Effects of Amounts and Units of Information on the Judgmental Process

2008

Psychological Bulletin

286

156

The mathematical representation of E. Brunswik's (1952) lens model has been used extensively to study human judgment and provides a unique opportunity to conduct a meta-analysis of studies that covers roughly 5 decades. Specifically, the authors analyzed statistics of the "lens model equation" (L. R. Tucker, 1964) associated with 249 different task environments obtained from 86 articles. On average, fairly high levels of judgmental achievement were found, and people were seen to be capable of achieving similar levels of cognitive performance in noisy and predictable environments. Further, the effects of task characteristics that influence judgment (numbers and types of cues, inter-cue redundancy, function forms and cue weights in the ecology, laboratory versus field studies, and experience with the task) were identified and estimated. A detailed analysis of learning studies revealed that the most effective form of feedback was information about the task. The authors also analyzed empirically under what conditions the application of bootstrapping--or replacing judges by their linear models--is advantageous. Finally, the authors note shortcomings of the kinds of studies conducted to date, limitations in the lens model methodology, and possibilities for future research.

Section: What Factors Affect the Accuracy Of Human Judgment?mentioning

confidence: 99%

Determinants of linear judgment: A meta-analysis of lens model studies.

2008

Psychological Bulletin

286

156

Heuristic and linear models of judgment: Matching rules and environments.

2007

Psychological Review

264

130

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.

On Heuristic and Linear Models of Judgment: Mapping the Demand for Knowledge

2006

SSRN Journal

Research on judgment and decision making presents a confusing picture of human abilities. For example, much research has emphasized the dysfunctional aspects of judgmental heuristics, and yet, other findings suggest that these can be highly effective. A further line of research has modeled judgment as resulting from "as if" linear models. This paper illuminates the distinctions in these approaches by providing a common analytical framework based on the central theoretical premise that understanding human performance requires specifying how characteristics of the decision rules people use interact with the demands of the tasks they face. Our work synthesizes the analytical tools of "lens model" research with novel methodology developed to specify the effectiveness of heuristics in different environments and allows direct comparisons between the different approaches. We illustrate with both theoretical analyses and simulations. We further link our results to the empirical literature by a meta-analysis of lens model studies and estimate both human and heuristic performance in the same tasks. Our results highlight the trade-off between linear models and heuristics. Whereas the former are cognitively demanding, the latter are simple to use. However, they require knowledge -and thus "maps" -of when and which heuristic to employ.Keywords: Decision making; heuristics; linear models; lens model; judgmental biases.JEL classification: D81, M10. 3Two classes of models have dominated research on judgment and decision making over the last decades. In one, explicit recognition is given to the costs and limits of information processing and people are assumed to use simplifying heuristics -typically making use of only part of the information available (Kahneman, Slovic, & Tversky, 1982;Gigerenzer, Todd, & the ABC Research Group, 1999). In the other, it is assumed that people can integrate all the information at hand and that this is combined and weighted "as if" using an algebraic -typically linear -model (Anderson, 1981;Brehmer, 1994;Hammond, 1996).Research on these models has been conducted within different traditions with few attempts to unify the two approaches (however, see Hammond, 1990). Whereas such unification is not our goal, we recognize the validity of both approaches and seek to illuminate their complementarities. For example, recent research suggests that people can process information in distinctive ways (cf., Chaiken & Trope, 1999), variously described as "experiential" vs. "rational" (Epstein, 1994), "System 1" vs."System 2" (Stanovich & West, 1998), or "tacit" vs. "deliberate" (Hogarth, 2001).The former denote processes that are intuitive or heuristic whereas the latter are the outcomes of more deliberative processes. We do not propose a one-to-one correspondence between the dual process approach, on the one hand, and heuristic and algebraic models, on the other hand. However, the analogy emphasizes the advantages of seeking complementarities.The topic of heuristics has been central to research on judgm...