NSF Org:	SES Division of Social and Economic Sciences
Recipient:	UNIVERSITY OF SOUTH CAROLINA
Initial Amendment Date:	March 9, 1993
Latest Amendment Date:	March 9, 1993
Award Number:	9223192
Award Instrument:	Standard Grant
Program Manager:	William Bainbridge SES  Division of Social and Economic Sciences SBE  Directorate for Social, Behavioral and Economic Sciences
Start Date:	March 15, 1993
End Date:	August 31, 1995 (Estimated)
Total Intended Award Amount:	$29,406.00
Total Awarded Amount to Date:	$29,406.00
Funds Obligated to Date:	FY 1993 = $29,406.00
History of Investigator:	John Skvoretz (Principal Investigator) jskvoretz@usf.edu
Recipient Sponsored Research Office:	University of South Carolina at Columbia 1600 HAMPTON ST COLUMBIA SC  US  29208-3403 (803)777-7093
Sponsor Congressional District:	06
Primary Place of Performance:	DATA NOT AVAILABLE
Primary Place of Performance Congressional District:
Unique Entity Identifier (UEI):	J22LNTMEDP73
Parent UEI:	Q93ZDA59ZAR5
NSF Program(s):	Sociology, Methodology, Measuremt & Stats
Primary Program Source:
Program Reference Code(s):
Program Element Code(s):	133100, 133300
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.075

ABSTRACT

This research will explore the applicability of genetic algorithms and classifier systems to the analysis of social interaction, seeking to provide a formally-based understanding. The forms of stable interaction range from highly-scripted, instrumentalized social action to informal group processes sustained by the rationality of individual participants. The techniques will be applied to three domains: social exchange, collective action, and action organization. A key advantage of this research is the power of the computer techniques to represent all forms of interaction in terms of an evolving population of genotypical action plans and thus to provide the basis for an integrated theory of social interaction systems that covers the full range of commonly recognized system types. Sociology has been slow to make use of several new approaches in computer technology, and this research will be the first substantial sociological application of the related techniques of genetic algorithms and classifier systems. Genetic algorithms are a way of arriving at a solution to a problem through successive approximation, with the great advantage that it develops many potential solutions simultaneously, successively combining features of different solutions and selecting the best solutions for further development. In the context of this research, classifer systems read computerized strings of symbols, such as the units produced by genetic algorithms, and interpret them as rules for human action under specified circumstances.

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