Lola L. Lopes

Between Hope and Fear: The Psychology of Risk

Publisher Summary This chapter discusses the psychology of risk: what risk is (if it is anything at all), how people think about it, what they feel about it, and what they do about it. The chapter describes the way psychologists think about risk: how they study it, what tasks they use, what factors they vary, and what models they build (or borrow) to describe risk-taking behavior. Technically, the word risk refers to situations in which a decision is made whose consequences depend on the outcomes of future events having known probabilities. Psychological studies of risky choice (it is the term used conventionally to refer to all but the most extreme instances of ignorance or ambiguity) fall into two groups. At one extreme are the studies run by mathematically inclined experimental psychologists in which subjects make decisions about gambles described in terms of amounts and probabilities. At the other extreme are studies run by personality psychologists, who are mostly interested in individual differences in risk taking. A theory of risky choice is presented in the chapter that attempts to meld the strengths of both approaches. Empirically and methodologically it is tied to the experimental approach to risky choice. But theoretically it is more strongly tied to motivational approaches.

Test of an ordering hypothesis in risky decision making

Abstract A primed response time task was used to test the hypothesis that judgments in risky decision making involve an anchoring and adjustment procedure in which the amount to be won in a gamble serves as the anchor and is reduced in accord with the probability of winning. As predicted, the data revealed that priming with the amount to be won allowed faster choices between gambles and sure things than priming with the probability of winning. The experiment is discussed in terms of serial fractionation , which is a form of anchoring and adjustment that is equivalent to analog multiplication.

Averaging rules and adjustment processes in Bayesian inference

Two empirically well-supported research findings in the judgment literature are (1) that human judgments often appear to follow an averaging rule, and (2) that judgments in Bayesian inference tasks are usually conservative relative to optimal judgments. This paper argues that both averaging and conservatism in the Bayesian task occur because subjects produce their judgments by using an adjustment strategy that is qualitatively equivalent to averaging. Two experiments are presented that show qualitative errors in the direction of revisions in the Bayesian task that are well accounted for by the simple adjustment strategy. Also noted is the tendency for subjects in one experiment to evaluate sample evidence according to representativeness rather than according to relative likelihood. The final discussion describes task variables that predispose subjects toward averaging processes.

Risk Perception and the Perceived Public

The popular press has a fondness for stories about the risks of life. Death and destruction, pollution and pestilence, murder and mayhem: the more the merrier. So it has always been. Recently, however, reporters have uncovered a new kind of threat. According to numerous psychological experiments, it now seems likely that lay people do not understand statistics well enough to make intelligent use of all this information about death and destruction, pollution and pestilence, murder and mayhem. As a writer for the Saturday Evening Post recently summed matters up, “when it comes to risk, we are idiots” (Bryson, 1988, p. 31).

Individual strategies in goal-setting

Abstract Information integration theory was applied in a study of long-term behavior in a goal-setting task. Goal-setting was conceptualized as a process of serial integration in which the goal on any one trial is a weighted average of previous successes and failures. Six subjects performed a computer controlled maze-running task, received score feedback, and predicted their score for the next trial. The data of all six subjects supported the serial integration model, but with individual differences in the number of proceding trials to which the subject attended. In addition, the data of two subjects suggested that they also attended to immediate rate of progress on the task.

Risk preference and feedback

Subjects’ preferences for a set of multi-outcome lotteries were assessed, and then subjects were exposed to a feedback period in which they played the lotteries and received information concerning outcomes. Afterward, preferences were reassessed. Most subjects’ preferences were risk averse in the prefeedback assessment, regardless of whether they were making single decisions (short-run condition) or choices for repeated plays (long-run condition), but long-run subjects were less risk averse than short-run subjects. In the postfeedback assessment, about half of the subjects in each condition became risk seeking and the other half maintained their risk-averse preferences. There were, however, differences between short-run and long-run subjects in the postfeedback pattern of risk seeking.

What Naive Decision Makers can Tell us about Risk

During the last 100 years, there have been many changes of fashion concerning the proper relation between the psychologist, the subject, and the subject matter. In the early days, subjects were trained in the techniques of introspection in the hope that they would be able to look beyond the products of higher mental processes and report back on sensation, itself. For reasons that now seem obvious, this program failed and psychological fashion swung to behaviorism, in which the scientific goal was to map directly from observable stimuli onto observable responses. The subject, therefore, came to be treated as a “black box” whose contents were theoretically inconsequential. Since World War II, however, behaviorism has been steadily losing ground to a newer approach, variously called “human information processing psychology” or “cognitive psychology. ” This approach uses the subject as a “window” on the flow of information through consciousness. Thus, verbal reports are becoming part of the database on which theory is built and for which explanation is required.

Judging similarity among strings described by hierarchical trees.

Abstract The paper compares the tree-theoretical model of similarity judgement (in which the similarity between two objects is a function of the distance between them in a conceptual tree) with an averaging model of similarity judgement that is drawn jointly from information integration theory and from current research indicating the prevalence of anchoring and adjustment mechanisms in judgement. Results of an experiment are presented that suggest that even when subjects organize conceptual material as a hierarchical tree, judgments of similarity among the objects are better accounted for by an averaging mechanism than by distances in the tree. These data are discussed in terms of the differences between the representation in which knowledge is encoded and the processes that operate on the represented information.