Psychology Of Decision Making Research Paper

1. Normative Theories Of Decision Making

Traditionally choices are assumed to be consequential, and options are evaluated using prior assessments of beliefs and values. Generally, tasks in this field of research therefore have a probability component or a value component, or both.

Edwards (1954) provided the first major review of research on human judgment and decision making. He argued that normative and prescriptive models based on economic and statistical theory should also be relevant to psychologists interested in human judgment and decision making. Edwards introduced SEU theory, which decomposes decisions or choices in probabilities and preferences, and provides a set of rules for combining beliefs (probabilities) and preferences (‘values’ or ‘utilities’). SEU maximization is considered an appropriate normative rule for decision making under risk or uncertainty. The theory is normative or prescriptive because it specifies how decisions should be made. If one accepts the axioms upon which it is based, then the most rational choice or decision is the one specified by the theory as having the highest subjectively expected utility.

The axioms of SEU theory are all based on the work of Von Neumann and Morgenstern (1944). Their work showed that if a person’s choices follow certain rules or axioms, it is possible to derive utilities in such a way that a preference for a specific alternative implies that its expected utility is greater than that of the other alternative(s). Their approach assumes that it is possible to associate a real number with each possible consequence of the relevant alternatives. This number (e.g., a number between one and 10) represents the utility of that consequence for the individual. The expected utility of a specific alternative is then the sum of the numbers associated with each possible consequence weighted by the probability that each consequence will occur. Thus a positive or negative outcome will be weighted by the probability of its occurrence; for example a certain outcome will be multiplied by 1.0, an outcome with a probability of 0.5 will be multiplied by 0.5, etc.

To illustrate this let us compare the two jobs in terms of three possible outcomes or consequences. These are initial salary, career opportunities and atmosphere. Initial salary is higher at firm A than at firm B, and receives ratings of eight and seven respectively on a scale ranging from one to 10. Career opportunities seem better at firm B but this element cannot be assessed with certainty. Let us assume that career opportunities get ratings of eight (firm A) and nine (firm B), but that you are less certain of your assessment of firm A (subjective probability of 0.7), than of firm B (subjective probability of 0.9). Finally, the atmosphere at firm B seemed to be better than at firm A; your scores are eight and seven, respectively. You spent quite some time at firm B and are certain about your judgment of the atmosphere (it seemed a great team of colleagues). You are less certain about your verdict of the atmosphere at firm A because you spent less time there (probabilities of 1.0 (B) vs. 0.9 (A)). This leads to the following scores: Firm A (1.0 * 8+0.7 * 8+0.8 * 7= 20.0), Firm B (1.0 * 7+0.9 * 9 +1.0 * 8) = 23.2. Thus, according to this analysis, the job offer of firm B is more attractive than that of firm A.

As argued by Dawes (1988) the central idea of the Von Neumann and Morgenstern theory is that it provides an important tool to reach a rational decision. SEU theory and related theories have dominated research on decision making since the mid-1940s, and especially the validity of theories such as SEU-theory as adequate descriptions of human choice and decision making has been a dominant theme in this research area. This is partly based on the idea that the study of human decision making should focus on the perceptual and cognitive factors that cause human judgment and decision making to deviate from the predictions of normative models such as SEU theory.

2. Violations And Anomalies

Research in the past decades has resulted in a wide variety of illustrations of systematic violations of principles of rationality. A clear example concerns the violation of the sure thing principle: If one prefers one option to another no matter what happens—or has happened—one should prefer that option without knowing what happens, or has happened. Tversky and Shafir (1992) gave an example of violating this principle. Their respondents were asked to consider a gamble with a 50/50 chance of winning $200 or loosing $100. Generally, a minority of about one third of respondents offered that gamble accept it. This preference pattern changes dramatically if respondents are asked to imagine they had already played the gamble and were now being offered the opportunity to play it a second time. In case they were asked to imagine they had won the first time, nearly 70 percent wanted to play again. If they had been asked to imagine they had lost the first time nearly 60 percent wished to play it a second time.

Another violation is related to so-called sunk costs. It is Saturday afternoon and you are on the brink of leaving for the railway station in order to get to the stadium. You bought your ticket for the match some time ago. It is pouring with rain, you do not feel that well and standing in the rain for 2 hours is not going to help. In sum, you prefer to spend the afternoon at home. Nevertheless, if you decide not to go, you will have ‘wasted’ the money you paid for the ticket. The nonrefundable money already paid for the ticket is termed a sunk cost. Strictly speaking there appears to be no rational argument to do things you would prefer not to do in the interest of honoring the sunk cost. However, people often honor sunk costs. Examples include trivial anecdotes such as the one described above, but also large-scale enterprises such as the development of the Concorde passenger aircraft and the Gulf War.

