Judgment Heuristics and Biases - Essay Example

Research paper Judgment heuristic and biases Decision making is a complex process. Economic behavior of people is dependant on the surrounding parameters like knowledge of the problem (either more or less), the conditions , statistical data , uncertainty which is expressed in numerical form as odds or subjective probabilities(Tversky and Kahneman, 1124). Judgmental heuristics are sometimes used to make this process simpler, to make better predictions and to make improved policy prescriptions. Judgmental heuristics are principles or methods by which one makes assessment or judgment of probability simpler. These are "rules of thumb", educated guesses, intuitive judgments or simply common sense (Wikipedia). Generally beliefs concerning the uncertain events are expressed by statements like "I think thator chances are etc. These are sometimes expressed in numerical form as odd or subjective probabilities. Heuristics are simple, efficient rules, fine-tuned by evolutionary processes or learned, which have been proposed to explain how people make decisions, judgments and solve problems, typically when facing serious problems or in case of inadequate information (Tversky, Kahneman,1124). People often follow a limited number of heuristic principles in day-to-day life, which reduces the process of assessing values and probabilities to much more simple judgmental operations. These heuristics are very useful but at times can leads to severe and systematic errors (Tversky, Kahneman, 1124). The most common types of heuristics used to assess probabilities and to predict values are the representative heuristic, the availability heuristic and the adjustment and anchoring heuristic. Representative Heuristic In case of representative heuristics (Tversky and Kahneman, 1126), the likelihood of an event is judged based upon the extent to which it represents the essential features of the parent population or the generating process. Representative heuristic is generally used by people to make judgment or impression about someone or something. (Koning, 1) It is usually employed while deciding the probability whether or not an object or event A belongs to class or process B. (Tversky and Kahneman, 1131) For illustration of representative heuristic let us consider the example of Steve who has been described by his neighbor as "very shy and withdrawn, invariably helpful but with little interest in people or in world of reality. A meek and tidy soul, he has a need for order and structure and a passion for detail. How are people going to judge the possible occupation of Steve from a list of possibilities (e.g. farmer, salesman, librarian, airline pilot or physician) In the representative heuristic, the probability of is assessed by the degree to which he is representative of, or similar to, in this case say a librarian, the stereotype of a librarian. Research with similar type of problems shows that probability and similarity plays equal important role in case of occupation of people. This method of arriving at a particular decision based on the similarity or the representativeness leads to erroneous results affecting the ultimate outcome because similarity is not influenced by the factors which influence judgment probability (Tversky and Kahneman, 1131). There are some drawbacks of representative heuristic which can be rectified by considering the following parameters. Insensitivity to prior probability of outcomes (Base-rate neglect): The base rate fallacy, also called base rate neglect, is an error that occurs when the conditional probability of some hypothesis given some evidence is assessed without taking sufficient account of the "base rate" or "prior probability" of hypothesis (Wikipedia). The prior probability or base-rate frequency of the final decision has a great deal of effect on the probability. In the above example, the fact that there are many more farmers than librarians in the population should be considered while judging, for an estimation of probability that Steve is a librarian rather than a farmer. People tend to evaluate the probability based on the similarity of Steve to the stereotype of either one and do not consider the base-rate frequency. This is what happens in representative heuristics where decisions or judgments are based only on the similarity or the representativeness of the stereotypes to the given categories neglecting relevant base rate or the prior probability and thus could lead to errors. Insensitivity to sample size: People fail to realize the influence of sample size in case of judgments. Judgements are dominated by sample proportions than the sample size which plays an important role in determining the chances of the particular sequence. Misconceptions of chance: People expect that a sequence of events generated by a random process will represent the essential characteristics of that process even when the sequence is short. If one observes a long run of red on the roulette wheel, people wrongly believe that the next will be black. This is perhaps because occurrence of black will be more representative than further repetition of red. This is known as gambler's fallacy, another example of misconception of chance that widely occurs in representative heuristics. Insensitivity to predictability: When someone is asked to predict the future profit of a company, or the possible outcome of a football match, or possible value of a stock, people use representative heuristics to make these predictions. For example, if people are asked to predict the future of say a few companies whose descriptions are made available to them. People rely on the description and based on that make predictions. A company whose description is more favorable is said to make high profit because this seems more representative with the description, the one whose description is not much favorable is termed as mediocre and so on. All these decisions are based on how much they are representative of the description. But what is overlooked here is the actual reliability on the evidence which leads to possible errors. The illusion of validity: People often predict by selecting the outcome (e.g. an occupation) that is most representative of the input (e.g. the description of a person). The unwarranted confidence which is produced by a good fit between the predicted outcome and the input information is called the illusion