Many social phenomena do not result solely from intentional actions by isolated individuals, but rather emerge as the result of repeated interactions among multiple individuals over time. However, such phenomena are often poorly captured by traditional empirical techniques. Moreover, complex adaptive systems are insufficiently described by verbal models. In this paper, we discuss how organizational psychologists and group dynamics researchers may benefit from the adoption of formal modeling, particularly agent-based modeling, for developing and testing richer theories. Agent-based modeling is well suited to capture multilevel dynamic processes and offers superior precision to verbal models. As an example, we present a model of social identity dynamics used to test the predictions of Brewer’s (1991) optimal distinctiveness theory, and discuss how the model extends the theory and produces novel research questions. We close with a general discussion on theory development using agent-based models.
AbramsD. (1994). Social self-regulation. Personality and Social Psychology Bulletin, 20(5), 473–483.
2.
AndersonE. G.LewisK. (2013). A dynamic model of individual and collective learning amid disruption. Organization Science, 25, 356–376.
3.
AxelrodR. (1997). The dissemination of culture. Journal of Conflict Resolution, 41, 203–226.
4.
BakP. (1996). How nature works: The science of self-organized criticality. New York, NY: Copernicus.
5.
BedauM. A. (1999). Can unrealistic computer models illuminate theoretical biology? In WuA. (Ed.), Proceedings of the 1999 Genetic and Evolutionary Computation Conference Workshop (pp. 20–23). Orlando, FL: GECC.
6.
BeisbartC. (2012). How can computer simulations produce new knowledge?European Journal for Philosophy of Science, 2, 395–434.
7.
BerdahlJ. L. (1998). The dynamics of composition and socialization in small groups: Insights gained from developing a computational model. In NealeM. A.MannixE. A.GruenfeldD. H. (Eds.), Research on managing groups and teams (Vol. 1, pp. 209–227). Greenwich, CT: JAI.
8.
BlanzM.MummendeyA.OttenS. (1995). Positive–negative asymmetry in social discrimination: The impact of stimulus valence and size and status differentials on intergroup evaluations. British Journal of Social Psychology, 34(4), 409–419.
9.
BrauerF.Castillo-ChávezC. (2001). Mathematical models in population biology and epidemiology. New York, NY: Springer.
10.
BrewerM. B. (1991). The social self: On being the same and different at the same time. Personality and Social Psychology Bulletin, 17(5), 475–482.
11.
BrewerM. B. (2007). The importance of being we: Human nature and intergroup relations. American Psychologist, 62(8), 728–738.
12.
BrewerM. B.CaporaelL. (2006). An evolutionary perspective on social identity: Revisiting groups. In SchallerM.SimpsonJ.KenrickD. (Eds.), Evolution and social psychology (pp. 143–161). New York, NY: Psychology Press.
13.
BrewerM. B.PickettC. L. (2002). The social self and group identification: Inclusion and distinctiveness motives in interpersonal and collective identities. In ForgasJ.WilliamsK. (Eds.), The social self: Cognitive, interpersonal, and intergroup perspectives (pp. 255–271). Philadelphia, PA: Psychology Press.
14.
BrewerM. B.WeberJ. G. (1994). Self-evaluation effects of interpersonal versus intergroup social comparison. Journal of Personality and Social Psychology, 66(2), 268–275.
15.
BrockmanJ. (1995). Third culture: Beyond the scientific revolution. New York, NY: Touchstone.
16.
CampbellD. T. (1974). “Downward causation” in hierarchically organised biological systems. In AyalaF.DobzhanskyT. (Eds.), Studies in the philosophy of biology (pp. 179–186). Berkeley: University of California Press.
17.
CentolaD.MacyM. (2007). Complex contagions and the weakness of long ties. American Journal of Sociology, 113, 702–734.
18.
CroninM. A.WeingartL. R.TodorovaG. (2011). Dynamics in groups: Are we there yet?The Academy of Management Annals, 5(1), 571–612.
19.
CrowderR. M.RobinsonM. A.HughesH. P. N.SimY. W. (2012). The development of an agent-based modeling framework for simulating engineering team work. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 42(6), 1425–1439.
20.
DeffuantG.NeauD.AmblardF.WeisbuchG. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 3, 87–98.
21.
EllemersN.van RijswijkW. (1997). Identity needs versus social opportunities: The use of group-level and individual-level identity management strategies. Social Psychology Quarterly, 60(1), 52–65.
22.
EpsteinJ. M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.
FangC.LeeJ.SchillingM. A. (2010). Balancing exploration and exploitation through structural design: The isolation of subgroups and organizational learning. Organization Science, 21, 625–642.
25.
