Abstract
We recently 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. We review our thesis here, provide the current research context, address a set of issues with the thesis, and provide 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 unity of purpose, an interdependence of theory and methods, and a call for the careful development of new approaches for understanding human social systems, broadly construed.
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Because cognitive systems are sometimes tightly yoked to neurophysiology, we consider three levels as central to our thesis: neurophysiology, cognitive architecture, and social systems
We don’t have an approximation of the degree to which our community clustering algorithm mapped onto the structure shown in Fig. 3.
Agent-based models, 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.
Simuilations on the order of say \(10^{4}\) to \(10^7\) agents; examples might include large communities within social media platforms; a bipartite graph among patients and care providers within a national health system; daily interactions in a multi-national corporation.
The ACT-R GitHub example described above used this platform
This idea has obvious roots in Wilson’s Sociobiological approach. Wilson (2000)
References
Anderson JR (2002) Spanning seven orders of magnitude: a challenge for cognitive modeling. Cognit Sci 26(1):85–112
Anderson JR (2007) How can the human mind occur in the physical universe?. Oxford University Press, Oxford
Anderson PW (1972) More is different: broken symmetry and the nature of the hierarchical structure of science. Science 177(4047):393–396
Axelrod R (1995) A model of the emergence of new political actors. The computer simulation of social life. Artificial societies, London, pp 19–39
Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech 10:P10008
Bunney BS, Chiodo LA, Grace AA (1991) Midbrain dopamine system electrophysiological functioning: a review and new hypothesis. Synapse 9(2):79–94
Caillou P, Gaudou B, Grignard A, Truong CQ, Taillandier P (2017) A simple-to-use bdi architecture for agent-based modeling and simulation. In: Advances in Social Simulation 2015, Springer, New York, pp 15–28
Chi EH, Rosien A, Suppattanasiri G, Williams A, Royer C, Chow C, Cousins S (2003) The bloodhound project: automating discovery of web usability issues using the infoscent simulator. In: ACM conference on human factors in computing systems, pp 505–512
Croft W (2008) Evolutionary linguistics. Annu Rev Anthropol 37:219–234
Epstein JM (2002) Modeling civil violence: an agent-based computational approach. Proc Natl Acad Sci USA 99(3):7243–7250
Epstein JM (2014) Agent\_Zero: toward neurocognitive foundations for generative social science. Princeton University Press, Princeton
Fagyal Z, Swarup S, Escobar AM, Gasser L, Lakkaraju K (2010) Centers and peripheries: network roles in language change. Lingua 120(8):2061–2079
Fu WT, Pirolli P (2007) Snif-act: a model of user navigation on the world wide web. Hum Comput Interact 22(4):355–412
Gigerenzer G, Todd PM (1999) Simple heuristics that make us smart. Oxford University Press, Oxford
Gonzalez C, Lerch FJ, Lebiere C (2003) Instance-based learning in dynamic decision making. Cognit Sci 27(4):591–635
Griffiths TL, Kalish ML (2007) Language evolution by iterated learning with bayesian agents. Cognit Sci 31(3):441–480
Hare M, Elman JL (1995) Learning and morphological change. Cognition 56(1):61–98
Hruschka DJ, Christiansen MH, Blythe RA, Croft W, Heggarty P, Mufwene SS, Pierrehumbert JB, Poplack S (2009) Building social cognitive models of language change. Trends Cognit Sci 13(11):464–469
Huberman BA, Pirolli P, Pitkow JE, Lukose RM (1998) Strong regularities in world wide web surfing. Science 280(5360):95–97
Ke J, Gong T, Wang WS (2008) Language change and social networks. Commun Comput Phys 3(4):935–949
Kennedy WG (2012) Modelling human behaviour in agent-based models. In: Agent-based models of geographical systems, Springer, New York, pp 167–179
Lebiere C, Best BJ (2009) From microcognition to macrocognition: architectural support for adversarial behavior. J Cognit Eng Decis Making 3(2):176–193
Lebiere C, Wallach D, West R (2000) A memory-based account of the prisoners dilemma and other 2 x 2 games. In: Proceedings of International Conference on Cognitive Modeling, Universal Press, Kansas, pp 185–193
Lebiere C, Gray R, Salvucci D, West R (2003) Choice and learning under uncertainty: A case study in baseball batting. In: Proceedings of the 25th Annual Meeting of the Cognitive Science Society, Mahwah, Erlbaum, pp 704–709
Lebiere C, Archer R, Best B, Schunk D (2008) Modeling pilot performance with an integrated task network and cognitive architecture approach. Hum Perform Model Aviat
Lou-Magnuson M, Onnis L (2018) Social network limits language complexity. Cognit Sci
