Computational modeling for reasoning about the social behavior of humans

Article

Abstract

The number of computationally-based models of human social behavior is growing rapidly. In fact, the current ease of programming is resulting in a plethora of tools with impressive interfaces but little theoretical power under the hood. Further, the overabundance of new toolkits for building models is facilitating the excessively rapid growth of simple proof-of-concept, or intellective, models. The current state of models range from the simplistic to the elaborate, from the conceptual to the empirical, and from the purely notional to the validatable. This review briefly describes the state of human social behavioral modeling. Key issues surrounding analysis and validation are discussed.

Keywords

Dynamic network analysis Social networks Agent based models Multi-agent simulation Network science 

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References

  1. Anderson JR (1993) Rules of the mind. Erlbaum, Hillsdale Google Scholar
  2. Anderson JR (1996) ACT: a simple theory of complex cognition. Am Psychol 51:355–365 CrossRefGoogle Scholar
  3. Axtell R, Axelrod R, Epstein JM, Cohen MD (1996) Aligning simulation models: a case study and results. Comput Math Organ Theory 1(2):123–141 CrossRefGoogle Scholar
  4. Burton RM, Obel B (1995) The validity of computational models in organization science: from model realism to purpose of the model. Comput Math Organ Theory 1(1):57–71 CrossRefGoogle Scholar
  5. Burton RM (1995) Validation and docking: an overview, summary and challenge. In: Simulating organizations: computational models of institutions and groups. MIT Press, Cambridge, pp 215–228 Google Scholar
  6. Carley KM (1996) Validating computational models. Working Paper Google Scholar
  7. Carley KM, Columbus D, DeReno M, Reminga J, Moon I (2008) ORA user’s guide 2008. Carnegie Mellon University, School of Computer Science, Institute for Software Research, Technical Report, CMU-ISR-08-125 Google Scholar
  8. Carley KM, Fridsma DB, Casman E et al. (2006) BioWar: scalable agent-based model of bioattacks. IEEE Trans Syst Man Cybern A 36:252–65 CrossRefGoogle Scholar
  9. Carley KM, Altman N, Kaminsky B, Nave D, Yahja A (2004) BioWar: a city-scale multi-agent network model of weaponized biological attacks. CASOS Technical Report: CMU-ISRI-04-101. Carnegie Mellon University, Pittsburgh, PA Google Scholar
  10. Carley KM, Newell A (1994) The nature of the social agent. J Math Sociol 19(4):221–262 Google Scholar
  11. Carley KM (1991) A theory of group stability. Am Sociol Rev 56(3):331–354 CrossRefGoogle Scholar
  12. Cohen MD, March JG, Olsen JP (1972) A garbage can model of organizational choice. Adm Sci Q 17(1):1–25 CrossRefGoogle Scholar
  13. Cyert RM, March JG (1963) A behavioral theory of the firm. Prentice-Hall, Englewood Cliffs Google Scholar
  14. Epstein J, Axtell R (1997) Growing artificial societies. MIT, Boston Google Scholar
  15. Harrison JR, Lin Z, Carroll GR, Carley KM (2007) Simulation modeling in organizational and management research. Acad Manag Rev 32:1229–1245 CrossRefGoogle Scholar
  16. Kauffman SA, Weinberger ED (1989) The N-K model of rugged fitness landscapes and its application to maturation of the immune response. J Theor Biol 141:211–245 CrossRefGoogle Scholar
  17. Laird JE, Newell A, Rosenbloom PS (1987) Soar: an architecture for general intelligence. Artif Intell 33:1–64 CrossRefGoogle Scholar
  18. Law AM (2007) Simulation modeling & analysis, 4th edn. McGraw-Hill, New York Google Scholar
  19. Maxwell D, Carley KM (in press) Principles for effectively representing heterogeneous populations in multi-agent simulations. In: Tolk A (ed) Complex systems in knowledge based environments. Springer, Berlin Google Scholar
  20. McCulloh IA, Carley KM (2008a) Social network change and detection. Institute for Software Research, Technical Report, CMU-ISR-08-116 Google Scholar
  21. McCulloh IA, Carley KM (2008b) Detecting change in human social behavior simulation. Institute for Software Research, Technical Report, CMU-ISR-08-135 Google Scholar
  22. Ross SM (2006) Simulation, 4th edn. Elsevier, New York Google Scholar
  23. Meyers RH, Montgomery DC (2002) Response surface methodology: process and product optimization using designed experiments. Wiley, New York Google Scholar
  24. Newell A (1990) Unified theories of cognition. Harvard University Press, Cambridge Google Scholar
  25. Tindall DB, Malinick TE (eds) (2008) Teaching about social networks. American Sociological Association, New York Google Scholar
  26. Sakoda JM (1971) The checkerboard model of social interaction. J Math Sociol 1:119–132 Google Scholar
  27. Schelling T (1969) Models of segregation. Am Econ Rev 59:488–493 Google Scholar
  28. Schelling T (1971) Dynamic models of segregation. J Math Sociol 1:143–186 Google Scholar
  29. Silverman B, Rees RL, Toth JA, Cornwell J, O’Brien K, Johns M, Caplan M (2005) Athena’s prism—a diplomatic strategy role playing simulation for generating ideas and exploring alternatives. Departmental Papers (ESE) Google Scholar
  30. Schreiber C, Carley KM (2004) Construct—a multi-agent network model for the co-evolution of agents and socio-cultural environments. Carnegie Mellon University, School of Computer Science, Institute for Software Research International, Technical Report CMU-ISRI-04-109 Google Scholar
  31. Yahja A (2006) WIZER: a tool for validation of social simulations. Ph.D. Thesis, School of Computer Science, Carnegie Mellon University Google Scholar
  32. Zacharias GL, MacMillan J, Van Hemel SB (eds) (2008) Behavioral modeling and simulation: from individuals to societies. Committee on organizational modeling: from individuals to societies. Academies Press, Washington Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Institute for Software Research, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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