Abstract
This chapter introduces and explains the main concepts that provide the theoretical background on how to model the ubiquitous socio-technical systems that are so important to modern life. First the notions of systems, adaptation and complexity are discussed as individual concepts before addressing complex adaptive systems as a whole. This is followed by a discussion on generative science and agent-based modelling, with special attention paid to how these concepts relate to socio-technical systems. Throughout the text examples of how the theories can be applied to real systems are provided. Armed with a solid understanding of concepts such as observer-dependence, evolution, intractability, emergence and self-organisation, the reader will have the right foundation for moving on to the practical aspects of building and using agent-based models for decision support in socio-technical systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
These systems have also been called large-scale socio-technical systems, complex socio-technical systems (Bonen 1981), socio-technical systems (Geels 2004), large technical systems (Bijker et al. 1987), complex innovation systems (Katz 2006), complex engineering systems (Ottens et al. 2006) and even the impressive sounding “system of systems” (DeLaurentis and Crossley 2005). For a detailed discussion on various application fields and uses of complex adaptive systems, please refer to the work of van der Lei et al. (2009).
- 2.
Please be careful, we seem to have misplaced our frictionless surface.
- 3.
Recursive structure is a characteristic of systems.
- 4.
- 5.
It just keeps coming.
- 6.
The specifically improved features, behaviours or traits are also called adaptations, but it is preferable to refer to these as adaptive traits to avoid ambiguity.
- 7.
A theory exploring how acquired or learned behaviour can become integrated into an orgnanism’s genetic markup.
- 8.
Alternatively, the attractors are sometimes depicted as the valleys in the fitness landscape, based on the premise that things can only roll downhill, and do so quite naturally.
- 9.
This inherent reversibility is also called the “arrow of time”, but if you start thinking that time arrows are baked into the fabric of reality you might be mixing your metaphors.
- 10.
Although normal English usage of “intractable” means uncooperative or stubborn, we are specifically using the computational complexity theory definition.
- 11.
Vast differentiates the super astronomically large from just ordinary large. For example, 1050 is a very, very large number. However, 10001000 is Vast (Dennet 1996).
- 12.
Concurring opinion in Jacobellis v. Ohio 378 U.S. 184 (1964) regarding possible obscenity in The Lovers.
- 13.
And new divisions for those parts, and for the parts into which they are divided, are always lurking at the edge of theory.
- 14.
- 15.
Modern greenhouses can of course be adapted to grow other crops. Some of the structures and systems would become completely redundant, others would need to be added, and still others would require some adjustments. Like the structurally simple greenhouse, there is potential for more, but the path dependency means that it will be much more costly to switch to a new crop if there is a risk that high cost investments, like a tomato picking robot, will become utterly useless.
- 16.
- 17.
How often do really simple structures fail? A rock, for example, tends to be flung from a catapult with a high degree of reliability.
- 18.
What else did you expect?
- 19.
Chaos is a complex behaviour, but chaos is not the only mechanism driving the complexity of complex adaptive systems, nor is chaos the same as complexity.
- 20.
Get your truly random numbers here: http://www.fourmilab.ch/hotbits.
- 21.
The terms stability and instability seem like each others opposites, when in fact instability is opposite robustness.
- 22.
Consider, for example, how the emergent property of living is lost if an organism is dissected, and how an amputated part ceases to be alive after removal.
- 23.
- 24.
than System Dynamics, Dynamic Systems or Discrete Event Simulation.
- 25.
It is interesting to note that Van Neumann’s idea is close to becoming a reality some 60 years later with the rise of open source 3D printers, which are currently able to build 90 % of themselves.
- 26.
Perhaps the economic crisis of 2008 could have been avoided or minimised if a complex adaptive systems approach was more widely used or appreciated. Although, we will never know, irreversibility being what it is.
- 27.
Agent-Based Computational Economics; essentially agent-based modelling with agents containing economic decision models.
- 28.
Ah, our old friend, context dependency. In this case, it means that no two agents will have exactly the same environment, because every agent will be in the environment for other agents, but not for himself.