Other violations concern principles such as transitivity. If people prefer option A over option B and also option B over option C, they should also prefer option A over option C. Violations of transitivity may arise from evaluating alternative options in terms of different dimensions. On some dimensions ‘A’ might be preferred over ‘B’ and ‘C’ while on others ‘C’ might be preferred over ‘A.’ If the salience of the dimensions varies, preferences could differ and one could violate transitivity. Another way in which transitivity may occur is through regret (see below).

The way in which alternative options are presented can also lead to anomalies. It turns out that people often make different choices as a function of the way alternatives are presented or framed, even though the alternatives remain unchanged. One example of framing is to present actions in terms of positive as opposed to negative consequences or vice versa. For example, a 50 percent chance of a successful outcome of surgery may appear to someone to be a more desirable outcome than a 50 percent chance of failure. Several studies provide support for this framing effect. Framing effects have been extensively investigated in decision problems in which the consequences of options are compared to psychologically salient reference points (e.g., a zero point, the status quo). Kahneman and Tversky (1979) found that when asked to choose between $15,000 for sure or a 50 50 chance of $10,000 or $20,000 most people choose the certain option. If one has received the $20,000 and was asked a second choice between returning $5000 or a 50/50 chance of giving back nothing or $10,000 most people choose the latter option. This pair of choices is contradictory. Similarly, when presented with a choice between a sure loss of $100 or a 50 50 chance of a loss of $200 and a 50/50 chance of no loss at all, most people will prefer the uncertain, risky alternative. This risk-seeking preference is in sharp contrast with preferences when the choice is between a sure gain of $100 or a 50 percent chance of a gain of $200 and a 50 percent choice of no gain at all. In this case most people will prefer the certain, risk-avoiding alternative. According to SEU-theory the two options in each problem are equally (un)attractive. Thus, inducing people to adopt a gain frame when choosing between a gamble and a sure thing tends to result in a risk-avoiding preference (the certain alternative). Inducing people to adopt a loss frame, on the other hand, tends to result in a risk-seeking preference (the risky alternative).

A final anomaly concerns violations of response invariance. Choice can also be influenced by the method by which preferences and choices are expressed. For instance, valuing something with money versus making a choice is frequently associated with preference reversals. Another example concerns the effect of choosing versus rejection of alternatives that have both bad and good characteristics. Shafir (1993) argues that a ‘choose’ orientation leads decision makers to search for good qualities of each option, while a ‘reject’ orientation makes them focus on bad qualities. Thus, if one alternative has more clear-cut positive and negative characteristics, and another alternative is relatively bland, the former could both be chosen and rejected depending on the question. Shafir (1993) demonstrated this in the context of awarding custody in which the question was either which parent to award custody or which parent to deny sole custody of the child.

3. Heuristics

The study of heuristics tends to focus on systematic errors in human decision making and these heuristics often help to understand anomalies of inferring expectations from evidence.

Three heuristics that deal with probabilistic thinking have received considerable attention: (a) availability, (b) representativeness, and (c) anchoring and adjustment. The availability heuristic refers to the tendency to assess the probability of an event based on the ease with which instances of that event come to mind. This heuristic has been investigated in a variety of domains and relates probability estimates to memory access. Generally people overestimate the probability of an event if concrete instances of that event are easily accessible in memory. Generally, ease of recall and frequency of occurrence are correlated. A number of factors that affect memory are, however, unrelated to probability. For example, vivid images are easier to recall than pallid ones. Thus, having been involved in a serious car accident is likely to be better remembered than annual statistics about the frequency of (types of) traffic accidents. The former is likely to have more impact on probability estimates than the latter. Dawes (1994) argued that the salience of negative and relatively extreme exemplars of drug addicts can bias policy-makers’ perceptions of the entire group and result in negative attitudes toward programs such as the provision of clean needles to prevent a further spread of the AIDS virus.

The representativeness heuristic refers to the tendency to assess the probability that a stimulus belongs to a particular class by judging the degree to which that event corresponds to an appropriate mental model. Kahneman and Tversky (1973) reported a well-known example of how ignoring prior probabilities can affect judgment. In their study, respondents were provided with brief personality sketches, supposedly of engineers and lawyers. They were asked to assess the probability that each sketch described a member of one profession or the other. Half the respondents were told the population from which the sketches were drawn consisted of 30 engineers and 70 lawyers, the remaining respondents were told that there were 70 engineers and 30 lawyers. Findings showed that the prior probabilities were essentially ignored, and that respondents estimated the probability of class membership by judging how similar each personality sketch was to their mental model of an engineer or a lawyer.

