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[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.
Heuristics and Biases
A short review of these subjects on a PowerPoint like format.
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.