- 29.
Random interaction soups and small-world networks only have a Poisson degree distribution, meaning that extremely popular agents are as common as extremely unpopular agents.
- 30.
Multitasking on single core computers is achieved by very quickly switching between tasks.
- 31.
Finally! Now we know the dirty little secret of agent-based modelling ….
References
Aldrich, H., & Whetten, D. (1981). Organization-sets, action-sets, and networks: making the most of simplicity. In Handbook of organizational design (Vol. 1, pp. 385–408).
Alexander, C. (1973). A city is not a tree. Surviving the city: a sourcebook of papers on urban livability, p. 106.
Allen, T., Tainter, J., & Hoekstra, T. (1999). Supply-side sustainability. Systems Research and Behavioral Science, 16, 403–427.
Argyris, C., & Schon, D. A. (1996). Organisational learning II; theory, method and practice. Amsterdam: Addison-Wesley.
Ashby, W. R. (1968). Variety, constraint, and the law of requisite variety. In Modern systems research for the behavioral scientist. Chicago: Aldine.
Axelrod, R. (1980). More effective choice in the prisoner’s dilemma. Journal of Conflict Resolution, 24(3), 379–403.
Baronchelli, A., Dall’Asta, L., Barratt, A., & Loreto, V. (2005). Topology induced coarsening in language games. Physical Review E 73, 015102.
Baronchelli, A., Loreto, V., Dall’Astra, L., & Barratt, A. (2006). Bootstrapping communication in language games: strategy, topology and all that. In Proceedings of the 6th international conference on the evolution of language (pp. 11–18).
Bijker, W., Hughes, T., & Pinch, T. (1987). The social construction of technological systems: new directions in the sociology and history of technology. Cambridge: MIT Press.
Boer, C., Verbraeck, A., & Veeke, H. (2002). Distributed simulation of complex systems: application in container handling. In Proceedings of SISO European simulation interoperability workshop (pp. 24–27).
Bonen, Z. (1981). Evolutionary behavior of complex sociotechnical systems. Research Policy, 10(1), 26–44.
Borshchev, A., & Filippov, A. (2004). From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. In Proceedings of the 22nd international conference of the system dynamics society (pp. 25–29).
Box, G. (1979). Some problems of statistics and everyday life. Journal of the American Statistical Association, 74(365), 1–4.
Boyson, S., Corsi, T., & Verbraeck, A. (2003). The e-supply chain portal: a core business model. Transportation Research, Part E, 39(2), 175–192.
Buchanan, M. (2000). In Ubiquity: the science of history or why the world is simpler than we think. London: Weidenfeld.
Buchanan, M. (2009). Economics: Meltdown modelling. Nature, 460(7256), 680.
Burdulis, A., Fitz, W., Vargas-Voracek, R., Lang, P., Steines, D., & Tsougarakis, K. (2010). Surgical tools facilitating increased accuracy, speed and simplicity in performing joint arthroplasty. US Patent App. 12/776,701.
Burks, A. (1970). Essays on cellular automata. Champaign: University of Illinois Press.
Callaway, D., Newman, M., Strogatz, S., & Watts, D. (2000). Network robustness and fragility: percolation on random graphs. Physical Review Letters, 85, 5468.
Campbell, N. (2002). Biology. Redwood City: Benjamin Cummings.
Checkland, P., & Checkland, P. (1999). Systems thinking, systems practice: includes a 30-year retrospective. New York: Wiley.
Cohen, M., March, J., & Olsen, J. (1972). Garbage can model of organizational choice. Administrative Science Quarterly, 17(1), 1–25.
Colletta, L. (2009). Political satire and postmodern irony in the age of Stephen Colbert and Jon Stewart. The Journal of Popular Culture, 42(5), 856–874.
Conway, J. (1970). The game of life. Scientific American, 223(4), 4.
