Theory of Adaptive Decision Making
{'Code': '04040500', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'BCS', 'LongName': 'Division Of Behavioral and Cognitive Sci'}}
{'SignBlockName': 'Jasmine Young', 'PO_EMAI': 'jyoung@nsf.gov', 'PO_PHON': '7032924833'}
The purpose of this research is to investigate how individuals                  learn to improve their decision-making skills on the basis of                   experience resulting from consequences of past decisions.  The                  research was designed to evaluate empirically a mathematical                    learning theory that can be summarized by three principles:  The                first principle describes how each situation is categorized for                 the purpose of identifying a set of appropriate decision-making                 rules; the second principle describes how one particular rule is                selected from a set of competing rules on the basis of average                  past performance; and the third principle describes how the                     performance of each decision rule is slowly improved by a fine                  tuning process guided by feedback from previous consequences.                   The first two experiments will test the hypothesis that a simple                "hill-climbing" mechanism is used to fine tune and improve the                  performance of a given rule.  Experiment 1 will investigate how                 diagnostic criteria are learned in a medical diagnosis task, and                Experiment 2 will investigate how individuals learn to improve                  resource allocations to maximize profits.  The next two                         experiments will test the hypothesis that rules are selected on                 the basis of performance estimates produced by a recency-weighted               averaging mechanism.  Experiment 3 will investigate how                         individuals learn to choose among fictitious medical treatments                 that differ according to efficacy, and Experiment 4 will                        investigate how individuals learn to choose among diagnostic                    rules for categorizing fictitious medical patients.  The last two               experiments will test the hypothesis that decision situations are               categorized prior to rule selection, and rule performance is                    assessed separately for each category.  Experiment 5 will test                  the hypothesis that untrained decision makers rely on concrete                  surface features, whereas trained desision makers rely on                       abstract structural principles for categorizing decision                        situations.  Experiment 6 will investigate how individuals learn                to discriminate rule performance on the basis of envirionmental                 cues.  Overall, this research program will improve our                          understanding about the evolution of decision strategies, the                   development of both optimal and non-optimal strategies, and the                 process that novice decision makers use to become experts through               practice.
{'FirstName': 'Jerome', 'LastName': 'Busemeyer', 'PI_MID_INIT': 'R', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Jerome R Busemeyer', 'EmailAddress': 'jbusemey@indiana.edu', 'NSF_ID': '000276216', 'StartDate': '09/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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{'Code': '118000', 'Text': 'HUMAN COGNITION & PERCEPTION'}
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