Method, Theory, and Multi-Agent Artificial Intelligence: Creating computer models of complex social interaction
Issue: Vol 1 No. 2 (2013) The Experimental Research of Religion
Journal: Journal for the Cognitive Science of Religion
Subject Areas: Religious Studies Cognitive Studies Linguistics
Abstract:
The construction of computer models is becoming an increasingly useful and popular way of testing theories in the cognitive sciences. This paper will present a brief overview of the methods available for constructing and testing computer models of social phenomena such as religious beliefs and behaviors. It will focus on the importance of theoretical continuity and data replication in computer modelling while negotiating the relationship between specificity and ecological validity when models are extended into novel contexts. This paper will argue that computer modeling is an important supplement to the methodological toolbox of cognitive scientists interested in human social phenomena. However, this is only the case if developers pay close attention to research methods and theories and if the method of a model’s development is appropriate for the target phenomenon (Sun, 2006). It concludes that multi-agent AI models are the most appropriate computational tool for the study of complex social phenomena.
Author: Justin E. Lane
References :
Alpert, J. & Hajaj, N. (2008). “Official Blog: We knew the web was big…” [website] Google.com. Posted by J. Alpert & N. Hajaj. Accessed online via http://googleblog.blogspot.co.uk/2008/07/we-knew-web-was-big.html
Ariely, D. (2010). Predictably Irrational: The Hidden Forces That Shape Our Decisions. New York: Harper Perennial.
Atkinson, Q. D. & Whitehouse, H. (2011). “The cultural morphospace of ritual form: Examining modes of religiosity cross-culturally.” Evolution and human behavior, 32: 50-62.
Axelrod, R. (1984). The Evolution of Cooperation. New York: Basic Books.
Axtell, R.L., Epstein, J.M., Dean, J.S., Gumerman, G.J., Swedlund, A.C., Harburger, J., Chakravarty, S., Hammond, R., Parker, J., & Parker, M. (2006). “Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley.” In Generative Social Science: Studies in Agent-Based Computational Modeling. Edited by J.M Eptstein. Princeton: Princeton University Press.
Bainbridge, W. (2006). God from the Machine: Artificial Intelligence Models of Religious Cognition. Lanham, MD: AltaMira Press.
Baronchelli, A., Ferrer-i-Chancho, R., Pastor-Satorras, R., Chater, N., & Christiansen, M.H. (2013). “Networks in cognitive science.” Trends in Cognitive Science, 17(7): 348-360.
Barrett, J.L. (2002). “Smart gods, dumb gods, and the role of social cognition in structuring ritual intuitions.” Journal of Cognition and Culture, 2(3): 183-193.
Barrett, J.L. & E.T. Lawson. (2001). “Ritual intuitions: Cognitive contributions to judgments of ritual efficacy.” Journal of Cognition and Culture, 1(2): 183- 201.
Berryman, A. A. (1992). “The origins and evolution of predatory-prey theory.” Ecology, 73(5): 1530-1535.
Boyer, P. (2001). Religion Explained: The Evolutionary Origins of Religious Thought. New York: Basic Books
Braxton, D. (2008). “Modeling the McCauley-Lawson Theory of Ritual Forms.” [Presentation] Presented to Aarhus University, Aarhus Denmark, in June 2008.
Dean, J.S., Gumerman, G.J., Epstein, J.M., Axtell, R.L., Swedlund, A.C., Parker, M.T., & McCarroll, S. (2006). “Understanding Anasazi culture change through agent-based modeling.” In Generative Social Science: Studies in Agent-Based Computational Modeling. Edited by J.M Eptstein. Princeton: Princeton University Press.
Epstein, J. & Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up. Washington, D.C: Brookings Institution Press.
Facebook. (2013). “Facebook Reports Second Quarter 2013 Results.” Facebook.com - Investor Relations. Accessed online via http://investor.fb.com/releasedetail.cfm?ReleaseID=780093
Fischer, G. & Kronenfeld, D.B. (2011). “Simulation (and modeling).” In A Companion to Cognitive Anthropology. Edited by V.C. de Munck, M.D. Fischer, G. Bennardo, & D.B. Kronenfeld, 210-226. Malden, MA: Wiley-Blackwell.
Gilbert, G. & Troitzsch, K. (2005). Simulation for the Social Scientist. Maidenhead, England and New York, NY: Open University Press.
Gumerman, G.J., Swedlund, A.C., Dean, J.S., & Epstein, J.M. (2006). “The evolution of social behavior in the prehistoric American southwest.” In Generative Social Science: Studies in Agent-Based Computational Modeling. Edited by J.M Eptstein. Princeton: Princeton University Press.
Iannaccone, L. (1992). “Sacrifice and stigma: Reducing free-riding in cults, communes, and other collectives.” Journal of Political Economy, 100(2): 271-291.
