Robin M. Hogarth

The Effects of Financial Incentives in Experiments: A Review and Capital-Labor-Production Framework

We review 74 experiments with no, low, or high performance-based financial incentives. The modal result has no effect on mean performance (though variance is usually reduced by higher payment). Higher incentive does improve performance often, typically judgment tasks that are responsive to better effort. Incentives also reduce “presentation” effects (e.g., generosity and risk-seeking). Incentive effects are comparable to effects of other variables, particularly “cognitive capital” and task “production” demands, and interact with those variables, so a narrow-minded focus on incentives alone is misguided. We also note that no replicated study has made rationality violations disappear purely by raising incentives.

Order effects in belief updating: The belief-adjustment model

Abstract Much literature attests to the existence of order effects in the updating of beliefs. However, under what conditions do primacy, recency, or no order effects occur? This paper presents a theory of belief updating that explicitly accounts for order-effect phenomena as arising from the interaction of information-processing strategies and task characteristics. Key task variables identified are complexity of the stimuli, length of the series of evidence items, and response mode (Step-by-Step or End-of-Sequence). A general anchoring-and-adjustment model of belief updating is proposed. This has two forms depending on whether information is processed in a Step-by-Step or End-of-Sequence manner. In addition, the model specifies that evidence can be encoded in two ways, either as a deviation relative to the size of the preceding anchor or as positive or negative vis-a-vis the hypothesis under consideration. Whereas the former (labeled estimation mode) results in data consistent with averaging models of judgment, the latter (labeled evaluation mode) implies adding models. Conditions are specified under which (a) evidence is encoded in estimation or evaluation modes and (b) use is made of the Step-by-Step or End-of-Sequence processing strategies. The theory is shown both to account for much existing data and to make novel predictions for combinations of task characteristics where current data are sparse. Some of these predictions are examined and validated in a series of five experiments. Finally, both the theory and the experimental results are discussed with respect to the structure of models of updating processes, limitations and extensions of the present work, and the importance of developing a procedural theory of judgment.

Decision Making under Ambiguity

Abstract : Ellsbergs paradox demonstrates that ambiguous or vague probabilities derived from choices between gambles are not coherent. A descriptive model of judgement under ambiguity is developed in which an initial estimate serves as a starting point and adjustments are made for abbiguity. The adjustments involve a mental simulation in which higher and lower probabilities are considered and differentially weighted. Implications of this model include ambiguity avoidance and seeking; sub- and superadditivity of complementary probabilities; dynamic ambiguity; and reversals in the meaning of data. Three experiments involving Ellsbergs paradox and the setting of buying and selling prices for insurance and warranties test the model. A choice rule under ambiguity is developed that implies a lack of independence between ambiguous probabilities and the sign of payoff utility. The applicability of the model to the case where probabilities are explicitly stated is considered, including the handling of context effects. Keywords: Ambiguity, Decision making, Insurance.

Unit weighting schemes for decision making

Abstract The general problem of forming composite variables from components is prevalent in many types of research. A major aspect of this problem is the weighting of components. Assuming that composites are a linear function of their components, composites formed by using standard linear regression are compared to those formed by simple unit weighting schemes, i.e., where predictor variables are weighted by 1.0. The degree of similarity between the two composites, expressed as the minimum possible correlation between them, is derived. This minimum correlation is found to be an increasing function of the intercorrelation of the components and a decreasing function of the number of predictors. Moreover, the minimum is fairly high for most applied situations. The predictive ability of the two methods is compared. For predictive purposes, unit weighting is a viable alternative to standard regression methods because unit weights: (1) are not estimated from the data and therefore do not “consume” degrees of freedom; (2) are “estimated” without error (i.e., they have no standard errors); (3) cannot reverse the “true” relative weights of the variables. Predictive ability of the two methods is examined as a function of sample size and number of predictors. It is shown that unit weighting will be superior to regression in certain situations and not greatly inferior in others. Various implications for using unit weighting are discussed and applications to several decision making situations are illustrated.