GilbertN.TernaP. (2000). How to build and use agent-based models in social science. Mind & Society, 1, 57–72.
26.
GrayK.RandD. G.ErtE.LewisK.HershmanS.NortonM. I. (2014). The emergence of “us and them” in 80 lines of code: Modeling group genesis in homogeneous populations. Psychological Science, 25, 982–990.
27.
HaldaneJ. B. S. (1964). A defense of beanbag genetics. Perspectives in Biology and Medicine, 7(3), 343–359.
28.
HarrisonJ. R.LinZ.CarrollG. R.CarleyK. M. (2007). Simulation modeling in organizational and management research. Academy of Management Review, 32, 1229–1245.
29.
HoggM. A.TerryD. J. (Eds.). (2001). Social identity processes in organizational contexts. Philadelphia, PA: Psychology Press.
30.
HopfieldJ. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79, 2554–2558.
31.
HughesH. P. N.CleggC. W.RobinsonM. A.CrowderR. M. (2012). Agent-based modelling and simulation: The potential contribution to organizational psychology. Journal of Occupational and Organizational Psychology, 85(3), 487–502.
32.
IlgenD. R.HollenbeckJ. R.JohnsonM.JundtD. (2005). Teams in organizations: From input-process output models to IMOI models. Annual Reviews of Psychology, 56, 517–543.
Johnson-LairdP. N. (1983). Mental models: Toward a cognitive science of language, inference, and consciousness. Cambridge, MA: Harvard University Press.
35.
KalickS. M.HamiltonT. E. (1986). The matching hypothesis reexamined. Journal of Personality and Social Psychology, 51, 673–682.
36.
KauffmanS. A. (1971). Articulation of parts explanation in biology and the rational search for them. In BuckR. C.CohenR. S. (Eds.), PSA 1970 (pp. 257–272). Boston, MA: Philosophy of Science Association.
37.
LeonardelliG. L.PickettC. L.BrewerM. B. (2010). Optimal distinctiveness theory: A framework for social identity, social cognition, and intergroup relations. In ZannaM. P.OlsonJ. M. (Eds.), Advances in experimental social psychology (Vol. 43, pp. 66–115). New York, NY: Academic Press.
38.
LukeS.Cioffi-RevillaC.SullivanK.BalanG. C. (2005). MASON: A multi-agent simulation environment. Simulation, 81, 517–527.
39.
MacCounR. J. (2012). The burden of social proof: Shared thresholds and social influence. Psychological Review, 119, 345–372.
40.
MacyM. W.WillerR. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28, 143–166.
41.
MarksM. A.ZaccaroS. J.MathieuJ. E. (2000). Performance implications of leader briefings and team-interaction training for team adaptation to novel environments. Journal of Applied Psychology, 85, 971–986.
42.
McClellandJ. L.RumelhartD. E., & the PDP Research Group. (1986). Parallel distributed processing, Vol. 2: Psychological and biological models. Cambridge, MA: MIT Press.
43.
McGrathJ. E. (1986). Studying groups at work: Ten critical needs for theory and practice. In GoodmanP. and associates (Eds.), Designing effective work groups (pp. 362–392). San Francisco, CA: Jossey-Bass.
44.
McGrathJ. E.ArrowH.BerdahlJ. L. (2000). The study of groups: Past, present, and future. Personality and Social Psychology Review, 4, 95–105.
45.
MillerJ. H.PageS. E. (2007). Complex adaptive systems: An introduction to computational models of social life. Princeton, NJ: Princeton University Press.
46.
MitchellM. (1997). An introduction to genetic algorithms. Cambridge, MA: MIT Press.
47.
MoussaïdM.HelbingD.TheraulazG. (2011). How simple rules determine pedestrian behavior and crowd disasters. Proceedings of the National Academy of Sciences, 108, 6884–6888.
48.
NowakA.SzamrejJ.LatanéB. (1990). From private attitude to public opinion: A dynamic theory of social impact. Psychological Review, 97, 362–376.
49.
ParkeE. (in press). Experiments, simulations, and epistemic privilege. Philosophy of Science.
50.
PopperK. (1963). Conjectures and refutations. London, UK: Routledge.
51.
RailsbackS. F.GrimmV. (2012). Agent-based and individual-based modeling: A practical introduction. Princeton, NJ: Princeton University Press.
52.
ResnickM. (1994). Turtles, termites, and traffic jams: Explorations in massively parallel microworlds. Cambridge, MA: MIT Press.
53.
SchankJ. C. (2001). Beyond reductionism: Refocusing on the individual with individual-based modeling. Complexity, 6(3), 33–40.
54.
