Bernoulli's framework of expected utility serves as a model for various psychological processes, including motivation, moral sense, attitudes, and decision making. To account for evidence at variance with expected utility, we generalize the framework of fast and frugal heuristics from inferences to preferences. The priority heuristic predicts (i) Allais' paradox, (ii) risk aversion for gains if probabilities are high, (iii) risk seeking for gains if probabilities are low (lottery tickets), (iv) risk aversion for losses if probabilities are low (buying insurance), (v) risk seeking for losses if probabilities are high, (vi) certainty effect, (vii) possibility effect, and (viii) intransitivities. We test how accurately the heuristic predicts people's choices, compared to previously proposed heuristics and three modifications of expected utility theory: security-potential/aspiration theory, transfer-of-attention-exchange model, and cumulative prospect theory.Conventional wisdom tells us that making decisions becomes difficult whenever multiple priorities, appetites, goals, values, or simply the attributes of the alternative options are in conflict. Should one undergo a medical treatment that has some chance of curing a lifethreatening illness but comes with the risk of debilitating side effects? Should one report a crime committed by a friend? Should one buy an expensive, high-quality camera or an inexpensive, low-quality camera? How do people resolve conflicts, ranging from the prosaic to the profound?The common denominator of many theories of human behavior is the premise that conflicts are mastered by making trade-offs. Since the Enlightenment, it has been believed that weighting and summing are the processes by which such trade-offs can be made in a rational way. Numerous theories of human behavior-including expected value theory, expected utility theory, prospect theory, Benjamin Franklin's moral algebra, theories of moral sense such as utilitarianism and consequentionalism (Gigerenzer, 2004), theories of risk taking (e.g., Wigfield & Eccles, 1992), motivational theories of achievement (Atkinson, 1957) and work behavior (e.g., Vroom, 1964), theories of social learning (Rotter, 1954), theories of attitude formation (e.g., Fishbein & Ajzen, 1975), and theories of health behavior (e.g., Becker, 1974; for a review see Heckhausen, 1991)-rest on these two processes. Take how expected utility theory would account for the choice between two investment plans as an example. The reasons for choosing are often negatively correlated with one another. High returns go with low probabilities, and low returns go with high probabilities. According to a common argument, negative correlations between reasons cause people to experience conflict, leading them to make trade-offs (Shanteau & Thomas, 2000). Europe PMC Funders Author ManuscriptsEurope PMC Funders Author Manuscripts utility, the trade-off between investment plans is performed by weighting the utility of the respective monetary outcomes by their probabilities and...
This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or non-linear functions thereof) and the separate evaluation of risky options (expectation models). Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models). We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter et al., 2006), and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up) and direction of search (i.e., gamble-wise vs. reason-wise). In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly); acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988) called “similarity.” In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies.
What motives do people prioritize in their social lives? Historically, social psychologists, especially those adopting an evolutionary perspective, have devoted a great deal of research attention to sexual attraction and romantic-partner choice (mate seeking). Research on long-term familial bonds (mate retention and kin care) has been less thoroughly connected to relevant comparative and evolutionary work on other species, and in the case of kin care, these bonds have been less well researched. Examining varied sources of data from 27 societies around the world, we found that people generally view familial motives as primary in importance and mate-seeking motives as relatively low in importance. Compared with other groups, college students, single people, and men place relatively higher emphasis on mate seeking, but even those samples rated kin-care motives as more important. Furthermore, motives linked to long-term familial bonds are positively associated with psychological well-being, but mate-seeking motives are associated with anxiety and depression. We address theoretical and empirical reasons why there has been extensive research on mate seeking and why people prioritize goals related to long-term familial bonds over mating goals. Reallocating relatively greater research effort toward long-term familial relationships would likely yield many interesting new findings relevant to everyday people’s highest social priorities.
Research has shown a tendency of decision makers to overweight small probabilities and to underweight moderate and large probabilities. In standard treatments this is graphically modeled by an inverse S-shaped probability weighting function. We suggest that emotions play a significant role in the shaping of the probability weighting function. In particular, the weighting function is proposed to be some function of objective probability, expected elation, and expected disappointment. The overweighting of small probabilities results from the anticipated elation after having won, given that winning was very unlikely. The underweighting of large probabilities results from anticipated disappointment after having failed to win, given that winning was very likely. Hence, probability is assumed to influence utility. Three experiments investigate these hypotheses. Experiments 1 and 2 show that a convex function relates probability to surprise. Experiment 3 elicits choice data and further supports the proposed hypotheses. The model adds to the understanding of the cognitive and emotional processes underlying the shape of the probability weighting function. Copyright # 2002 John Wiley & Sons, Ltd. key words probability weighting; emotion; disappointment; elation To describe people's choices under risk, decision theory holds that choices can be understood on the basis of their expected consequences. Traditional modeling has focused on experimental gambles and has used outcomes, probabilities, and a function that combines outcomes and probabilities. Since it has been found that choices and preferences cannot be described by raw outcomes and by raw probabilities, recent modeling has used transformed values for outcomes and probabilities. Expected utility (EU) theory has only transformed the outcomes by a utility function, but other theories, like prospect theory (Kahneman and Tversky, 1979) have additionally transformed the probabilities by a probability weighting function. In cumulative prospect
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