Skip to main content
Log in

Modeling High-Resolution Broadband Discourse in Complex Adaptive Systems

  • Published:
Nonlinear Dynamics, Psychology, and Life Sciences

Abstract

Numerous researchers and practitioners have turned to complexity science to better understand human systems. Simulation can be used to observe how the microlevel actions of many human agents create emergent structures and novel behavior in complex adaptive systems. In such simulations, communication between human agents is often modeled simply as message passing, where a message or text may transfer data, trigger action, or inform context. Human communication involves more than the transmission of texts and messages, however. Such a perspective is likely to limit the effectiveness and insight that we can gain from simulations, and complexity science itself. In this paper, we propose a model of how close analysis of discursive processes between individuals (high-resolution), which occur simultaneously across a human system (broadband), dynamically evolve. We propose six different processes that describe how evolutionary variation can occur in texts—recontextualization, pruning, chunking, merging, appropriation, and mutation. These process models can facilitate the simulation of high-resolution, broadband discourse processes, and can aid in the analysis of data from such processes. Examples are used to illustrate each process. We make the tentative suggestion that discourse may evolve to the “edge of chaos.” We conclude with a discussion concerning how high-resolution, broadband discourse data could actually be collected.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Anderson, P. (1999). Complexity theory and organization science. Organization Science, 10 216-232.

    Google Scholar 

  • Axelrod, R. (1984). The evolution of cooperation. NY: Basic Books.

    Google Scholar 

  • Axelrod, R., & Cohen, M. D. (1999). Harnessing complexity: Organizational implications of a scientific frontier. New York: The Free Press.

    Google Scholar 

  • Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern Information Retrieval. New York: ACM Press/Addison-Wesley.

    Google Scholar 

  • Bak, P., & Chen, K. (1991). Self-organized criticality. Scientific American, 264(1), 46-53.

    Google Scholar 

  • Bakeman, R. & Gottman, J. M. (1986), Observing interaction: An introduction to sequential analysis. New York: Cambridge University Press.

    Google Scholar 

  • Bastien, D., McPhee, R., & Bolton K. (1995). A study and extended theory of the structuration of climate. Communication Monographs, 62 87-109.

    Google Scholar 

  • Boden, D. (1997). Temporal frames: Time and talk in organizations. Time and Society, 6(1), 5-33.

    Google Scholar 

  • Botan, C. (1996). Communication work and electronic surveillance: A model for predicting panoptic effects. Communication Monographs, 63 293-313.

    Google Scholar 

  • Bousquet, F., Lynam, T., & d'Aquino P. (2000). Multi-agent simulation models in applied, natural resource use decision-making in developing countries. Conference of the International Society for Ecological Econimics, Canberra, Australia.

  • Brown, S., & Eisenhardt, K. (1998). Competing on the Edge, Boston: Harvard Business.

    Google Scholar 

  • Carley, K. (1997). Network text analysis: The network position of concepts. In C. W. Roberts (Ed.), Text Analysis for the Social Sciences, 79-102. Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Carley, K. M., & Kaufer, D. S. (1993). Semantic connectivity: Anapproach for analyzing symbols in semantic networks. Communication Theory, 3, 183-213.

    Google Scholar 

  • Carley, K., & Prietula M. (1994). Computational organization theory. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Casti, J. (1995). Cooperation: The ghost in the machinery of evolution. In J. Casti & A. Karlqvist (eds.) Cooperation and Conflict in General Evolutionary Processes, (pp. 63-88). NY: John Wiley and Sons.

    Google Scholar 

  • Cheng, Y. & Van de Ven A. H. (1996). Learning the innovation journey: Order out of chaos? Organization Science, 7, 593-614.

    Google Scholar 

  • Choi, T., Dooley, K., & Rungtusanatham M. (2001). Conceptualizing Supply Networks As A Complex Adaptive System: Its Meaning, Its Properties, And Its Implications. Journal of Operations Management, 19, 351-366.

    Google Scholar 

  • Corman, S. R. & Scott C. R. (1994). Perceived networks, activity foci, and observable communication in social collectivities. Communication Theory 4, 171-190.

    Google Scholar 

  • Corman, S. R., Kuhn, T. K, McPhee, R. D., & Dooley, K. J. (forthcoming). Studying complex discursive systems: Centering resonance analysis of organizational communication. Human Communication Research.

  • Danowski, J. A. (1982). Anetwork-based content analysis methodology for computer mediated communication: An illustration with a computer bulletin board. In: M. Burgoon (Ed.), Communication Yearbook 6 (pp. 904-925). Beverly Hills, CA: Sage.

    Google Scholar 

  • Danowski, J. A. (1988). Organizational infographics and automated auditing: Using computers to unobtrusively gather and analyze communication. In G. Goldhaber & G. Barnett (Eds.) Handbook of Organizational Communication (pp. 385-433). Norwood, NJ: Ablex.