Anchoring and adjustment refers to a general judgment process in which an initially given or generated response serves as an anchor, and other information is insufficiently used to adjust that response. The anchoring and adjustment heuristic is based on the assumption that people often start their judgmental process by focusing on some initial value that serves as an anchor. The biases related to this heuristic stem from two distinct aspects. First, one could use irrelevant anchors, second one could insufficiently adjust up or down from an original starting value or anchor.

4. Descriptive Approaches To Decision Making

People often tend to solve problems in a way that satisfices (Simon 1955) in terms of avoiding computational complexity and/or in terms of having good reasons for one’s decision. Descriptive theories of human judgment and decision making attempt to explain why people violate rational choice theory. These descriptive frameworks also view decisions as more reasonable and adaptive than previously thought. In this section a number of these frameworks will be briefly discussed.

4.1 Prospect Theory

Kahnemann and Tversky (1979) developed prospect theory to remedy the descriptive failures of SEU theories of decision making. Prospect theory attempts to describe and explain decisions under uncertainty. Like SEU theories prospect theory assumes that the value of an option or alternative is calculated as the summed products over specified outcomes. Each product consists of a utility and a weight attached to the objective probability. Both the value function and the probability weighting function are nonlinear. The two functions are not given in closed mathematical form but have a number of important features. The most important feature of the probability weighting function is that small probabilities are over-weighted, and large probabilities are underweighted. The probability weighting function is generally not well behaved near the end-points. Extremely low probability out-comes can be exaggerated or ignored entirely. Similarly, small differences between high probability and certainty are sometimes neglected, sometimes accentuated. According to Kahneman and Tversky this is so because people find it difficult to comprehend and evaluate extreme probabilities.

The value function is defined in terms of gains and losses relative to a psychologically neutral reference point. The value function is S-shaped; concave in the region of gains above the reference point, convex in the region of losses (see Fig. 1). Thus, each unit increase in gain (loss) has decreasing value as gain (loss) increases. In other words, the subjective difference between gaining nothing and gaining $100 is greater than the difference between gaining $100 and gaining $200. Finally, the value function is steeper for losses than for gains. This implies that losing $100 is more unpleasant than gaining $100 is pleasant.

Prospect theory can help to explain both the violation of the sure thing principle and some of the framing effects discussed above. Thus, for respondents who won the first gamble in the Tversky and Shafir (1992) study, the average of the value of $100 and $400 may well be greater than the value of the $200 they already won. After losing, the negative value for -$100 is less than the average of -$200 and +$100. When people do not know whether they won or lost they will compare the possible outcomes with the zero-point, and in that case the gamble is not very attractive. Prospect theory can also help to explain the framing effects discussed above. The probability function and the value function will confirm the higher attractiveness of the risky option (in case of losses) and the risk-avoiding option (in case of gains).

Most other descriptive approaches assume that people rely on a variety of strategies or heuristics for solving decision problems. Experience will affect the availability of these strategies, and strategy choice will also be affected by the expected advantages (benefits) and disadvantages (costs) of the chosen strategy. For many decisions an exhaustive analysis such as pre-scribed by SEU-theory simply is not worth the trouble. Thus, for many problems people aim for an acceptable solution and not necessarily the optimal solution due to the costs (time, effort) of ‘calculating’ the best possible option.

4.2 Other Descriptive Approaches

Beach (1990) proposed ‘image theory’ which stresses the intuitive and automatic aspects of decision making. In this theory the decision-maker’s goals or values (images) play a crucial role and much attention is paid to simplified strategies. The theory emphasizes that individuals make judgments about the compatibility of an alternative with one’s image. This assessment is assumed to be rapid and intuitive. More analytical and elaborate decision-making processes such as these assumed by SEU theory are expected to be evoked only in specific circumstances (e.g., decisions with possible severe consequences). Beach refers to so-called noncompensatory strategies that are often used in real-life decision making. Since cognitive overload provokes a need for simplification, especially when one has to combine substantial amounts of relevant information about probabilities and values, it can be expected that people use decision rules that require less cognitive effort than normative theories such as SEU-theory.