of validity. For example people can always predict the final grade point average with confidence of a student whose first year grades were all B's than the ones who has been getting A's and C's. It has been seen that people show more confidence if the input variables are correlated. But the statistics of correlation shows that we get more accurate results if the input variables are independent of each other that are if they are not correlated hence applying representative heuristics may lead to errors in judgment. Misconceptions of regression: When a large group of persons are examined on two equivalent versions of an aptitude test, if one selects 10 persons among them who did better on one of the two versions, he will find their performance in the second version somewhat disappointing. These observations illustrate a general phenomenon known as regression towards the mean. Psychologists Daniel Kahneman and Amos Tversky attempted to explain this finding in terms of the representative heuristic. Richard Nisbett has argued that some attributional biases like the fundamental attribution error are instances of the base rate error: people underutilize "consensus information" (the "base rate") about how others behaved in similar situations and instead prefer simpler dispositional attributions. Jon Krosnick, a professor in Communication at Stanford, in his work has proposed that the effects that Kahneman and Tversky saw in their work may be partially attributed to information order effects. When the order of information was reversed - with probability figures coming later, a lot of the effects were mitigated. Availability heuristics The relative frequency of an event often depends on the availability or accessability of the object or the event under perception memory or construction of imagination. This is availability heuristics. (Garns, 1) The availability heuristic is a way we come to a decision by what is readily available in our minds depending on what we have experienced. At times the assessment of frequency of a class or probability of an event is based on the ease with which these occurrences come to our mind. An important problem faced while applying availability heuristic is that we may bring to mind what is typical for a particular situation which may lead to erroneous decision. The media also plays a large role here as well. Something that is televised or say publicized a lot is recognized more, even though the opposite of the matter may be true. For example, a lot many people consider air travel highly dangerous, thanks to the widespread and often detailed reporting of air disasters in television and other media sources. It is also observed that people usually consider travel by car or train much safer than air travel. The chances of death are rated higher by a plane crash than by a train or a car accident or death by natural disasters, just because of greater coverage of these events by the media, when the actual fact is that death by car accidents are much more common than airline accidents. There are some limitations of availability heuristic which can be overcome by considering the following parameters. Biases due to retrievability of instances: When the size of a class is judged by the availability of instances, a class (e.g ladies) whose instances are easily retrieved will appear more numerous, than a class (e.g men) of equal frequency whose instances are less retrievable. For example, in a group of same no of famous men and women, if the no. of women are more highlighted, then there is a tendency that people will think that women are more famous. The impact of having been an eye-witness to a massive accident on the subjective probability of accidents is much more than simply reading about it in a local newspaper. Biases due to the effectiveness of a search set: If one is asked to rate the frequency of abstract words (like love, thought) as well as concrete words (like door, water) in written English. It appears easy if the search is based on the context. It has been found that thinking of context in which an abstract word will appear (like love in love stories) is easier than thinking of context in which a concrete word will appear. Thus the frequency of abstract words will be higher if is measured based on the availability of context in which these words appear. This leads to bias as we think that abstact words appear in a greater frequency than concrete words. Biases of imaginability: Our ability to imagine plays a very important role in deciding and estimating probabilities. If one imagines the risks and dangers associated with an adventurous trip too much in detail, then the expedition appears to be very dangerous indeed, on the other hand the possible risk may not appear or may get underestimated if the related dangers do not simply come to mind or are difficult to even think about. Illusory correlation: This is a bias in judgment of frequency with which co-occurrence of two events is observed. Adjustment and anchoring heuristic: Anchoring or focalism is a cognitive bias that describes the common human tendency to rely too heavily, or "anchor," on one trait or piece of information when making decisions (Wikipedia). Anchoring and adjustment is a psychological heuristic that influences the way people assess probabilities. According to this heuristic, people start with an implicitly suggested reference point (the "anchor") and make adjustments to it to reach their estimate (Wikipedia). Adjustment and anchoring is generally used in numerical predictions when a relevant value is available. (Tversky and Kahneman, 1128) The anchoring and adjustment heuristic was first theorized by Amos Tversky and Daniel Kahneman. In one of their studies, they showed that when asked to guess the percentage of African nations which are members of the United Nations, people who were first asked "Was it more or less than 45%" guessed lower values than those who had been asked if it was more or less than 65%. Similar pattern has been observed in other experiments for a wide variety of different subjects of estimation. It has been suggested that anchoring and adjustment affects other kinds of estimates, like perceptions of fair prices and good deals. There are some limitations of anchoring heuristic which can be overcome by considering the following parameters. Insufficient adjustment: Anchoring is also observed when the subject finalizes his