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Multi-scale Resolution of Cognitive Architectures: A Paradigm for Simulating Minds and Society

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10899))

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.

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Notes

  1. 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. 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

  1. Sun, R.: Cognition and Multi-agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, Cambridge (2006)

    Google Scholar 

  2. Prietula, M., Carley, K., Gasser, L.: Simulating Organizations: Computational Models of Institutions and Groups, vol. 1. The MIT Press, Cambridge (1998)

    Google Scholar 

  3. Vallacher, R.R., Read, S.J., Nowak, A.: Computational Social Psychology. Routledge, Abingdon (2017)

    Google Scholar 

  4. Simon, H.A.: Bounded rationality and organizational learning. Organ. Sci. 2(1), 125–134 (1991)

    Article  Google Scholar 

  5. Anderson, J.R.: How can the Human Mind Occur in the Physical Universe?. Oxford University Press, Oxford (2007)

    Book  Google Scholar 

  6. 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)

    Article  Google Scholar 

  7. Gonzalez, C., Lerch, F.J., Lebiere, C.: Instance-based learning in dynamic decision making. Cognit. Sci. 27(4), 591–635 (2003)

    Article  Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. West, R.L., Lebiere, C.: Simple games as dynamic, coupled systems: randomness and other emergent properties. Cognit. Syst. Res. 1(4), 221–239 (2001)

    Article  Google Scholar 

  11. 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)

    Google Scholar 

  12. 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)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. Schelling, T.C.: Models of segregation. Am. Econ. Rev. 59(2), 488–493 (1969)

    Google Scholar 

  17. 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)

    Google Scholar 

  18. Epstein, J.M.: Modeling civil violence: an agent-based computational approach. Proc. Natl. Acad. Sci. 99(suppl 3), 7243–7250 (2002)

    Article  Google Scholar 

  19. Epstein, J.M.: Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science. Princeton University Press, Princeton (2014)

    Google Scholar 

  20. 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

    Chapter  Google Scholar 

  21. 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

    Chapter  Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. Pires, B., Crooks, A.T.: Modeling the emergence of riots: a geosimulation approach. Comput. Environ. Urban Syst. 61, 66–80 (2017)

    Article  Google Scholar 

  24. 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

    Chapter  Google Scholar 

  25. Rao, A.S., Georgeff, M.P., et al.: BDI agents: from theory to practice. In: ICMAS, vol. 95, pp. 312–319 (1995)

    Google Scholar 

  26. Schmidt, B.: The modelling of human behaviour: The PECS reference models. SCS-Europe BVBA (2000)

    Google Scholar 

  27. 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)

    Google Scholar 

  28. Lebiere, C., Best, B.J.: From microcognition to macrocognition: architectural support for adversarial behavior. J. Cognit. Eng. Decis. Mak. 3(2), 176–193 (2009)

    Article  Google Scholar 

  29. 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)

    Google Scholar 

  30. 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)

    Google Scholar 

  31. Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990)

    Google Scholar 

  32. Anderson, J.R.: Spanning seven orders of magnitude: a challenge for cognitive modeling. Cognit. Sci. 26(1), 85–112 (2002)

    Article  Google Scholar 

  33. Huberman, B.A., Pirolli, P., Pitkow, J.E., Lukose, R.M.: Strong regularities in world wide web surfing. Science 280(5360), 95–97 (1998)

    Article  Google Scholar 

  34. 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)

    Google Scholar 

  35. 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)

    Google Scholar 

  36. 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)

    Article  Google Scholar 

  37. Schultz, W., Dayan, P., Montague, P.R.: A neural substrate of prediction and reward. Science 275(5306), 1593–1599 (1997)

    Article  Google Scholar 

  38. Sutton, R.S.: Learning to predict by the methods of temporal differences. Mach. Learn. 3(1), 9–44 (1988)

    Google Scholar 

  39. 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)

    Article  Google Scholar 

  40. Schultz, W.: Getting formal with dopamine and reward. Neuron 36(2), 241–263 (2002)

    Article  Google Scholar 

  41. Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput. Graph. 21(4), 25–34 (1987)

    Article  Google Scholar 

  42. Miller, J.H., Page, S.E.: Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press, Princeton (2009)

    Google Scholar 

  43. Gigerenzer, G., Todd, P.M., ABC Research Group, et al.: Simple Heuristics That Make Us Smart. Oxford University Press, Oxford (1999)

    Google Scholar 

  44. 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)

    Google Scholar 

  45. Simon, H.A.: The architecture of complexity. Proc. Am. Philos. Soc. 106(6), 467–482 (1962)

    Google Scholar 

  46. Anderson, P.W.: More is different: broken symmetry and the nature of the hierarchical structure of science. Science 177(4047), 393–396 (1972)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Biocomplexity Institute of Virginia Tech, Blacksburg, USA

    Mark G. Orr & Bianica Pires

  2. Carnegie Mellon University, Pittsburgh, USA

    Christian Lebiere

  3. University of Washington, Seattle, USA

    Andrea Stocco

  4. Institute for Human and Machine Cognition, Pensacola, USA

    Peter Pirolli

  5. George Mason University, Fairfax, USA

    William G. Kennedy

Authors
  1. Mark G. Orr
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  2. Christian Lebiere
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  3. Andrea Stocco
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  4. Peter Pirolli
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  5. Bianica Pires
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  6. William G. Kennedy
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Corresponding author

Correspondence to Mark G. Orr .

Editor information

Editors and Affiliations

  1. United States Military Academy, West Point, New York, USA

    Robert Thomson

  2. Bucknell University, Lewisburg, Pennsylvania, USA

    Christopher Dancy

  3. The Ohio State University, Columbus, Ohio, USA

    Ayaz Hyder

  4. University of Michigan–Flint, Flint, Michigan, USA

    Halil Bisgin

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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

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  • DOI: https://doi.org/10.1007/978-3-319-93372-6_1

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