Abstract
The models used in social simulation to date have mostly been very simplistic cognitively, with little attention paid to the details of individual cognition. This work proposes a more cognitively realistic approach to social simulation. It begins with a model created by Gilbert (1997) for capturing the growth of academic science. Gilbert’s model, which was equation-based, is replaced here by an agent-based model, with the cognitive architecture CLARION providing greater cognitive realism. Using this cognitive agent model, results comparable to previous simulations and to human data are obtained. It is found that while different cognitive settings may affect the aggregate number of scientific articles produced, they do not generally lead to different distributions of number of articles per author. The paper concludes with a discussion of the correspondence between the model and the constructivist view of academic science. It is argued that using more cognitively realistic models in simulations may lead to novel insights.
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Ahlert M (2003) An axiomatic approach to bounded rationality in negotiations. Paper read at the 2003 European Meeting of the Economic Science Association, September 18–21, Erfurt
Anderson JR, Lebiere C (1998) The atomic components of thought. Lawrence Erlbaum Associates, Mahwah, NJ
Axelrod R (1987) The evolution of strategies in the iterated prisoner’s dilemma. In: Davis L (ed). Genetic algorithms and simulated annealing, Pitman
Axtell RL (2000) Why agents? On the varied motivations for agent computing in the social sciences. In: Proceedings of the Workshop on Agent Simulation: Applications, Models and Tools, Argonne National Laboratory, IL
Axtell RL, Axelrod J, Cohen M (1996) Aligning simulation models: A case study and results. Comput Math Organiz Theory 1(2):123–141
Best BJ, Lebiere C (2003) Teamwork, communication, and planning in ACT-R: Agents engaging in Urban combat in virtual environments. In: Proceedings of the IJCAI 2003 Workshop on Cognitive Modeling of Agents and Multi-Agent Interactions, Acapulco, Mexico
Bowers K, Regehr G, Balthazard C, Parker K (1990) Intuition in the context of discovery. Cognitive Psychology 17:72–110
Bruner J, Goodnow J, Austin J (1956) A study of thinking. Wiley, NY
Carley KM, Newell A (1994) The nature of the social agent. Journal of Mathematical Sociology 19(4):221–262
Castelfranchi C (2001) The theory of social functions: Challenges for computational social science and multi-agent learning. Cognitive Systems Research, Special Issue on the Multi-Disciplinary Studies of Multi-Agent Learn 2(1):5–38
Cecconi F, Parisi D (1998) Individual versus social survival strategies. Journal of Artificial Societies and Social Simulation, 1(2). Available online at http://www.soc.surrey.ac.uk/JASSS/1/2/1.html
Citro CF, Hanushek EA (eds) (1991) Improving information for social policy decisions: The use of microsimulation modeling. Vol I: Review and recommendations. National Academy Press, Washington, DC
Cleeremans A (1997) Principles for implicit learning. In: Berry D (ed), How implicit is implicit learning? Oxford University Press, Oxford UK
Conte R, Gilbert N (1995) Introduction. In: Gilbert GN, Conte R (eds), Artificial societies: The Computer Simulation of Social Life. UCL Press, London UK
Edmonds B, Moss S (2001) The importance of representing cognitive processes in multi-agent models. In: Dorffner G, Bischof H, Hornik K (eds), Artificial neural networks—ICANN’2001, Springer-Verlag: Lecture Notes in Computer Science vol. 2130, pp 759–766
Epstein JM, Axtell RL (1996) Growing artificial societies: Social science from the bottom up. MIT Press, Cambridge
Gergen KJ (1985) The social constructionist movement in modern psychology. Amer Psychol 40(3):266–275
Gergen KJ, Gergen MM (1991) Towards reflexive methodologies. In: Steier F (ed), Research and reflexivity. Sage, Newsbury Park CA
Gilbert N (1997) A simulation of the structure of academic science. Sociological Research Online 2(2). Available online at http://www.socresonline.org.uk/socresonline/2/2/3.html.
Glaserfeld E (1987) The construction of knowledge. Intersystems Publications Seaside
Grobman KH, Gilmore RO (2003) The gradual emergence of domain-general problem solving strategies during infancy. Manuscript under revision for Child Development
Hannum WH (1973) A study of select factors influencing the retention of rules. Ph.D. Thesis, Florida State University, Florida
Haraway D (1988) The science question in feminism as a site of discourse on the privilege of partial perspective. Feminist Studies 14(3):575–579
Hutchins E (1995) How a cockpit remembers its speeds. Cognitive Science 19:265–288
Karmiloff-Smith A (1986) From meta-processes to conscious access: Evidence from children’s metalinguistic and repair data. Cognition 23:95–147
Klahr D, Langley P, Neches R (eds) (1987) Production system models of learning and development. MIT Press, Cambridge
Kleinberg JM, Kumar R, Prabhakar R, Sridhar R, Tomkins AS (1999) The web as a graph: Measurements, models, and methods. In: Proceedings of the 5th International Conference on Computing and Combinatorics July 26-28, Tokyo
Knorr-Cetina KD (1981) The manufacture of knowledge. Pergamon Press, Oxford
Knorr-Cetina KD (1983) Towards a constructivist interpretation of science. In: Knorr-Cetina KD, Mulkay M (eds), Science observed, Sage CA
Kuhn T (1962) The structure of scientific revolutions. Chicago University Press, Chicago
Kumar SR, Raghavan P, Rajagopalan S, Tomkins A (1999) Trawling the web for emerging cyber-communities. In: Proceedings of the 8th WWW Conference
Lave J (1988) Cognition in practice. Cambridge University Press, Cambridge UK
Lotka AJ (1926) The frequency distribution of scientific productivity. J Washington Acad Sci 16:317–323
Mandler J (1992) How to build a baby. Psychol Rev 99(4):587–604
Mathews R, Buss R, Stanley W, Blanchard-Fields F, Cho J, Druhan B (1989) Role of implicit and explicit processes in learning from examples: A synergistic effect. J Exper Psychol: Learning, Memory and Cognition 15:1083–1100
Moss S (1999) Relevance, realism and rigour: A third way for social and economic research. CPM Report No. 99-56. Center for Policy Analysis, Manchester Metropolitan University, Manchester UK
Nosofsky R, Palmeri T, McKinley S (1994) Rule-plus-exception model of classification learning. Psychol Rev 101(1):53–79
Oliver JE (1991) The incomplete guide to the art of discovery. Columbia University Press, NY.
