Abstract
We put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative social science are interdependent and should thus be mutually informative. The thesis invokes a paradigm, the reciprocal constraints paradigm, that was designed to leverage the interdependence between the social and cognitive levels of scale for the purpose of building cognitive and social simulations with better resolution. In addition to explaining our thesis, we provide the current research context, a set of issues with the thesis and some parting thoughts to provoke discussion. We see this work as an initial step to motivate both social and cognitive sciences in a new direction, one that represents some unity of purpose and interdependence of theory and methods.
The research is (partially) based upon work supported by the Defense Advanced Research Projects Agency (DARPA), via the Air Force Research Laboratory (AFRL). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA, the AFRL or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Because cognitive systems are sometimes tightly yoked to neurophysiology, we consider three levels as central to our thesis: neurophysiology, cognitive architecture, and social systems.
- 2.
ABMs, however, can range in abstraction, from the stylized models just described to empirically-driven models; although the latter in no way implies incorporation of cognitive constraints.
References
Sun, R.: Cognition and Multi-agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, Cambridge (2006)
Prietula, M., Carley, K., Gasser, L.: Simulating Organizations: Computational Models of Institutions and Groups, vol. 1. The MIT Press, Cambridge (1998)
Vallacher, R.R., Read, S.J., Nowak, A.: Computational Social Psychology. Routledge, Abingdon (2017)
Simon, H.A.: Bounded rationality and organizational learning. Organ. Sci. 2(1), 125–134 (1991)
Anderson, J.R.: How can the Human Mind Occur in the Physical Universe?. Oxford University Press, Oxford (2007)
Stocco, A., Lebiere, C., Anderson, J.R.: Conditional routing of information to the cortex: a model of the basal ganglia’s role in cognitive coordination. Psychol. Rev. 117(2), 541–574 (2010)
Gonzalez, C., Lerch, F.J., Lebiere, C.: Instance-based learning in dynamic decision making. Cognit. Sci. 27(4), 591–635 (2003)
Stocco, A.: A biologically plausible action selection system for cognitive architectures: implications of basal ganglia anatomy for learning and decision-making models. Cognit. Sci. 42, 457–490 (2018)
Stocco, A., Murray, N.L., Yamasaki, B.L., Renno, T.J., Nguyen, J., Prat, C.S.: Individual differences in the simon effect are underpinned by differences in the competitive dynamics in the basal ganglia: An experimental verification and a computational model. Cognition 164, 31–45 (2017)
West, R.L., Lebiere, C.: Simple games as dynamic, coupled systems: randomness and other emergent properties. Cognit. Syst. Res. 1(4), 221–239 (2001)
Lebiere, C., Gray, R., Salvucci, D., West, R.: Choice and learning under uncertainty: a case study in baseball batting. In: Proceedings of the 25th Annual Meeting of the Cognitive Science Society, pp. 704–709. Erlbaum, Mahwah (2003)
Lebiere, C., Wallach, D., West, R.: A memory-based account of the prisoner’s dilemma and other 2x2 games. In: Proceedings of International Conference on Cognitive Modeling, pp. 185–193. Universal Press, NL (2000)
West, R.L., Stewart, T.C., Lebiere, C., Chandrasekharan, S.: Stochastic resonance in human cognition: Act-r vs. game theory, associative neural networks, recursive neural networks, q-learning, and humans. In: Proceedings of the 27th Annual Conference of the Cognitive Science Society, pp. 2353–2358. Lawrence Erlbaum Associates, Mahwah (2005)
Romero, O., Lebiere, C.: Simulating network behavioral dynamics by using a multi-agent approach driven by act-r cognitive architecture. In: Proceedings of the Behavior Representation in Modeling and Simulation Conference (2014)
Reitter, D., Lebiere, C.: Social cognition: memory decay and adaptive information filtering for robust information maintenance. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI, pp. 242–248 (2012)
Schelling, T.C.: Models of segregation. Am. Econ. Rev. 59(2), 488–493 (1969)
Axelrod, R., et al.: A model of the emergence of new political actors. In: Artificial societies The Computer Simulation of Social Life, pp. 19–39 (1995)
Epstein, J.M.: Modeling civil violence: an agent-based computational approach. Proc. Natl. Acad. Sci. 99(suppl 3), 7243–7250 (2002)
Epstein, J.M.: Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science. Princeton University Press, Princeton (2014)
Caillou, P., Gaudou, B., Grignard, A., Truong, C.Q., Taillandier, P.: A simple-to-use BDI architecture for agent-based modeling and simulation. In: Jager, W., Verbrugge, R., Flache, A., de Roo, G., Hoogduin, L., Hemelrijk, C. (eds.) Advances in Social Simulation 2015. AISC, vol. 528, pp. 15–28. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47253-9_2