Malleson N, See L, Evans A, Heppenstall A (2012) Implementing comprehensive offender behaviour in a realistic agent-based model of burglary. Simulation 88(1):50–71
Middleton FA, Strick PL (1996) The temporal lobe is a target of output from the basal ganglia. Proc Natl Acad Sci USA 93(16):8683–8687
Miller JH, Page SE (2009) Complex adaptive systems: an introduction to computational models of social life. Princeton University Press, Princeton
Newell A (1990) Unified theories of cognition. Harvard University Press, Cambridge
Nowak MA, Komarova NL, Niyogi P (2001) Evolution of universal grammar. Science 291(5501):114–118
Orr MG, Lebiere C, Stocco A, Pirolli P, Pires B, Kennedy WG (2018) Multi-scale resolution of cognitive architectures: A paradigm for simulating minds and society. International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation. Springer, New York, pp 3–15
Pais D, Caicedo-Nunez CH, Leonard NE (2012) Hopf bifurcations and limit cycles in evolutionary network dynamics. SIAM J Appl Dyn Syst 11(4):1754–1784
Pires B, Crooks AT (2017) Modeling the emergence of riots: a geosimulation approach. Comput Environ Urb Syst 61:66–80
Polinsky M, Van Everbroeck E (2003) Development of gender classifications: modeling the historical change from latin to french. Language, pp 356–390
Prietula M, Carley K, Gasser L (1998) Simulating organizations: computational models of institutions and groups, vol 1. The MIT Press, Cambridge
Rao AS, Georgeff MP (1995) Bdi agents: from theory to practice. ICMAS 95:312–319
Reitter D, Lebiere C (2010) Accountable modeling in act-up, a scalable, rapid-prototyping ACT-R implementation. In: Proceedings of the 2010 international conference on cognitive modeling
Reitter D, Lebiere C (2012) 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
Reynolds CW (1987) Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput Gr 21(4):25–34
Ritter F, Haynes SR, Cohen M, Howes A, John B, Best B, Lebiere C, Jones RM, Crossman J, Lewis RL, St Amant R, McBride SP, Urbas L, Leuchter S, Vera A (2012) High-level behavior representation languages revisited. In: Proceedings of the twenty-sixth AAAI conference on artificial intelligence, AAAI, pp 242–248
Romero O, Lebiere C (2014) 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
Sakellariou I, Kefalas P, Stamatopoulou I (2008) Enhancing netlogo to simulate BDI communicating agents. SETN, Springer, New York, pp 263–275
Schelling TC (1969) Models of segregation. Am Econ Rev 59(2):488–493
Schmidt B (2000) The modelling of human behaviour: the PECS reference models. SCS-Europe BVBA, Delft
Schultz W (2002) Getting formal with dopamine and reward. Neuron 36(2):241–263
Schultz W, Dayan P, Montague PR (1997) A neural substrate of prediction and reward. Science 275(5306):1593–1599
Sierhuis M, Clancey WJ, Van Hoof RJ (2007) Brahms: a multi-agent modelling environment for simulating work processes and practices. Int J Simul Process Model 3(3):134–152
Simon HA (1962) The architecture of complexity. Proc Am Philos Soc 106(6):467–482
Simon HA (1991) Bounded rationality and organizational learning. Organ Sci 2(1):125–134
Stocco A (2018) 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. https://doi.org/10.1111/cogs.12506
Stocco A, Lebiere C, Anderson JR (2010) Conditional routing of information to the cortex: a model of the basal ganglias role in cognitive coordination. Psychol Rev 117(2):541–574
Stocco A, Murray NL, Yamasaki BL, Renno TJ, Nguyen J, Prat CS (2017) 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
Sun R (2006) Cognition and multi-agent interaction: from cognitive modeling to social simulation. Cambridge University Press, Cambridge
Sutton RS (1988) Learning to predict by the methods of temporal differences. Mach Learn 3(1):9–44
Vallacher RR, Read SJ, Nowak A (2017) Computational social psychology. Routledge, London
West R, Nagy N, Karimi F, Dudzik K (2017) 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
West RL, Lebiere C (2001) Simple games as dynamic, coupled systems: randomness and other emergent properties. Cognit Syst Res 1(4):221–239
West RL, Stewart TC, Lebiere C, Chandrasekharan S (2005) 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, Lawrence Erlbaum Associates, Mahwah, pp 2353–2358
Wilson EO (2000) Sociobiology. Harvard University Press, Cambridge
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The Matrix Agent-Based Modeling Platform was developed by Parantapa Bhattacharya, Saliya Ekanayake, and Mandy Wilson.
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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. This article is an extended version of Orr et al. (2018).
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Orr, M.G., Lebiere, C., Stocco, A. et al. Multi-scale resolution of neural, cognitive and social systems. Comput Math Organ Theory 25, 4–23 (2019). https://doi.org/10.1007/s10588-018-09291-0
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DOI: https://doi.org/10.1007/s10588-018-09291-0