estimate on the result of some incomplete computations and information. This is called insufficient adjustment and leads to biases. Biases in the evaluation of conjunctive and disjunctive events: The overall probability is overestimated in case of conjunctive problems and underestimated in case of disjunctive problems as a consequence of anchoring, Anchoring in the assessment of subjective probability distributions: People reflect more certainty when they state narrow confidence intervals than is justified by their knowledge of the assessed quantities. Proper scoring rules alone cannot rule out this bias. External calibration measures are required to eliminate this bias.. Conclusion Heuristics is something that is generally employed by people while making judgments. These are simple short cuts which save us from carrying out statistical processing and computation. It helps people make decisions faster, and cope with multiple affordances at the same time. Heuristics are very useful economically and highly effective, but at times can lead to severe and systematic errors. Though it are useful in making economic predictions but at the same time heuristics are equally prone to a number of judgmental biases which can be prevented by having a proper understanding of this topic. While using heuristics we do not take into consideration all the information available, we miscompute probabilities and misevaluate thus coming to wrong judgment. This is not the case only with laymen , even experienced researchers and economists are prone to these biases. People consider heuristic judgment to be a means to take decision fast and solve a problem in hand. What they do not recognize is that this technique of making decision is prone to biases at times. We as humans often think our judgment is the most correct and most accurate which at times can lead to errors. Thus we need to realize that overconfidence is not what is needed, as human cognition has its own limits. People should utilize the fundamental statistical rules, regression towards the mean or the effect of sample size on the sampling variability. As not everyone is exposed to these in normal life, it is important that we check out our predictions with the actual results. In this way we will be able to rule out biases to some extent. Overall what can be concluded is that while making judgments one should consider heuristics together with all the available information, his own knowledge and probability laws. One needs to think about both the sides of the coin, assess the probability of the ultimate outcome, use reliable information that is available not excluding the prior probabilities and then finally come to the final outcome. This will prevent us from making hasty decisions and make our judgment closest to the actual outcome, avoiding the possible biases. References: Tversky, Amos and Kahneman, Daniel. "Judgment under Uncertainty: Heuristics and Biases" Science, New Series, Vol. 185, No. 4157. (Sep. 27, 1974), pp. 1124-131. Koning, Karin. "Judgmental heuristics." Untitled. WIU. n.d. Web. 21 July 2008. . Garns, Rudy. "Judgment heuristics and biases." Judgment heuristic and biases. N.p., n.d. Web. 22 July 2008. . "Anchoring -Wikipedia the free encyclopedia" Anchoring. N.p., 6 June 2008Web 30 July 2008< http://en.wikipedia.org/wiki/Anchoring_and_adjustment > "Base rate fallacy- Wikipedia the free encyclopedia" Base rate fallacy. N.p , 28 July 2008 Web 1 Aug 2008< http://en.wikipedia.org/wiki/Base_rate_fallacy > "Heuristic- Wikipedia the free encyclopedia" Heuristic.N.p., 18 July 2008 Web 01 Aug 2008 < http://en.wikipedia.org/wiki/Heuristic> Proposal: Judgment heuristic and biases Decision making is a complex process. Judgmental heuristics are sometimes used to make this process simpler. These are "rules of thumb", educated guesses, intuitive judgments or simply common sense. Heuristics are simple, efficient rules, fine-tuned by evolutionary processes or learned, which have been proposed to explain how people make decisions, judgments and solve problems, typically when facing serious problems or in case of inadequate information(Tversky, Kahneman,1124). These heuristics are very useful but at times can leads to severe and systematic errors. (Tversky, Kahneman, 1124) The most common types of heuristics used to assess probabilities and to predict values are the representative heuristic, the availability heuristic and the adjustment and anchoring heuristic. In case of representative heuristics (Tversky and Kahneman, 1126), the likelihood of an event is judged based upon the extent to which it represents the essential features of the parent population or the generating process. Representative heuristic is generally used by people to make judgment or impression about someone or something. (Koning, 1) The relative frequency of an event often depends on the availability or accessability of the object or the event under perception memory or construction of imagination. This is availability heuristics. (Garns, 1) Adjustment and anchoring is generally used in numerical predictions when a relevant value is available. (Tversky and Kahneman, 1128) There are many decisions that are based on heuristics. How do people cope with the complexities of the world business and economy, the uncertain behavior of friends and adversaries, or their own changing tastes and personalities When are people's judgments prone to bias, and what is responsible for their biases (Tversky and Kahneman, 1124) The selected topic deals with the way we make decisions, specifically when no dependable criteria for statistical evidence are available. This is something which is relevant in our day to day life, something that is applicable to each one of us. We as humans often think our judgment is the most correct and most accurate which at times can lead to errors. Thus we need to realize that overconfidence is not what is needed, as human cognition has its own limits. Also, this topic deals with human behavior and psychology which makes its study all the more apt and interesting. References: Tversky, Amos and Kahneman, Daniel. "Judgment under Uncertainty: Heuristics and Biases" Science, New Series, Vol. 185, No. 4157. (Sep. 27, 1974), pp. 1124-131. Koning, Karin. "Judgmental heuristics." Untitled. WIU. n.d. Web. 21 July 2008. . Garns, Rudy. "Judgment heuristics and biases." Judgment heuristic and biases. N.p., n.d. Web. 22 July 2008. . Read More