Iannaccone, L.R. & Makowsky, M.D. (2007). “Accidental atheists? Agent-based explanations for the persistence of religious regionalism.” Journal for the Scientific Study of Religion, 46(1): 1–16.
Kahneman, D. & A. Tversky (eds.). (2000). Choices, values, and frames. Cambridge: Cambridge University Press.
Lane, J.E. (2013). Semantic Network Mapping of Religious Material. [Conference Presentation] Society for Complex Systems in Cognitive Science. Berlin, Germany, 30 July 2013.
Lawson, E. & McCauley, R. (1990). Rethinking Religion: Connecting Cognition and Culture. Cambridge England New York: Cambridge University Press.
Malley, B. & Barrett, J. (2003). “Can Ritual Form Be Predicted From Religious Belief? A Test of the Lawson-McCauley Hypotheses.” Journal of Ritual Studies, 17(2): 1-14.
McCauley, R. & Lawson, E. (2002). Bringing Ritual to Mind: Psychological Foundations of Cultural Forms. Cambridge, UK New York: Cambridge University Press.
McCorkle, W.W. & Lane, J.E. (2012). “Ancestors in the simulation machine: measuring the transmission and oscillation of religiosity in computer modeling.” Religion Brain & Behavior, 2(3): 1-4.
Meadows, D, Randers, J., & Meadows, D. (2004). Limits to Growth: The 30-Year Update. Chelsea Green Publishing Company: White River Junction, Vermont, USA.
Nielbo, K.L., Braxton, D.M., Upal, A. (2012). “Computing religion: A new tool in the multi-level analysis of religion.” Method and Theory in the Study of Religion, 24: 267-290.
Pyysiäinen, I. (2004). “Corrupt doctrine and doctrinal revival: On the nature and limits of the modes theory.” In Theorizing Religions Past: Archaeology, History, and Cognition. Edited by Harvey Whitehouse and Luther H. Martin. Walnut Creek, CA: AltaMira Press.
Riley, M. A., & Van Orden, G. C. (2005). Tutorials in Contemporary Nonlinear Methods for the Behavioral Sciences. Retrieved 1 September 2012, from http://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.jsp
Schreiber, C. & Carley, K. (2004). “Going beyond the data: Empirical validation leading to grounded theory.” Computational & Mathematical Organization Theory, 10: 155–164.
Schumahcer, G. & Vis, B. (2012). “Why do social democrats retrench the welfare state? A simulation.” Journal of Artificial Societies and Social Simulation, 15(3). Accessed online via http://jasss.soc.surrey.ac.uk/15/3/4.html
Sørensen, J., P. Liénard, & C. Feeny. (2006). “Agent and instrument in judgments of ritual efficacy.” Journal of Cognition and Culture, 6(3-4): 463-482.
Sosis, R and Bressler, E.R. (2003). “Cooperation and commune longevity: a test of the costly signaling theory of religion.” Cross-Cultural Research, 37(2): 211-239.
Stark, R. & Bainbridge, W. (1996). A theory of religion. New Brunswick, N.J: Rutgers University Press.
Sun, R. (ed.). (2006). Cognition and multi-agent interaction: From cognitive modeling to social simulation. New York: Cambridge University Press.
Sun, R. (ed.). (2012). Grounding Social Sciences in Cognitive Sciences. Cambridge, Mass: MIT Press.
Sun, R. & Fleischer, P. (2012). “A cognitive social simulation of tribal survival strategies: The importance of cognitive and motivational factors.” Journal of Cognition and Culture, 12(3-4): 287-321.
Sun, R. & Hélie, S. (2013). “Psychologically realistic cognitive agents: taking human cognition seriously.” Journal of Experimental and Theoretical Artificial Intelligence, 25(1): 65-92.
R. Sun, X. Zhang, P. Slusarz, & R. Mathews. (2007). “The interaction of implicit learning, explicit hypothesis testing learning, and implicit-to-explicit knowledge extraction.” Neural Networks, 20(1): 34-47.
Whitehouse, H. (1995). Inside the Cult: Religious Innovation and Transmission in Papua New Guinea. Oxford and New York: Oxford University Press.
Whitehouse, H. (2004). Modes of Religiosity: A Cognitive Theory of Religious Transmission. Walnut Creek, CA: AltaMira Press.
Whitehouse, H., Kahn, K., Hochberg, M., & Bryson, J. (2012). “From the imaginary to the real: the back and forth between reality and simulation.” Religion, Brain & Behavior, 2(3): 219-224.
Wolfram, S. (2002). A New Kind of Science. Champaign, IL: Wolfram Media
Younger, S. (2010). “Leadership in small societies.” Journal of Artificial Societies and Social Simulation, 13(3). Accessed online via http://jasss.soc.surrey.ac.uk/13/3/5.html