M
Mandeep K. Dhami
Researcher at Middlesex University
Publications - 114
Citations - 2881
Mandeep K. Dhami is an academic researcher from Middlesex University. The author has contributed to research in topics: Poison control & Intelligence analysis. The author has an hindex of 26, co-authored 106 publications receiving 2559 citations. Previous affiliations of Mandeep K. Dhami include University of Cambridge & University of Victoria.
Papers
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The role of representative design in an ecological approach to cognition
TL;DR: The authors review the development of representative design, its use in judgment and decision-making research, and the effect of design on research findings, and suggest that some of the practical difficulties associated with representative design may be overcome with modern technologies.
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Psychological Models of Professional Decision Making
TL;DR: 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.
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Adaptation to imprisonment - Indigenous or imported?
TL;DR: Time spent in prison had a direct effect on prisoners' participation in programs, their thoughts of needing control over their lives, their feelings of hopelessness, and their disciplinary infractions in prison.
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Bailing and jailing the fast and frugal way
Mandeep K. Dhami,Peter Ayton +1 more
TL;DR: In this paper, a judgment analysis of the bail decision making policies of 81 magistrates from 44 courts throughout England and Wales revealed intra-and inter-magistrate inconsistency in bail decisions, discrepancies between stated and elicited cue use, and high levels of post-decisional confidence.
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Take-the-best in expert-novice decision strategies for residential burglary
TL;DR: Examination of the decision strategies and cue use of experts and novices in a consequential domain: crime found that experts’ choices were best predicted by a lexicographic heuristic strategy called take-the-best that implies noncompensatory information processing, whereasNovices’ choice were best predicting by a weighted additive linear strategy that implies compensatory processing.