| |
[Huang]
"Sometimes heuristics are good for making decisions, while
at other times heuristics are bad for making decisions. The reason
for this mixed or nuanced answer is namely (that) heuristics
act faster than rational deliberation, but precisely because
of their speed, heuristics can mislead us into systematic errors
in making decisions". |
People
use heuristics to control extreme complexity. Heuristics are rules
or strategies for information processing, which help to find a quick
but not necessarily optimal decision. Heuristics are used when people
are overwhelmed by information processing, and help to find a quick,
but not necessarily optimal, solution. |
Heuristics
in Investor Decision Making, Dreman
"Despite what many economists and financial theorists assume,
people are not good intuitive statisticians, particularly under
difficult conditions. They do not calculate odds properly when making
investment decisions, which causes consistent errors. People are
swamped with information and react consciously to only a small part
of it. When overwhelmed with facts, we select a small part of them
and usually reach a different conclusion from what the entire data
set would suggest. Researchers have found that people react to this
avalanche of data by adopting shortcuts or rules of thumb rather
than formally calculating the actual odds of a given outcome. Known
to psychologists as judgmental heuristics in technical jargon, these
shortcuts are learning and simplifying strategies we use for managing
large amounts of information. Backed by the experience of a lifetime,
most of these judgmental shortcuts work exceptionally well, and
allow us to cope with data that would otherwise overwhelm us. We
also use selective processes in dealing with probabilities: in many
of our decisions and judgments, we tend to be intuitive statisticians.
We apply mental shortcuts that work well most of the time. We think
our odds of survival are better when driving at 55 miles an hour
that at 90 miles an hour, although few of us have ever bothered
to check the actual numbers. A professional basketball team is likely
to beat an amateur one, a discount computer store will probably
sell personal computers more cheaply than Macy's or Bloomingdale's.
And we might expect to get to a city 300 miles away faster by air
than by ground (if it is not a United Express flight to a Colorado
ski resort). There are dozens of examples that such procedures are
valuable and immensely timesaving. But being an intuitive statistician
has limitations as well as blessings. The very simplifying processes
that are normally efficient time-savers lead to systematic mistakes
in investment decisions. They can make you believe the odds are
dramatically different from what they actually are. As a result,
they consistently shortchange the investor. The distortions produced
by the subjectively calculated probabilities are large, systematic,
and difficult to eliminate, even after people have been made fully
aware of them". |
Judgement
under Uncertainty: Heuristics and Biases, Tversky and Kahneman,
1974
A heuristic is a strategy that can be applied to a variety of problems
and that usually - but not always - yields a correct solution. People
often use heuristics (or shortcuts) that reduce complex problem
solving to more simple judgmental operations. Three of the most
popular heuristics are discussed in this article: Representativeness
heuristic: What is the probability that person A (Steve,
a very shy and withdrawn man) belongs to group B (librarians) or
C (exotic dancers)? In answering such questions, people typically
evaluate the probabilities by the degree to which A is representative
of B or C (Steve´s shyness seems to be more representative for librarians
than for exotic dancers) and sometimes neglect base rates (there
are far more exotic dancers than librarians in a certain sample).
Availability heuristic: This heuristic is used to evaluate
the frequency or likelihood of an event on the basis of how quickly
instances or associations come to mind. When examples or associations
are easily brought to mind, this fact leads to an overestimation
of the frequency or likelihood of this event. Example: People are
overestimating the divorce rate if they can quickly find examples
of divorced friends. People tend to be biased by information that
is easier to recall. They are swayed by information that is vivid,
well-publicized, or recent. People also tend to be biased by examples
that they can easily retrieve. Anchoring and adjustment:
People who have to make judgements 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 person's productivity, the anchor for your final
(adjusted) judgement 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. |
 [Hester]
"Kahneman and Tversky found that when people make a decision
they start from a reference point (the "Anchor").
This is the case even if the reference point has little to do with
the decision. For example, in one study researchers spun a roulette-type
wheel labeled with the numbers 1-100. Then they asked participants
the percentage of African countries that were members of the United
Nations. The responses were heavily dependent on the number the
wheel landed on. For example, when the number on the wheel was 65,
the median guess was 45 percent of countries. When the wheel landed
on 10, the median guess was 25. Numerous other studies duplicated
these results. Cornell MBA students were asked what year Attila
the Hun was defeated. Before answering the question, the students
were asked to add 400 to the last three digits of their phone number.
This nearly random number affected the student's answers. When the
sum was between 400 and 599, the student's average guess was that
Attila was defeated in AD 629. When the number was between 1200
and 1399, their average guess was AD 988. (Attila the Hun was defeated
in AD 451)". |
Hazardous
Heuristics, SUNSTEIN
New work on heuristics and biases has explored the role of emotions
and affect; the idea of "dual processing"; the place of
heuristics and biases outside of the laboratory; and the implications
of heuristics and biases for policy and law. This review-essay focuses
on certain aspects of "Heuristics and Biases: The Psychology
of Intuitive Judgment", edited by Thomas Gilovich, Dale Griffin,
and Daniel Kahneman. An understanding of heuristics and biases casts
light on many issues in law, involving jury awards, risk regulation,
and political economy in general. Some attention is given to the
possibility of "moral heuristics" - rules of thumb, for
purposes of morality, that generally work well but that also systematically
misfire.
|
|
Darwins
Mind: The evolutionary foundations of heuristics and biases,
Montier, 2002
The catalogue of biases that cognitive psychologists have built
up over the last three decades seem to have stem from one of three
roots ? self- deception, heuristic simplification (including affect),
and social interaction. This paper attempts to explore the evolutionary
basis of each of these roots. The simple truth is that we aren?t
adapted to face the world as it is today. We evolved in a very different
environment, and it is that ancestral evolutionary environment that
governs the way in which we think and feel. We can learn to push
our minds into alternative ways of thinking, but it isn?t easy as
we have to overcome the limits to learning posed by self-deception.
In addition, we need to practice the reframing of data into more
evolutionary familiar forms if we are to process it correctly. |
|
|
|
