than of an explicit weighing of costs and benefits
(e.g., Leboeuf 2002; Medin and Bazerman 1999) ( Camerer
Loewenstein Prelec )
5
 With experience at a task or problem, the brain seems to gradually shift
toward modules that can solve problems automatically and efficiently
with low-effort. It naturally follows, that, for a wide range of problems
and tasks, people will rely on cognitive capabilities that are relatively
well developed, such as visual perception and object recognition rather
than operations that we are not very good at, like decomposing and then
summing up costs and benefits.
 .. Decision making may be explained as the product of a complex
interaction between two processes: automatic pattern matching and
an explicit weighing of costs and benefits.
 ( Camerer Loewenstein Prelec )
End