Knowledge and Inference in Probabilistic Environments II:   Learning and Experience in Real-World Contexts
{'Code': '04050100', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SES', 'LongName': 'Divn Of Social and Economic Sciences'}}
{'SignBlockName': 'William Bainbridge', 'PO_EMAI': None, 'PO_PHON': None}
How does an experienced neurologist learn to determine the cause of a           patient's chronic headaches, a venture capitalist learn to spot a good          prospect, or a teacher learn to motivate students?  In all these                cases, successful judgment is part talent, part training, and part              luck.  But the development of judgmental abilities over time also               requires learning from experience.  Because we live in an inherently            probabilistic world, we must try to extract information from incomplete         and imperfect feedback.  The research proposed here explores the                processes by which learning takes place in probabilistic environments,          and the strategies by which people try to accomplish that task.  The            goal of this research is to provide information useful for both                 understanding and improving these learning processes.                                                                                                           The results of this work have important potential applications in               practical decision contexts.  A great many major decisions are made on          the basis of intuitive judgment, particularly when the situation is             ill-defined, unstable, or novel, or when time, information, and                 resources are limited.  It would be especially useful to have a better          understanding of the limitations and possibilities of human learning            in such situations.  A better understanding of human learning can also          guide the development and use of statistical methods and other decision         aids.  Such understanding can help clarify the costs and benefits of            reliance on human vs. mechanical judgment, aid in the design and                implementation of interactive man-machine systems, and identify means           by which human learning processes themselves can be improved and                facilitated.
{'FirstName': 'Joshua', 'LastName': 'Klayman', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Joshua Klayman', 'EmailAddress': 'joshk@uchicago.edu', 'NSF_ID': '000172462', 'StartDate': '08/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'National Opinion Research Center', 'CityName': 'CHICAGO', 'ZipCode': '606035713', 'PhoneNumber': '7732566000', 'StreetAddress': '55 E MONROE ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Illinois', 'StateCode': 'IL', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_ORG': 'IL07', 'ORG_UEI_NUM': 'MPYFY5UMSDP4', 'ORG_LGL_BUS_NAME': 'NATIONAL OPINION RESEARCH CENTER', 'ORG_PRNT_UEI_NUM': 'MPYFY5UMSDP4'}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '1321', 'Text': 'Decision, Risk & Mgmt Sci'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8706101.xml'}