Examples of simplifying decision-rules mentioned in the literature are: The dominance rule (alternative A should be chosen over B if A is better on at least one attribute and not worse than B on all remaining attributes), the conjunctive decision rule (any alternative that does not meet a minimally required value on one or more attributes is dropped from the list of remaining possible alternatives), and the lexicographic decision rule (choose alternative which is most at-tractive on the most important attribute. If two alternatives are equally attractive in terms of the most important attribute move to the next attribute). Finally, the elimination by aspects rule is often interpreted as a combination of the lexicographic rule and the conjunctive rule. First, the most important at-tribute is selected. All alternatives that fail to meet the criterion on this attribute are eliminated. This procedure is repeated for each of the remaining attributes.

These decision rules require considerably less cognitive effort than the decision rule required by SEU models (a compensatory decision rule which allows negative scores on one attribute to be compensated by positive scores on another attribute). These simplifying rules can provide adequate short cuts in complex decision environments. Payne et al. (1992) use the term contingent decision making in which the cognitive costs and benefits of the various strategies people might use determine their choice of strategy. Basic questions in this choice concern the balance between cognitive effort and accuracy, but also factors such as decisional avoidance and accountability influence strategy selection.

4.2.1 Rule Following. Many decisions are based on rules that convey information about who we are and how we interact with others. They may convey a personal or moral identity, but also a social or group identity. They may also express habits such as routine purchases at the grocery. Generally, rules minimize effort and saves time. For instance, Prelec and Herrnstein (1991) discuss cases in which people avoid cost-benefit analyses and rely on prudential rules such as moral considerations and self-control (see also Mellers et al. 1998). Adopting a rule is especially useful for controlling behavioral practices with ad-verse consequences that are felt only with repetition or with a delay between benefits and costs. Examples are dietery behavior and alcohol consumption.

4.2.2 Reason-Based Choice. Sometimes rules do not apply and fail to single out the best action. In such cases decision makers may search for reasons to guide their choices. Reasons may be lists of pros and cons, but they can also take the form of stories. Pennington and Hastie (1992) investigated this in the context of jury decision making and presented evidence to respondents either as stories or issues and found that story organization resulted in stronger and more confident decisions. People often wish to have ‘good reasons’ for what they do. Reasons can also help to explain the violation of the sure thing principle discussed above. Tversky and Shafir (1992) argued that in the case of accepting the gamble for a second play the simple fact that having won the first time and hence having ‘nothing to lose’ would be a good reason to play a second time.

Other descriptive approaches introduced added elements that play a role in judgment and decision making. One of these approaches is Regret Theory (Bell 1982, Looms and Sugden 1987). The basic idea underlying this approach is that the value placed on the outcome of a specific choice or gamble also depends on the outcome that would have been received if one had chosen an alternative choice or gamble. Thus winning $100 is likely to be affected by the knowledge that one could have won $500 (vs., e.g., $10) if one had selected another alternative. Basically regret theory posits that people anticipate the possibility of regret and that this anticipation affects their decision making. People not only want to maximize utility, they also want to minimize the possibility of regret.

5. Formulas vs. Intuitive Decision Making

One of the aims of decision-making research is to improve our understanding of how decision makers search for information and of how this information is combined or processed. These processes of information integration are often called clinical judgment, and compared with the use of a formula or model. The latter can be based on experts’ decision rules, or empirically assessed relationships between predictors (e.g., the presence of symptoms, scores on tests) and outcomes (e.g., having a specific disease or the ability of a job candidate). A substantial amount of research has shown that judgments are generally better if they are made using a formula (Dawes 1988). This applies especially to diagnostic judgments in which a limited number of indicators can lead to adequate prediction. Payne et al. (1992) note that two issues are of importance in this context. First, what factors influence the use of a statistical, automated, decision procedure? Second, how can we reach a situation in which clinical and automated decision making complement one another, rather than compete? The still modest use of automated decision procedures could be related to limited knowledge about their benefits, experienced difficulties in applying the rules to individual decisions, and overly optimistic beliefs in the accuracy of clinical judgment.

6. Conclusions

Nearly five decades after the seminal work of Edwards (1954), decision making research is becoming more prominent in psychology textbooks and a clear and separate research area has emerged, generally referred to as Behavioral Decision Research. An important characteristic of this field of inquiry is that it relies on concepts, models and methods from a variety of disciplines including economics, statistics, and social and cognitive psychology. Research on decision making pays considerable attention to the discrepancies between normative models and actual behavior, al-though this is an issue that requires more conceptual and empirical work. Most of the research attempting to account for these discrepancies has focused on the information processing strategies, or heuristics, that people use when making judgments or decisions. A variety of descriptive approaches to human decision making has improved our insight into the cognitive processes underlying decisions, and should also help to improve the quality of decision making. Tasks for the future include determining when various heuristics are most likely to be used and the role of emotion in the decision-making process.

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