Skip to main content
SpringerLink
Log in

Social institution, cognition, and survival: a cognitive–social simulation

  • Original Article
  • Published:
Mind & Society Aims and scope Submit manuscript

Abstract

Although computational models of cognitive agents that incorporate a wide range of cognitive functionalities have been developed in cognitive science, most of the work in social simulation still assumes rudimentary cognition on the part of the agents. In contrast, in this work, the interaction of cognition and social structures/processes is explored, through simulating survival strategies of tribal societies. The results of the simulation demonstrate interactions between cognitive and social factors. For example, we show that cognitive capabilities and tendencies may be relevant to what social institutions may be adopted. This work points to a cognitively based approach towards social simulation, as well as a new area of research—exploring the cognitive–social interaction through cognitively based social simulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. These tasks include serial reaction time tasks, artificial grammar learning tasks, process control tasks, categorical inference tasks, alphabetical arithmetic tasks, and the Tower of Hanoi task (see, e.g., Sun 2002). In addition, extensive work has been done on a complex minefield navigation task (Sun et al. 2001). Simulations involving motivational structures and metacognitive processes are also under way.

  2. Due to running-time considerations, the specialization threshold is held constant in all simulations reported here.

  3. This method is also known as Luce’s choice axiom (Watkins 1989). It is found to match psychological data in many domains.

  4. Note that our simulation so far did not deal with the evolution of cognitive attributes, such as learning rate and so on, which should be tackled in future work.

  5. Note that in this work, we did not deal with the evolution of social institutions in a substantive way. This issue should be tackled in future.

  6. But again, our simulation so far did not deal with the evolution of certain important cognitive attributes, such as learning rate and so on.

  7. For example, in CLARION, the cognitive parameters that might be evolved include learning rate, probability of using the bottom level, and so on.

References

  • Alexander J, Giesen B, Munch R, Smelser N (eds) (1987) The micro–macro link. University of California Press, Berkeley

  • Atran S, Norenzayan A (2003) Religion’s evolutionary landscape: counterintuition, commitment, compassion, and communion. Brain Behav Sci (in press)

  • Axelrod R (1984) The evolution of cooperation. Basic Books, New York

    Google Scholar 

  • Bar-Yam Y (1997) Dynamics of complex systems. Perseus Books, New York

    Google Scholar 

  • Bourdieu P, Wacquant L (1992) An invitation to reflexive sociology. University of Chicago Press, Chicago

    Google Scholar 

  • Boyer P, Ramble C (2001) Cognitive templates for religious concepts: cross-cultural evidence for recall of counter-intuitive representations. Cogn Sci 25:535–564

    Article  Google Scholar 

  • Camerer C (1997) Progress in behavioral game theory. J Econ Perspect 11(4):167–188

    Google Scholar 

  • Carley K, Newell A (1994) The nature of social agent. J Math Sociol 19(4):221–262

    Article  Google Scholar 

  • Castelfranchi C (2001) The theory of social functions: challenges for computational social science and multi-agent learning. In: Ron Sun (ed) Cognitive systems research, special issue on the multi-disciplinary studies of multi-agent learning 2(1):5–38

  • Cecconi F, Parisi D (1998) Individual versus social survival strategies. J Artif Soc Soc Simul 1(2) http://www.soc.surrey.ac.uk/JASSS/1/2/1.html

  • Conte R, Castelfranchi C (1995) Understanding the effects of norms in social groups through simulation. In: Gilbert N, Conte R (eds) Artificial societies: the computer simulation of social life. UCL Press, London

    Google Scholar 

  • Cosmides L, Tooby J (1994) Beyond intuition and instinct blindness: toward an evolutionarily rigorous cognitive science. Cognition 50:41–77

    Article  Google Scholar 

  • Doran J, Palmer M (1995) The EOS project: integrating two models of Palaeolithic social change. In: Gilbert N, Conte R (eds) Artificial societies. UCL Press, London

    Google Scholar 

  • Doran J, Palmer M, Gilbert N, Mellars P (1994) The EOS project: modeling upper Palaeolithic social change. In: Gilbert N, Doran J (eds) Simulating societies. UCL Press, London

    Google Scholar 

  • Durkheim W (1895/1962) The rules of the sociological method. The Free Press, Glencoe

  • Gilbert N (1995) Simulation: an emergent perspective. In: Conference on new technologies in the social sciences. Bournemouth, UK

  • Gray W, Altmann E (2001) Cognitive modeling and human–computer interaction. In: Karwowski W (ed) International encyclopedia of ergonomics and human factors. Taylor and Francis, New York 1:387–391