SchankJ. C. (2008). The development of locomotor kinematics in neonatal rats: An agent-based modeling analysis in group and individual contexts. Journal of Theoretical Biology, 254, 826–842.
55.
SchankJ. C.MayC. J.JoshiS. S. (2014). Models as scaffold for understanding. In CaporaelL. R.GriesemerJ. R.WimsattW. C. (Eds.), Developing scaffolds in evolution, culture, and cognition (pp. 147–167). Cambridge, MA: MIT Press.
56.
SchellingT. C. (1978). Micromotives and macrobehavior. New York, NY: W. W. Norton.
57.
ShepherdD.HaynieJ. M. (2009). Birds of a feather don’t always flock together: Identity management in entrepreneurship. Journal of Business Venturing, 24, 316–337.
58.
SimãoJ.ToddP. M. (2002). Modeling mate choice in monogamous mating systems with courtship. Adaptive Behavior, 10, 113–136.
59.
SimonB.BrownR. (1987). Perceived intragroup homogeneity in minority–majority contexts. Journal of Personality and Social Psychology, 53(4), 703–711.
60.
SimonB.HamiltonD. L. (1994). Self-stereotyping and social context: The effects of relative in-group size and in-group status. Journal of Personality and Social Psychology, 66(4), 699–711.
61.
SmaldinoP. E. (2014a). The cultural evolution of emergent group-level traits. Behavioral and Brain Sciences, 37, 243–295.
62.
SmaldinoP. E. (2014b). Group-level traits emerge. Behavioral and Brain Sciences, 37, 281–288.
63.
SmaldinoP. E.LubellM. (2014). Institutions and cooperation in an ecology of games. Artificial Life, 20, 207–221.
64.
SmaldinoP. E.PickettC.ShermanJ.SchankJ. (2012). An agent-based model of social identity dynamics. Journal of Artificial Societies and Social Simulation, 15(4). Retrieved fromhttp://jasss.soc.surrey.ac.uk/15/4/7.html
65.
SmaldinoP. E.RichersonP. J. (2013). Human cumulative cultural evolution as a form of distributed computation. In MichelucciP. (Ed.), Handbook of human computation (pp. 979–992). New York, NY: Springer.
66.
SmaldinoP. E.SchankJ. C. (2012). Human mate choice is a complex system. Complexity, 17(5), 11–22.
67.
SmithE. R. (2014). Evil acts and malicious gossip: A multiagent model of the effects of gossip in socially distributed person perception. Personality and Social Psychology Review. Advance online publication. doi:10.1177/1088868314530515
68.
SmithE. R.CollinsE. C. (2009). Contextualizing person perception: Distributed social cognition. Psychological Review, 116, 343–364.
69.
SmithE. R.ConreyF. R. (2007). Agent-based modeling: A new approach for theory building in social psychology. Personality and Social Psychology Review, 11, 87–104.
70.
StasserG. (1988). Computer simulation as a research tool: The DISCUSS model of group decision making. Journal of Experimental Social Psychology, 24, 393–422.
71.
TurnerJ. C.OakesP. J.HaslamS. A.McGartyC. (1994). Self and collective: Cognition and social context. Personality and Social Psychology Bulletin, 20(5), 454–463.
72.
VallacherR. R.NowakA. (1997). The emergence of dynamical social psychology. Psychological Inquiry, 8, 73–99.
73.
Van KnippenbergD. (2000). Work motivation and performance: A social identity perspective. Applied Psychology, 49(3), 357–371.
74.
VignolesV. L. (2011). Identity motives. In SchwartzS. J.LuyckxK.VignolesV. L. (Eds.), Handbook of identity theory and research (pp. 403–432). New York, NY: Springer.
75.
WeinhardtJ. M.VancouverJ. B. (2012). Computational models and organizational psychology: Opportunities abound. Organizational Psychology Review, 2(4), 267–292.
76.
WeisbergM. (2007). Who is a modeler?British Journal for the Philosophy of Science, 58, 201–233.
77.
WilenskyU. (1999). NetLogo. Evanston, IL: Northwestern University, Center for Connected Learning and Computer-Based Modeling. Retrieved fromhttp://ccl.northwestern.edu/netlogo/
78.
WilsonJ. M.GoodmanP.CroninM. A. (2007). Group learning. Academy of Management Review, 32, 1041–1059.
79.
WimsattW. C. (1974). Complexity and organization. In SchaffnerK.CohenR. S. (Eds.), PSA1972 (pp. 67–86). Boston, MA: Philosophy of Science Association.
80.
WimsattW. C. (1987). False models as means to truer theories. In NiteckiM. H.HoffmanA. (Eds.), Neutral models in biology (pp. 23–55). New York, NY: Oxford University Press.
