Measuring Strength of Evidence
{'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'}
In science, in the law, in business, and in everyday social                     interaction, people attempt to base their beliefs on the                        available evidence.  The standard (Bayesian) theory asserts that                people's beliefs consist of probabilities for various                           propositions, for example, the probability that a particular                    scientific hypothesis is correct or the probability that an                     accused person is guilty as charged.  But proposed applications                 of the Bayesian theory in science and in law have encountered                   strong and quite justifiable resistance, largely because the                    theory demands that people express detailed beliefs even in the                 absence of any evidence (prior probabilities).                                                                                                                  This research will develop non-Bayesian theories in which                       perceived strength of evidence will be measured on a numerical                  scale quite different from probability.  This measurement system                is well adapted to science and the law since, in the absence of                 evidence, all beliefs can be rated as zero.                                                                                                                     One main research activity will be to determine formulae for                    converting probabilistic and statistical information to the                     strength-of-evidence measurement scale, and thus will provide a                 basis for systematic integration of such information with other                 kinds of evidence.  Another research activity will be to develop                graded standards of statistical and nonstatistical evidence,                    against which arbitrary items of evidence can be compared and                   evaluated, much as a meterstick provides a graded series of                     lengths, against which the lengths of other objects can be                      measured.                                                                                                                                                       The graded standards for evidence will be most useful if they can               be applied analytically, decomposing a complex body of evidence                 into parts, each of which can be evaluated by comparison with the               standards.  One of the principal research activities will be to                 evaluate the bias inherent in such analytic decompositions.  That               research will compare evaluations of partial evidence by two                    groups of people, those who are aware of the full body of                       evidence and those who are not.  If possible, conditions will be                found for which these two groups evaluate evidence identically,                 i.e., there is no appreciable bias due to parts of the evidence                 that are ignored in any step of the analytic process.  The                      research will also test conditions under which strong biases are                expected.
{'FirstName': 'David', 'LastName': 'Krantz', 'PI_MID_INIT': 'H', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'David H Krantz', 'EmailAddress': 'dhk@columbia.edu', 'NSF_ID': '000167110', 'StartDate': '08/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Columbia University', 'CityName': 'NEW YORK', 'ZipCode': '100277922', 'PhoneNumber': '2128546851', 'StreetAddress': '615 W 131ST ST', 'StreetAddress2': 'MC 8741', 'CountryName': 'United States', 'StateName': 'New York', 'StateCode': 'NY', 'CONGRESSDISTRICT': '13', 'CONGRESS_DISTRICT_ORG': 'NY13', 'ORG_UEI_NUM': 'F4N1QNPB95M4', 'ORG_LGL_BUS_NAME': 'THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '118000', 'Text': 'HUMAN COGNITION & PERCEPTION'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8711573.xml'}