    Google Scholar 

  • Danowski, J. (1993). Network analysis of message content. In W. D. Richards & G. A. Barnett (Eds.) Progress in Communication Sciences XII (pp. 197-222). Norwood, NJ: Ablex.

    Google Scholar 

  • Dautenhahn, K., & Coles, S. J. (2001). Narrative intelligence from the bottom up: A computational framework for the study of story-telling in autonomous agents. Journal of Artificial Societies and Social Simulation, 4(1) http: //www.soc.surrey.ac.uk/JASSS/4/1/1.html.

  • Deming, W.E. (1986). Out of the crisis. Cambridge, MA: MIT-CAES.

    Google Scholar 

  • DiMaggio, P. (1991). The micro-macro dilemma in organizational research: Implications of role system theory. In J. Huber (Ed.), Macro-Micro Linkages in Sociology, (pp. 76-98). Newbury Park, CA: Sage

    Google Scholar 

  • DiMaggio, P. & Powell W. W. (1983). The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. American Sociological Review 48, 147-160.

    Google Scholar 

  • Dooley, K. (1997). A complex adaptive systems model of organization change. Nonlinear Dynamics, Psychology, & Life Science, 1, 69-97.

    Google Scholar 

  • Dooley, K. (2001). The paradigms of quality: Evolution and revolution in the history of the discipline. Advances in the Management of Organizational Quality, 5, 1-28.

    Google Scholar 

  • Dooley, K., Johnson, T., & Bush, D. (1995). TQM, chaos, and complexity. Human Systems Management 14, 1-16.

    Google Scholar 

  • Dooley, K., & Van de Ven, A. (1999). Explaining complex organizational dynamics. Organization Science, 10, 358-372.

    Google Scholar 

  • Eoyang, G., (1997). Coping with chaos: Seven simple tools, Cheyenne, WY: Lagumo Press.

    Google Scholar 

  • Feichtinger, G., & Kopel, M. (1993). Chaos in nonlinear dynamical systems exemplified by an R&D model. European Journal of Operations Research, 68, 145-159.

    Google Scholar 

  • Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215-239.

    Google Scholar 

  • Goldstein, J. (1994). The Unshackled Organization. Portland: Productivity Press.

    Google Scholar 

  • Green, D.G. (1993). Emergent behaviour in biological systems. D.G. Green and T. J. Bossomaier (eds.), Complex Systems-From Biology to Computation, (pp. 25-36). Amsterdam: IOS Press.

    Google Scholar 

  • Gronn, P. (1983). Talk as the work: The Accomplishment of social administration. Administrative Science Quarterly, 28, 1-21.

    Google Scholar 

  • Grosz, B. J., Weinstein, S., & Joshi, A. K. (1995). Centering: A framework for modeling the local coherence of a discourse. Computational Linguistics, 21 203-225.

    Google Scholar 

  • Guastello, S. J. (1995). Chaos, Catastrophe, and Human Affairs Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Guastello, S. J. & Philippe, P. (1997). Dynamics in the development of large information exchange groups and virtual communities. Nonlinear Dynamics, Psychology, and Life Sciences, 1, 123-150.

    Google Scholar 

  • Holland, J.H. (1995), Hidden Order, Reading, MA: Addison-Wesley.

    Google Scholar 

  • Hopper, R. (1992). Telephone conversation. Bloomington: Indiana University Press

    Google Scholar 

  • House, R., D. Rousseau, & M. Thomas-Hunt (1995). The meso paradigm: A framework for the integration of micro and macro organizational behavior. Research in Organizational Behavior 17, 71-114.

    Google Scholar 

  • Juran, J. & F. Gryna (1980). Quality planning and analysis. NY: McGraw-Hill.

    Google Scholar 

  • Jayanthi, S. & Sinha, K.K. (1998). Innovation implementation in high technology manufacturing: Chaos-theoretic empirical analysis. Journal of Operations Management, 16, 471-494.

    Google Scholar 

  • Kauffman, S. (1993). Origins of Order. Oxford University Press.

  • Kuhn, T. (2000), The complex process of planned organizational change: Developing a model of knowledge, activity, and communication networks, Arizona State University, unpublished dissertation.

  • Lee, M. E. (1997). From enlightenment to chaos: Toward nonmodern social theory. In R. A. Eve, S. Horsfall, & M. E. Lee (Eds.), Chaos, complexity, and sociology: Myths, models, and theories (pp. 15-29). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Levinthal, D. & Warglien, M. (1999). Landscape design: Designing for local action in complex worlds. Organization Science, 10, 342-357.

    Google Scholar 

  • Levitt, R., Cohen, P., Kunz, J., Nass, C., Christiansen, T. & Jin Y. (1994). The virtual design team: Simulating how organizational structure and communication tools affect team performance. In K.M. Carley and M.J. Prietula, editors, Computational Organization Theory. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Levy, D. (1994). Chaos theory and strategy: Theory, applications, and managerial implications. Strategic Management Journal 15, 167-178.