Reber A (1989) Implicit learning and tacit knowledge. J Exper Psychol: General 118(3):219–235
Resnick LB, Levine JM, Teasley SD (1991) Perspectives on socially shared cognition. Amer Psychol Assoc, Hyattsville MD
Ridley DR, Gonzales E (1994) Zipf’s law extended to small samples of adult speech. Perceptual and Motor Skills 79:153–154
Rosenbloom P, Laird J, Newell A (1993) The SOAR papers: Research on integrated intelligence. MIT Press, Cambridge
Rumelhart D, McClelland J (eds) (1986) Parallel distributed processing I. MIT Press, Cambridge
Schacter D (1990) Toward a cognitive neuropsychology of awareness: Implicit Knowledge and Anosagnosia. Journal of Clinical and Experimental Neuropsychology 12(1):155-178
Schwandt TA (1994) Constructivist, interpretivist approaches to human inquiry. In: N.K. Denzin and Y.S. Lincoln (eds), Handbook of qualitative research. Sage, Newbury Park CA
Seger C (1994) Implicit learning. Psychol Bull 115(2):163–196
Shanks D (1993) Human instrumental learning: A critical review of data and theory. British J Psychol 84:319–354
Shapin S (1982) History of science and its social reconstructions. History of Science 20:157–211
Simon HA (1957) Models of man, social and rational. Wiley, NY
Smolensky, P. (1988),“On the Proper Treatment of Connectionism,” Behavioral and Brain Sciences, 11(1), 1-74.
Stanley W, Mathews R, Buss R, Kotler-Cope S (1989) Insight without awareness: On the interaction of verbalization, instruction and practice in a simulated process control task. Quarterly J Exp Psychol 41A(3):553–577
Sun R (1994) Integrating rules and connectionism for robust commonsense reasoning. Wiley, NY
Sun R (1997) Learning, action, and consciousness: A hybrid approach towards modeling consciousness. Neural Networks, Special Issue on Consciousness 10(7):1317–1331
Sun R (2002) Duality of the mind. Lawrence Erlbaum Associates, Mahwah NJ
Sun R (2003) A tutorial on CLARION 5.0. Available online at http://www.cogsci.rpi.edu/rsun/sun.tutorial.pdf
Sun R, Merrill E, Peterson T (2001) From implicit skills to explicit knowledge: A bottom-up model of skill learning. Cognitive Sci 25(2):203–244
Sun R, Naveh I (2004) Simulating organizational decision-making using a cognitively realistic agent model. Journal of Artificial Societies and Social Simulation 7(3). http://jasss.soc.surrey.ac.uk/7/3/5.html
Sun R, Peterson T (1998) Autonomous learning of sequential tasks: Experiments and analyses. IEEE Trans Neural Networks 9(6):1217–1234
Sun R, Slusarz P, Terry C (2005) The interaction of the explicit and the implicit in skill learning: A dual-process approach. Psychol Rev 112(1):159–192
Sun R, Zhang X (2004) Top-down versus bottom-up learning in cognitive skill acquisition. Cognitive Systems Research 5(1):63–89
Thagard P (1992) Conceptual revolutions. Princeton University Press, Princeton
Watkins C (1989) Learning with delayed rewards. Ph.D. Thesis, Cambridge University, Cambridge UK
West RL, Lebiere C, Bothell DJ (2003) Cognitive architectures, game playing, and interactive agents. In: Ron Sun (ed), Proceedings of the IJCAI 2003 Workshop on Cognitive Modeling of Agents and Multi-Agent Interactions. Acapulco Mexico
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Isaac Naveh obtained a master’s degree in computer science at the University of Missouri. His research interests include hybrid cognitive models and multi-agent learning.
Ron Sun is Professor of Cognitive Science at Rensselaer Polytechnic Institute, and formerly the James C. Dowell Professor of Engineering and Professor of Computer Science at University of Missouri-Columbia. He received his Ph.D in 1992 from Brandeis University. His research interest centers around studies of cognition, especially in the areas of cognitive architectures, human reasoning and learning, cognitive social simulation, and hybrid connectionist models. For his paper on integrating rule-based and connectionist models for accounting for human everyday reasoning, he received the 1991 David Marr Award from Cognitive Science Society. He is the founding co-editor-in-chief of the journal Cognitive Systems Research, and also serves on the editorial boards of many other journals. He is the general chair and program chair for CogSci 2006, and a member of the Governing Board of International Neural Networks Society. His URL is: http://www.cogsci.rpi.edu/~rsun
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Naveh, I., Sun, R. A cognitively based simulation of academic science. Comput Math Organiz Theor 12, 313–337 (2006). https://doi.org/10.1007/s10588-006-8872-z
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DOI: https://doi.org/10.1007/s10588-006-8872-z