Mandeep K. Dhami

The role of representative design in an ecological approach to cognition.

Egon Brunswik argued that psychological processes are adapted to environmental properties. He proposed the method of representative design to capture these processes and advocated that psychology be a science of organism-environment relations. Representative design involves randomly sampling stimuli from the environment or creating stimuli in which environmental properties are preserved. This departs from systematic design. The authors review the development of representative design, examine its use in judgment and decision-making research, and demonstrate the effect of design on research findings. They suggest that some of the practical difficulties associated with representative design may be overcome with modern technologies. The importance of representative design in psychology and the implications of this method for ecological approaches to cognition are discussed.

Psychological Models of Professional Decision Making

People are often expected to make decisions based on all of the relevant information, weighted and combined appropriately. Under many conditions, however, people use heuristic strategies that depart from this ideal. I tested the ability of two models to predict bail decisions made by judges in two courts. In both courts, a simple heuristic proved to be a better predictor of judicial decisions than a more complex model that instantiated the principles of due process. Specifically, judges were “passing the buck” because they relied on decisions made by the police, prosecution, and previous bench. Problematically, these earlier decisions were not significantly related to case characteristics. These findings have implications for the types of models researchers use to capture professional decision-making policies.

Take-the-Best in Expert-Novice Decision Strategies for Residential Burglary

We examined the decision strategies and cue use of experts and novices in a consequential domain: crime. Three participant groups decided which of two residential properties was more likely to be burgled, on the basis of eight cues such as location of the property. The two expert groups were experienced burglars and police officers, and the novice group was composed of graduate students. We found that experts’ choices were best predicted by a lexicographic heuristic strategy called take-the-best that implies noncompensatory information processing, whereas novices’ choices were best predicted by a weighted additive linear strategy that implies compensatory processing. The two expert groups, however, differed in the cues they considered important in making their choices, and the police officers were actually more similar to novices in this regard. These findings extend the literature on judgment, decision making, and expertise, and have implications for criminal justice policy.

Restorative justice in prisons

Restorative justice (RJ) has found significant utility outside the prison setting. For many reasons, it has not received the same level of consideration inside the institution. While not every case can, or perhaps should be considered for restorative justice processing inside the prison, some could easily fall into the broad purview range of restorative and transformative justice. We provide examples of RJ practices that exist in some prisons focusing on: offending behavior and victim awareness programs, community service work, and victim‐offender mediation, as well as prison systems that exhibit a RJ philosophy. Also considered are the effectiveness of prison RJ practices, and the limitations of such efforts. Although RJ has the potential to have a positive impact on the work of prisons and the experience of imprisonment, it has not found wide acceptance and is currently limited to a relatively small number of prisons and then often only delivered in partial form. We believe that RJ has a realistic future in prison settings and that the contradictions that may be identified are not debilitating.

Interpersonal comparison of subjective probabilities: Toward translating linguistic probabilities

Interpersonal variability in understanding linguistic probabilities can adversely affect decision making. Using the fact that everyone judges canonical probability events similarly in a manner consistent with axiom systems that yield a probability measure, we developed and tested a method for comparing the meanings of probability phrases across individuals. An experiment demonstrated that despite extreme heterogeneity in participants’ linguistic probability lexicons, interpersonal similarity in phrase meaning is well predicted by phrase rank order within the lexicons. Thus, equally ranked phrases have similar meanings, and individual differences in linguistic probabilities may simply be explained by the phrases people use at each rank.

Pictures speak louder than numbers: on communicating medical risks to immigrants with limited non-native language proficiency

Background and objectives  Medical risk communication has been infrequently studied in immigrants with limited non‐native language proficiency, even though they may be at greatest risk of illness. In a study, we examined to what extent Polish immigrants to the UK have difficulties in understanding treatment risk reduction expressed as ratios either in their native language or in a non‐native language (English). We further investigated whether this population can be aided by using visual displays to enhance comprehension.

Simply criminal: predicting burglars' occupancy decisions with a simple heuristic.