Corsi, T., Boyson, S., Verbraeck, A., Van Houten, S., Han, C., & Macdonald, J. (2006). The real-time global supply chain game: new educational tool for developing supply chain management professionals. Transportation Journal, 45(3), 61.
Coulson, J., & Richardson, J. (1999). Coulson & Richardson’s chemical engineering, Stoneham: Butterworth/Heinemann.
Crutchfield, J. (1994). The calculi of emergence: computation, dynamics and induction. Physica D, 75(1–3), 11–54.
Darwin, C. (1985). The origin of the species. Baltimore: Penguin.
David, P. (2000). Path dependence and varieties of learning in the evolution of technological practice. In Technological innovation as an evolutionary process (p. 119). London: Cambridge University Press.
Dawkins, R. (1990). The selfish gene. London: Oxford University Press.
DeLaurentis, D., & Crossley, W. (2005). A taxonomy-based perspective for systems of systems design methods. In 2005 IEEE international conference on systems, man and cybernetics (Vol. 1).
Dennet, D. (1996). Darwin’s dangerous idea: evolution and the meanings of life. New York: Simon & Schuster.
Economides, N. (1996). The economics of networks. International Journal of Industrial Organization, 14(6), 673–699.
Economist, T. (2010). Agents of change. The Economist. http://www.economist.com/node/16636121.
Epstein, J. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.
Farmer, J., & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460(7256), 685–686.
Ferguson, C. (1968). Absence of copula and the notion of simplicity: a study of normal speech, baby talk, foreigner talk and pidgins.
Foerster, H. (1972). Perception of the future and the future of perception. Instructional Science, 1(1), 31–43.
Forrester, J. W. (1958). Industrial dynamics—a major breakthrough for decision makers. Harvard Business Review, 36(4), 37–66.
Forrester, J., & Wright, J. (1961). Industrial dynamics. Cambridge: MIT Press.
Funtowicz, S., & Ravetz, J. (1993). Science for the post-normal age. Futures, 25(7), 739–755.
Futuyma, D. (1983). Evolutionary interactions among herbivorous insects and plants. In Coevolution (pp. 207–231). Sunderland: Sinauer.
Gaukroger, S. (2001). Francis Bacon and the transformation of early-modern philosophy. Cambridge: Cambridge University Press.
Geels, F. W. (2004). From sectoral systems of innovation to socio-technical systems—Insights about dynamics and change from sociology and institutional theory. Research Policy, 33(6–7), 897–920.
Gleick, J. (1997). Chaos: making a new science. New York: Vintage/Ebury.
Gordon, G. (1978). The development of the general purpose simulation system (GPSS). In History of programming languages I table of contents (pp. 403–426).
Green, P. (1981). A new look at statistics in fission-track dating. Nuclear Tracks, 5(1), 77–86.
Hadeli, W., Valckenaers, P., Kollingbaum, M., & Brussel, H. V. (2004). Multi-agent coordination and control using stigmergy. Computers in Industry, 53(1), 75–96.
Hartmanis, J., Sewelson, V., & Immerman, N. (1983). Sparse sets in np-p: exptime versus nexptime. In STOC ’83: proceedings of the fifteenth annual ACM symposium on theory of computing (pp. 382–391). New York: ACM.
Hietbrink, O., Ruijs, M., & Breukers, A. (2008). The power of dutch greenhouse vegetable horticulture: an analysis of the private sector and its institutional framework. Technical report 2008-049, LEI Wageningen UR, The Hague.
Hix, J. (1996). The glasshouse. London: Phaidon Press.
Holland, J. (1996). Hidden order; how adaptation builds complexity. Reading: Addison-Wesley.
Holling, C. S. (2001). Understanding the complexity of economic, ecological, and social systems. Ecosystems, 4(5), 390–405.
Honkela, T., & Winter, J. (2003). Simulating language learning in community of agents using self-organizing maps.
Hume, D. (1962). A treatise of human nature, vol. 1. Glasgow: Collins.
Iyengar, S., & Kamenica, E. (2010). Choice proliferation, simplicity seeking, and asset allocation. Journal of Public Economics, 94(7–8), 530–539.