Sakellariou, I., Kefalas, P., Stamatopoulou, I.: Enhancing netlogo to simulate BDI communicating agents. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds.) SETN 2008. LNCS (LNAI), vol. 5138, pp. 263–275. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87881-0_24
Malleson, N., See, L., Evans, A., Heppenstall, A.: Implementing comprehensive offender behaviour in a realistic agent-based model of burglary. Simulation 88(1), 50–71 (2012)
Pires, B., Crooks, A.T.: Modeling the emergence of riots: a geosimulation approach. Comput. Environ. Urban Syst. 61, 66–80 (2017)
Kennedy, W.G.: Modelling human behaviour in agent-based models. In: Heppenstall, A., Crooks, A., See, L., Batty, M. (eds.) Agent-Based Models of Geographical Systems, pp. 167–179. Springer, Heidelberg (2012). https://doi.org/10.1007/978-90-481-8927-4_9
Rao, A.S., Georgeff, M.P., et al.: BDI agents: from theory to practice. In: ICMAS, vol. 95, pp. 312–319 (1995)
Schmidt, B.: The modelling of human behaviour: The PECS reference models. SCS-Europe BVBA (2000)
West, R., Nagy, N., Karimi, F., Dudzik, K.: Detecting macro cognitive influences in micro cognition: using micro strategies to evaluate the SGOMS macro architecture as implemented in ACT-R. In: Proceedings of the 15th International Conference on Cognitive Modeling, pp. 235–236 (2017)
Lebiere, C., Best, B.J.: From microcognition to macrocognition: architectural support for adversarial behavior. J. Cognit. Eng. Decis. Mak. 3(2), 176–193 (2009)
Lebiere, C., Archer, R., Best, B., Schunk, D.: Modeling pilot performance with an integrated task network and cognitive architecture approach. Hum. Perform. Model. Aviat. (2008)
Ritter, F., Haynes, S.R., Cohen, M., Howes, A., John, B., Best, B., Lebiere, C., Jones, R.M., Crossman, J., Lewis, R.L., St. Amant, R., McBride, S.P., Urbas, L., Leuchter, S., Vera, A.: High-level behavior representation languages revisited. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI, pp. 242–248 (2012)
Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990)
Anderson, J.R.: Spanning seven orders of magnitude: a challenge for cognitive modeling. Cognit. Sci. 26(1), 85–112 (2002)
Huberman, B.A., Pirolli, P., Pitkow, J.E., Lukose, R.M.: Strong regularities in world wide web surfing. Science 280(5360), 95–97 (1998)
Fu, W.T., Pirolli, P.: Snif-act: a model of user navigation on the world wide web. Hum. Comput. Interact. 22(4), 355–412 (2007)
Chi, E.H., Rosien, A., Suppattanasiri, G., Williams, A., Royer, C., Chow, C., Cousins, S.: The bloodhound project: automating discovery of web usability issues using the infoscent simulator. In: ACM Conference on Human Factors in Computing Systems, CHI Letters, vol. 5, no. 1, pp. 505–512 (2003)
Middleton, F.A., Strick, P.L.: The temporal lobe is a target of output from the basal ganglia. Proc. Natl. Acad. Sci. 93(16), 8683–8687 (1996)
Schultz, W., Dayan, P., Montague, P.R.: A neural substrate of prediction and reward. Science 275(5306), 1593–1599 (1997)
Sutton, R.S.: Learning to predict by the methods of temporal differences. Mach. Learn. 3(1), 9–44 (1988)
Bunney, B.S., Chiodo, L.A., Grace, A.A.: Midbrain dopamine system electrophysiological functioning: a review and new hypothesis. Synapse 9(2), 79–94 (1991)
Schultz, W.: Getting formal with dopamine and reward. Neuron 36(2), 241–263 (2002)
Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput. Graph. 21(4), 25–34 (1987)
Miller, J.H., Page, S.E.: Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press, Princeton (2009)
Gigerenzer, G., Todd, P.M., ABC Research Group, et al.: Simple Heuristics That Make Us Smart. Oxford University Press, Oxford (1999)
Reitter, D., Lebiere, C.: Accountable modeling in ACT-UP, a scalable, rapid-prototyping ACT-R implementation. In: Proceedings of the 2010 International Conference on Cognitive Modeling (2010)
Simon, H.A.: The architecture of complexity. Proc. Am. Philos. Soc. 106(6), 467–482 (1962)
Anderson, P.W.: More is different: broken symmetry and the nature of the hierarchical structure of science. Science 177(4047), 393–396 (1972)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Orr, M.G., Lebiere, C., Stocco, A., Pirolli, P., Pires, B., Kennedy, W.G. (2018). Multi-scale Resolution of Cognitive Architectures: A Paradigm for Simulating Minds and Society. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2018. Lecture Notes in Computer Science(), vol 10899. Springer, Cham. https://doi.org/10.1007/978-3-319-93372-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-93372-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93371-9
Online ISBN: 978-3-319-93372-6
eBook Packages: Computer ScienceComputer Science (R0)