  • Hutchins E (1995) How a cockpit remembers its speeds. Cogn Sci 19:265–288

    Article  Google Scholar 

  • Kahan J, Rapoport A (1984) Theories of coalition formation. Erlbaum, Mahwah, NJ

    Google Scholar 

  • Kenrick D, Li N, Butner J (2003) Dynamical evolutionary psychology: Individual decision rules and emergent social norms. Psychol Rev 110(1):3–28

    Article  Google Scholar 

  • Kluver J, Malecki R, Schmidt J, Stoica C (2003) Sociocultural evolution and cognitive ontogenesis: a sociocultural-cognitive algorithm. Comput Math Organ Theory 9:255–273

    Article  Google Scholar 

  • Kluver J, Schmidt J, Stoica C (2005) The emergence of social order by processes of typifying: a computational model. J Math Sociol 29:155–176

    Article  Google Scholar 

  • Lustick I (2000) Agent-based modeling of collective identity: testing constructivist theory. J Artif Soc Soc Simul 3(1) http://www.soc.surrey.ac.uk/JASSS/3/1/1.html

  • Mandler J (1992) How to build a baby. Psychol Rev 99(4):587–604

    Article  Google Scholar 

  • 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

  • Reber A (1989) Implicit learning and tacit knowledge. J Exp Psychol Gen 118(3):219–235

    Article  Google Scholar 

  • Reynolds R (1994) Learning to co-operate using cultural algorithms. In: Gilbert N, Doran J (eds) Simulating societies: the computer simulation of social phenomena. UCL Press, London, UK

    Google Scholar 

  • Sawyer R (2003) Multiagent systems and the micro-macro link in sociological theory. Sociol Methods Res 31(3):325–363

    Article  Google Scholar 

  • Schacter D (1990) Toward a cognitive neuropsychology of awareness: Implicit knowledge and anosagnosia. J Clin Exp Neuropsychol 12(1):155–178

    Google Scholar 

  • Seger C (1994) Implicit learning. Psychol Bull 115(2):163–196

    Article  Google Scholar 

  • Stadler M, Frensch P (1998) Handbook of implicit learning. Sage Publications, Thousand Oaks

    Google Scholar 

  • 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. Q J Exp Psychol 41A(3):553–577

    Google Scholar 

  • Sun R (1995) Robust reasoning: Integrating rule-based and similarity-based reasoning. Artificial Intelligence 75(2):241–296

    Article  Google Scholar 

  • Sun R (2001) Cognitive science meets multi-agent systems: A prolegomenon. Philos Psychol 14(1):5–28

    Article  Google Scholar 

  • Sun R (2002) Duality of the mind. Lawrence Erlbaum Associates, Mahwah, NJ

    Google Scholar 

  • Sun R (2003) A tutorial on CLARION 5.0. Technical report, Cognitive Science Department, Rensselaer Polytechnic Institute http://www.cogsci.rpi.edu/∼rsun/sun.tutorial.pdf

  • Sun R (ed) (2006) Cognition and multi-agent interaction: from cognitive modeling to social simulation. Cambridge University Press, New York

  • Sun R, Naveh I (2004) Simulating organizational decision-making using a cognitively realistic agent model. J Artif Soc Soc Simul 7(3) http://www.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 Netw 9(6):1217–1234

    Article  Google Scholar 

  • Sun R, Merrill E, Peterson T (2001) From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cogn Sci 25(2):203–244

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Tetlock P (2002) Social-functionalist frameworks for judgment and choice: the intuitive politician, theologian, and prosecutor. Psychol Rev 109:451–472

    Article  Google Scholar 

  • Tetlock P, Lebow RN (2001) Poking counterfactual holes in covering laws: cognitive styles and historical reasoning. Am Polit Sci Rev 95:829–843

    Google Scholar 

  • Watkins C (1989) Learning with delayed rewards, PhD Thesis. Cambridge University, Cambridge, UK

  • Wynn T (2002) Archaeology and cognitive evolution. Brain Behav Sci 25(3):389–438

    Google Scholar 

  • Zerubavel E (1997) Social mindscape: an invitation to cognitive sociology. Harvard University Press, Cambridge, MA

    Google Scholar 

Download references

Acknowledgment

We acknowledge Xi Zhang for his assistance in conducting this simulation.

Author information

Authors and Affiliations

  1. Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Ron Sun

  2. Department of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA

    Isaac Naveh

Authors
  1. Ron Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Isaac Naveh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ron Sun.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sun, R., Naveh, I. Social institution, cognition, and survival: a cognitive–social simulation. Mind & Society 6, 115–142 (2007). https://doi.org/10.1007/s11299-007-0027-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11299-007-0027-5

Keywords

Search

Navigation