    Google Scholar 

  • Lewis, C.I. (1929). Mind and the World-Order, New York: Charles Scribner's Sons.

    Google Scholar 

  • Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical cooccurrence. Behavior Research Methods, Instruments, & Computers, 28 203-208.

    Google Scholar 

  • Lynch, A. (1996). Thought contagion: How belief spreads through society, the new science of memes. New York: Basic Books.

    Google Scholar 

  • March, J. G. (1994): A Primer On Decision Making. NY: Free Press.

    Google Scholar 

  • Martin, J., Feldman, M., Hatch, M. J., & Sitkin, S. B. (1983). The uniqueness aradox in organizational stories. Administrative Science Quarterly, 28 438-453.

    Google Scholar 

  • McKelvey, B. (1997). Quasi-natural organization science. Organization Science, 8, 351-380.

    Google Scholar 

  • McKoon, G. & Ratcliff, R. (1998). Memory-based language models: Psycholinguistic research in the 1990s. Annual Review of Psychology, 49, 25-42.

    Google Scholar 

  • McPhee, R. D., & Zaug, P. (2000). The communicative constitution of organizations: A framework for explanation. The Electronic Journal of Communication/La Revue Electronique de Communication, 10.

  • Mizruchi, M, & L. Fein (1999). The social construction of organizational knowledge: Astudy of the uses of coercive, mimetic, and normative isomorphism. Administrative Science Quarterly 44, 653-683.

    Google Scholar 

  • Mumby, D. K. (1987). The political function of narrative in organizations. Communication Monographs, 54 113-127.

    Google Scholar 

  • Newell, A. (1992) Unified theories of cognition and the role of SOAR. In: J. Michon and A. Akyrek (Eds.) SOAR: A Cognitive Architecture in Perspective (pp. 25-79). New York: Kluwer.

    Google Scholar 

  • Peirce, J. (2000). The paradox of physicians and administrators in health care organizations. Health Care Management Review, 25, 1, 7-28.

    Google Scholar 

  • Poole, M., Van de Ven, A., Dooley, K., & Holmes, M. (2000), Organizational Change Processes: Theory and Methods for Research, Oxford: Oxford Press.

    Google Scholar 

  • Prigogine, I., & Stengers, I. (1984). Order out of chaos: Man's new dialogue with nature. New York: Bantam.

    Google Scholar 

  • Putnam, L. L., Phillips, N., & Chapman, P. (1996). Metaphors of communication and organization. In S. R. Clegg, C. Hardy, & W. R. Nord (Eds.), Handbook of organization studies (pp. 375-408). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Saperstein, A. M. (1997). The origins of order and disorder in physical and social deterministic systems. In R. A. Eve, S. Horsfall, & M. E. Lee (Eds.), Chaos, complexity, and sociology: Myths, models, and theories (pp. 102-124). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Schiffrin, D. (1994), Approaches to Discourse, Cambridge, MA: Blackwell.

    Google Scholar 

  • Stacey, R., 1992. Managing the Unknowable San Francisco, Jossey-Bass.

    Google Scholar 

  • Taylor, J. R., Flanagin, A. J., Cheney, G., & Seibold, D. R. (2001). Organizational communication research: Key moments, central concerns, and future challenges. In W. Gudykunst (Ed.), Communication yearbook 24 (pp. 99-137). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Thietart, R.A., & Forgues, B. (1995). Chaos theory and organization. Organization Science 6, 19-31.

    Google Scholar 

  • Trethewey, A., & Corman, S. R. (2001). Anticipating k-commerce: E-commerce, knowledge management, and organizational communication. Management Communication Quarterly, 14 (4), 619-628.

    Google Scholar 

  • Tulin, M. (1997). Talking organization: Possibilities for conversation analysis in organizational behavior research. Journal of Management Inquiry, 6, 101-119.

    Google Scholar 

  • Walker, C. & Dooley, K. (1999) The stability of self-organized rule following work teams. Computational and Mathematical Organization Theory 5, 5-30.

    Google Scholar 

  • Walton, M. (1986). The Deming management method. NY: Putnam.

    Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press.

    Google Scholar 

  • Weick, K. (1979). The Social psychology of organizing. New York: Random House.

    Google Scholar 

  • Weller, S. C., & Romney, A. K. (1990). Metric scaling: Correspondence analysis. Newbury Park, CA: Sage

    Google Scholar 

  • Zimmerman, B., Lindberg, C. & Plsek, P. (1998). Edgeware, VHA: Irving, TX.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kevin J. Dooley.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dooley, K.J., Corman, S.R., McPhee, R.D. et al. Modeling High-Resolution Broadband Discourse in Complex Adaptive Systems. Nonlinear Dynamics Psychol Life Sci 7, 61–85 (2003). https://doi.org/10.1023/A:1020414109458

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1020414109458

Navigation