Jablonka, E., & Ziman, J. (2000). Biological evolution: processes and phenomena. In Technological innovation as an evolutionary process (pp. 13–26). Cambridge: Cambridge University Press.
Janis, I. (1982). Groupthink: psychological studies of policy decisions and fiascoes. Boston: Houghton.
Jantzen, D. (1980). When is it coevolution. Evolution, 34, 611–612.
Jennings, N. (2000). On agent-based software engineering. Artificial Intelligence, 117(2), 277–296.
Jones, R. (1965). The structure of simple general equilibrium models. The Journal of Political Economy, 73(6), 557.
Katz, J. S. (2006). Indicators for complex innovation systems. Research Policy, 35(7), 893–909.
Kauffman, S. (2008). Reinventing the sacred: a new view of science, reason and religion.
Kauffman, S., & Johnsen, S. (1991). Coevolution to the edge of chaos—coupled fitness landscapes, poised states, and coevolutionary avalanches. Journal of Theoretical Biology, 149(4), 467–505.
Kay, J. (2002). On complexity theory, exergy and industrial ecology: some implications for construction ecology. In C. Kibert, J. Sendzimir, & B. Guy (Eds.), Construction ecology: nature as the basis for green buildings (pp. 72–107). London: Spon.
Kellert, S. (1993). In the wake of chaos: unpredictable order in dynamical systems. Chicago: University of Chicago Press.
Kim, J. (1999). Making sense of emergence. Philosophical Studies, 95(1), 3–36.
Lanzola, G., Gatti, L., Falasconi, S., & Stefanelli, M. (1999). A framework for building cooperative software agents in medical applications. Artificial Intelligence in Medicine, 16(3), 223–249.
Lee, R. (2003). Ijade surveillant—an intelligent multi-resolution composite neuro-oscillatory agent-based surveillance system. Pattern Recognition, 36(6), 1425–1444.
Leontief, W. (1998). Environmental repercussions and the economic structure: an input-output approach. International Library of Critical Writings in Economics, 92, 24–33.
Lindblom, C., Cohen, D., & Warfield, J. (1980). Usable knowledge, social science and social problem solving. IEEE Transactions on Systems, Man and Cybernetics, 10(5), 281.
Luhmann, N. (1995). Social systems. Stanford: Stanford University Press.
Mandelbrot, B. (1983). The fractal geometry of nature. New York: Freeman.
Mandeville, B., & Harth, P. (1989). The fable of the bees. Baltimore: Penguin.
Mikulecky, D. (2001). The emergence of complexity: science coming of age or science growing old? Computers and Chemistry, 25(4), 341–348.
Miller, D. (1993). The architecture of simplicity. The Academy of Management Review, 18, 116–138.
Morin, E. (1999). Organization and complexity. Tempos in Science Nature: Structures, Relations, Complexity, 879, 115–121.
Munger, M. (2008). Blogging and political information: truth or truthiness? Public Choice, 134, 125–138.
Negenborn, R., De Schutter, B., & Hellendoorn, H. (2006). Multi-agent model predictive control of transportation networks. In Proceedings of the 2006 IEEE international conference on networking, sensing and control (ICNSC 2006) (pp. 296–301).
Newman, M. (2003). The structure and function of complex networks. SIAM Review, 45, 167–256.
Ottens, M., Franssen, M., Kroes, P., & van de Poel, I. (2006). Modelling infrastructures as socio-technical systems. International Journal of Critical Infrastructures, 2(2–3), 133–145.
Padgett, J., Lee, D., & Collier, N. (2003). Economic production as chemistry. Industrial and Corporate Change, 12(4), 843–877.
Prigogine, I. (1967). Introduction to thermodynamics of irreversible processes (3rd ed.). New York: Interscience.
Prigogine, I., & Stengers, I. (1984). Order out of chaos: man’s new dialogue with nature. Boulder: New Science Library.
