.png){kind=icon}
Return
Decision by sampling
.png){kind=icon}
OA
AI
DOI：10.1016/j.cogpsych.2005.10.003.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present a theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales. Instead, we assume that an attribute's subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory and is its rank within the sample. We assume that the sample reflects both the immediate distribution of attribute values from the current decision's context and also the background, real-world distribution of attribute values. DbS accounts for concave utility functions; losses looming larger than gains; hyperbolic temporal discounting; and the overestimation of small probabilities and the underestimation of large probabilities. (c) 2005 Elsevier Inc. All rights reserved.
Keywords:
judgment
decision making
sampling
memory
utility
gains and losses
temporal discounting
subjective probability
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
990
Citations:
7.8K
Organization
No organization information available
Cited Papers
ShareSaveShareSaveDECISION FIELD-THEORY - A DYNAMIC COGNITIVE APPROACH TO DECISION-MAKING IN AN UNCERTAIN ENVIRONMENT.png)
PSYCHOLOGICAL REVIEW
PSYCHOLOGICAL REVIEWIF5.8
PREAI
ShareSaveShareSaveThe early immunological response to acute ischemic stroke: Differential gene expression in subpopulations of mononuclear cellsNeuroscience
NeuroscienceIF0
PREAI
ShareSave
View more