Rational choice theories of criminal decision making assume that offenders weight and integrate multiple cues when making decisions (i.e., are compensatory). We tested this assumption by comparing how well a compensatory strategy called Franklin’s Rule captured burglars’ decision policies regarding residence occupancy compared to a non-compensatory strategy (i.e., Matching Heuristic). Forty burglars each decided on the occupancy of 20 randomly selected photographs of residences (for which actual occupancy was known when the photo was taken). Participants also provided open-ended reports on the cues that influenced their decisions in each case, and then rated the importance of eight cues (e.g., deadbolt visible) over all decisions. Burglars predicted occupancy beyond chance levels. The Matching Heuristic was a significantly better predictor of burglars’ decisions than Franklin’s Rule, and cue use in the Matching Heuristic better corresponded to the cue ecological validities in the environment than cue use in Franklin’s Rule. The most important cue in burglars’ models was also the most ecologically valid or predictive of actual occupancy (i.e., vehicle present). The majority of burglars correctly identified the most important cue in their models, and the open-ended technique showed greater correspondence between self-reported and captured cue use than the rating over decision technique. Our findings support a limited rationality perspective to understanding criminal decision making, and have implications for crime prevention.

Judgment and decision making as a skill : learning, development and evolution

Part I. Evolutionary and Neural Bases of JDM: 1. The evolved foundations of decision making Andreas Wilke and Peter M. Todd 2. Neural bases of judgment and decision making Oshin Vartanian and David R. Mandel Part II. Developmental Approaches to JDM: 3. Judgment and decision making in young children Anne Schlottmann and Friedrich Wikening 4. Judgment and decision making in adolescents Wandi Brunie de Bruin 5. Aging and decision skills Ellen Peters and Wandi Brunie de Bruin Part III. Learning JDM: 6. Learning of judgment and decision-making strategies Rui Mata and Jorg Rieskamp 7. Casual models in judgment and decision making York Hagmayer and David A. Lagnado 8. Learning judgment and decision making from feedback Nigel Harvey Part IV. Improving and Aiding JDM: 9. Improving judgment and decision making through communication and representation Peter Sedlmeier and Denis J. Hilton 10. Aiding judgment and decision making J. Frank Yates and Andrea M. Angott Conclusion: 11. Perspectives on judgment and decision making as a skill Mandeep K. Dhami, Anne Schlottmann, Michael R. Waldman, James Shanteau, Thomas S. Wallsten, Baruch Fischhoff, Irwin P. Levin (with Joshua A. Weller and Elaine A. Bossard), Valerie F. Reyna, Jonathan Baron, Robin M. Hogarth, Joshua Klayman and Michael H. Birnbaum.

On Measuring Quantitative Interpretations of Reasonable Doubt

Beyond reasonable doubt represents a probability value that acts as the criterion for conviction in criminal trials. I introduce the membership function (MF) method as a new tool for measuring quantitative interpretations of reasonable doubt. Experiment 1 demonstrated that three different methods (i.e., direct rating, decision theory based, and MF) provided significantly different and uncorrelated interpretations of reasonable doubt, although all methods predicted verdicts equally well, and showed inter-individual variability in interpretations. In Experiment 2 only the direct rating method demonstrated a significant effect of judicial instructions on reasonable doubt. In both experiments, the MF method showed intra-individual variability in interpretations of reasonable doubt. The methods may be capturing different aspects of the concept of reasonable doubt. These findings have implications for the validity of past research findings on reasonable doubt and for the utility of triangulation of methods in future research.

Improving Intelligence Analysis With Decision Science

Intelligence analysis plays a vital role in policy decision making. Key functions of intelligence analysis include accurately forecasting significant events, appropriately characterizing the uncertainties inherent in such forecasts, and effectively communicating those probabilistic forecasts to stakeholders. We review decision research on probabilistic forecasting and uncertainty communication, drawing attention to findings that could be used to reform intelligence processes and contribute to more effective intelligence oversight. We recommend that the intelligence community (IC) regularly and quantitatively monitor its forecasting accuracy to better understand how well it is achieving its functions. We also recommend that the IC use decision science to improve these functions (namely, forecasting and communication of intelligence estimates made under conditions of uncertainty). In the case of forecasting, decision research offers suggestions for improvement that involve interventions on data (e.g., transforming forecasts to debias them) and behavior (e.g., via selection, training, and effective team structuring). In the case of uncertainty communication, the literature suggests that current intelligence procedures, which emphasize the use of verbal probabilities, are ineffective. The IC should, therefore, leverage research that points to ways in which verbal probability use may be improved as well as exploring the use of numerical probabilities wherever feasible.