Reynolds, C. (1987). Flocks, herds and schools: a distributed behavioral model. Computer Graphics, 21, 25–34.
Roberts, R. (1989). Serendipity: accidental discoveries in science. In Serendipity: accidental discoveries in science (p. 288).
Rohilla Shalizi, C. (2006). Methods and techniques of complex systems science: an overview. In Complex systems science in biomedicine (pp. 33–114). Berlin: Springer.
Rosenberg, R., & Karnopp, D. (1983). Introduction to physical system dynamics. New York: McGraw-Hill.
Ryan, A. (2008). What is a systems approach? arXiv:0809.1698.
Schelling, T. (1971). Dynamic models of segregation. The Journal of Mathematical Sociology, 1(2), 143–186.
Schneider, S., Easterling, W., & Mearns, L. (2000). Adaptation: Sensitivity to natural variability, agent assumptions and dynamic climate changes. Climate Change, 45(1), 203–221.
Schonberger, R. (1982). Japanese manufacturing techniques: nine hidden lessons in simplicity. New York: Free Press.
Simon, H. (1982). Models of bounded rationality. Cambridge: MIT Press.
Smith, A. (1963). An inquiry into the nature and causes of the wealth of nations. New York: Wiley.
Smith, J., & Szathmáry, E. (1997). The major transitions in evolution. London: Oxford University Press.
Stewart, J. P. (1964). Jacobellis vs Ohio, 378 u.s. 184, Jacobellis v. Ohio. Appeal from the supreme court of Ohio. No. 11.
Strogatz, S., & Henry, S. (2000). Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. New York: Westview Press.
Tautz, D., Trick, M., & Dover, G. (1986). Cryptic simplicity in DNA is a major source of genetic variation.
Teisman, G. Publiek Management op de Grens van Chaos en Orde: Over Leidinggeven en Organiseren in Complexiteit. Den Haag, SDU Uitgevers bv.
Tesfatsion, L. (2007). Agents come to bits: Towards a constructive comprehensive taxonomy of economic entities. Journal of Economic Behavior & Organization, 63(2), 333–346.
Thompson, J. (1994). The coevolutionary process. Chicago: University of Chicago Press.
Ulrich, W. (1988). C. west churchman-75 years. Systemic Practice and Action Research, 1(4), 341–350.
van den Muijzenberg, E. (1980). A history of greenhouses. Institute for Agricultural Engineering.
van der Lei, T. E., Bekebrede, G., & Nikolic, I. (2009). Critical infrastructures: A review from a complex systems perspective. International Journal of Critical Infrastructures, 5(4).
Von Bertalanffy, L. (1972). The history and status of general systems theory. The Academy of Management Journal, 15(4), 407–426.
Von Neumann, J., & Burks, A. (1966). Theory of self-reproducing automata. Urbana: University of Illinois Press.
Waldorp, M. (1992). Complexity: the emerging science at the edge of order and chaos. New York: Simon and Schuster.
Weber, B., & Depew, D. (2003). Evolution and learning: the Baldwin effect reconsidered. Cambridge: MIT Press.
Whitehead, A. N. (1911). An introduction to mathematics. Williams and Norgate.
Wilds, R., Kauffman, S., & Glass, L. (2008). Evolution of complex dynamics. Chaos: An Interdisciplinary Journal of Nonlinear Science, 18, 033109.
Williamson, W. O. (1987). The economic institutions of capitalism. New York: Free Press.
Wooldridge, M., & Jennings, N. (1995). Intelligent agents—theory and practice. Knowledge Engineering Review, 10(2), 115–152.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Nikolic, I., Kasmire, J. (2013). Theory. In: van Dam, K., Nikolic, I., Lukszo, Z. (eds) Agent-Based Modelling of Socio-Technical Systems. Agent-Based Social Systems, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4933-7_2
Download citation
DOI: https://doi.org/10.1007/978-94-007-4933-7_2
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-4932-0
Online ISBN: 978-94-007-4933-7
eBook Packages: Computer ScienceComputer Science (R0)