Cognitive Processes and the Assessment of Subjective Probability Distributions

Abstract This article considers the implications of recent research on judgmental processes for the assessment of subjective probability distributions. It is argued that since man is a selective, sequential information processing system with limited capacity, he is ill-suited for assessing probability distributions. Various studies attesting to mans difficulties in acting as an “intuitive statistician” are summarized in support of this contention. The importance of task characteristics on judgmental performance is also emphasized. A critical survey of the probability assessment literature is provided and organized around five topics: (1) the “meaningfulness” of probability assessments; (2) methods of eliciting distributions; (3) feedback and evaluation of assessors; (4) differential ability of groups of assessors and (5) the problems of eliciting a single distribution from a group of assessors. Conclusions from the analysis with respect to future work include the need to capitalize on cognitive simplific...

Risk, Ambiguity, and Insurance

In a series of experiments, economically sophisticated subjects, including professional actuaries, priced insurance both as consumers and as firms under conditions of ambiguity. Findings support implications of the Einhorn-Hogarth ambiguity model: (1) For low probability-of-loss events, prices of both consumers and firms indicated aversion to ambiguity; (2) As probabilities of losses increased, aversion to ambiguity decreased, with consumers exhibiting ambiguity preference for high probability-of-loss events; and (3) Firms showed greater aversion to ambiguity than consumers. The results are shown to be incompatible with traditional economic analysis of insurance markets and are discussed with respect to the effects of ambiguity on the supply and demand for insurance.

Heuristic and Linear Models of Judgment: Matching Rules and Environments.

Much research has highlighted incoherent implications of judgmental heuristics, yet other findings have demonstrated high correspondence between predictions and outcomes. At the same time, judgment has been well modeled in the form of as if linear models. Accepting the probabilistic nature of the environment, the authors use statistical tools to model how the performance of heuristic rules varies as a function of environmental characteristics. They further characterize the human use of linear models by exploring effects of different levels of cognitive ability. They illustrate with both theoretical analyses and simulations. Results are linked to the empirical literature by a meta-analysis of lens model studies. Using the same tasks, the authors estimate the performance of both heuristics and humans where the latter are assumed to use linear models. Their results emphasize that judgmental accuracy depends on matching characteristics of rules and environments and highlight the trade-off between using linear models and heuristics. Whereas the former can be cognitively demanding, the latter are simple to implement. However, heuristics require knowledge to indicate when they should be used.

Learning from feedback: exactingness and incentives

In a series of five experiments, exactingness, or the extent to which deviations from optimal decisions are punished, is studied within the context of learning a repetitive decision-making task together with the effects of incentives. Results include the findings that (a) performance is an inverted-U shaped function of exactingness, (b) performance is better under incentives when environments are lenient but not when they are exacting, (c) the interaction between exactingness and incentives does not obtain when an incentives function fails to discriminate sharply between good and bad performance, and (d) when the negative effects of exactingness on performance are eliminated, performance increases with exactingness.

Insurer Ambiguity and Market Failure

A series of studies investigate the decision processes of actuaries, underwriters, and reinsurers in setting premiums for ambiguous and uncertain risks. Survey data on prices reveal that all three types of these insurance decision makers are risk averse and ambiguity averse. In addition, groups appear to be influenced in their premium-setting decisions by specific reference points such as expected loss and the concern with insolvency. This behavior is consistent with a growing analytical and empirical literature in economics and decision processes that investigates the role that uncertainty plays on managerial choices. Improved risk-assessment procedures and government involvement in providing protection against catastrophic losses may induce insurers to reduce premiums and broaden available coverage.

Intuition: A Challenge for Psychological Research on Decision Making

Intuition represents an enormous challenge for research on decision making. What is intuition? How does it modify our appreciation of cognitive abilities? When should people trust intuition? These questions set the agenda for this article, which (a) defines intuition, (b) comments on how intuition has been viewed across time in the decision making literature, (c) stresses the need to specify different types of intuition, (d) discusses when intuition is likely to lead to good decisions, and (e) presents four challenges. These are, first, elucidating the evolution of preferences; second, illuminating culturally acquired values such as morals; third, the need to educate intuitive responses; and fourth, problems in using intuition for decision making in a changing world. However, the major challenge facing intuition research is the need for conceptual work to define the nature and scope of different intuitive phenomena. To be useful, the concept should not become too broad.