Release 0.31 · 8 October 2026

ISSS: routes into systems sciences

The ISSS student resource page is a useful entry into modelling, with software, training leads, and a bibliography spanning several method families. Its emphasis does not define the whole of systems sciences.

Bibliography and resource list read; listed books and articles have not all been read. Source spellings and edition ambiguities remain reviewable. The 57 rows below retain the source’s bibliographic statements, including edition variants. A title appearing twice is not silently treated as two distinct works.

Choose a route through the sources

For feedback over time, start with system dynamics. For heterogeneous actors and local interactions, inspect agent-based models. For relational structure, inspect network methods. Then ask what each representation leaves outside its boundary.

Compare these modelling routes with Ing’s design and inquiry route, the practice laboratory, and the Systems Sciences stewardship page. This comparison is editorial guidance rather than a recommendation attributed to ISSS.

Tools and training leads

These are links listed by ISSS. Historical dates, software editions, and access conditions need checking at the destination.

General overviews of systems methods

  1. Garson, G. D. 2009. Computerized simulation in the social sciences: A survey and evaluation. Simulation & Gaming 40.2: 267–279.
  2. Gilbert, N., and K. G. Troitzsch. 1999. Simulation for the social scientist. Philadelphia: Open Univ. Press.
  3. Lansing, J. S. 2003. Complex adaptive systems. Annual Review of Anthropology 32:183–204.
  4. Miller, J. H., and S. E. Page. 2007. Complex adaptive systems: An introduction to computational models of social life. Princeton, NJ: Princeton Univ. Press.
  5. Mitchell, M. 2011. Complexity: A guided tour. New York: Oxford Univ. Press.
  6. Shalizi, C. R. 2006. Methods and techniques of complex systems science: An overview. In Complex systems science in biomedicine . Edited by T. S. Deisboeck and J. Yasha Kresh, 33–114. New York: Springer.

Modelling methods

  1. de Savigny, D., and T. Adam, eds. 2009. Systems thinking for health systems strengthening. Geneva, Switzerland: World Health Organization.
  2. Epstein, J. M. & Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up. Washington, DC: The Brookings Institution.
  3. Finegood, D. T., L. Johnston, P. Giabbanelli, et al. 2012. Complexity and systems theory . Oxford Bibliographies: Public Health. New York: Oxford Univ. Press. Source-listed link
  4. Galea, S., M. Riddle, and G. A. Kaplan. 2010. Causal thinking and complex system approaches in epidemiology. International Journal of Epidemiology 39:97–106.
  5. Gilbert, N. & Troitzsch, Klaus G. (2005) Simulation for the Social Scientist . (Second Edition). New York: Open University Press.
  6. Heppenstall, A. J., Crooks, A. T., See, L. M., Batty, M. (Eds.) (2012). Agent-Based Models of Geographical Systems . Dordrecht: Springer.
  7. Janssen, M.A. (2015). Introduction to Agent Based Modeling.
  8. Leischow, S. J., A. Best, W. M. Trochim, et al. 2008. Systems thinking to improve the public’s health. American Journal of Preventive Medicine 35.2: S196–S203.
  9. Luke, D. A., and K. A. Stamatakis. 2012. Systems science methods in public health: Dynamics, networks and agents. Annual Review of Public Health 33:357–376.
  10. Norman, C. D. 2009. Health promotion as a systems science and practice. Journal of Evaluation in Clinical Practice 15.5: 868–872.
  11. Plsek, P. E., and T. Greenhalgh. 2001. The challenge of complexity in health care. British Medical Journal 323:625–628.
  12. Railsback, S. F. & Grimm, V. (2012). Agent-Based and Individual-Based Modeling . Princeton: Princeton University Press.
  13. Shoham, Y. & Leyton-Brown, K. (2009). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations . New York: Cambridge University Press.
  14. Weiss, G. (Ed.) (1999). Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence . Cambridge, MA: The MIT Press.
  15. Wilensky, U. & Rand, W. (2015). An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo . Cambridge, MA: The MIT Press.
  16. Willis, C. D., C. Mitton, J. Gordon, and A. Best. 2012. Systems tools for systems change. BMJ Quality and Safety 21.3: 250–262.

Agent-based models

  1. Auchincloss, A. H., and A. V. Diez Roux. 2008. A new tool for epidemiology: The usefulness of dynamic-agent models in understanding place effects on health. American Journal of Epidemiology 168.1: 1–8.
  2. Berk, R. 2008. How you can tell if the simulations in computational criminology are any good. Journal of Experimental Criminology 4:289–308.
  3. Epstein, J. M. 2006. Generative social science: Studies in agent-based computational modeling. Princeton, NJ: Princeton Univ. Press.
  4. Galea, S., C. Hall, and G. A. Kaplan. 2008. Social epidemiology and complex system dynamic modeling as applied to health behavior and drug use research. International Journal of Drug Policy 20.3: 209–216.
  5. Gilbert, N. 2008. Agent-based models. Thousand Oaks, CA: SAGE.
  6. Grimm, V., U. Berger, F. Bastiansen, et al. 2006. A standard protocol for describing individual-based and agent-based models. Ecological Modeling 198:115–126.
  7. Israel, N., and M. Wolf-Branigin. 2011. Nonlinearity in social service evaluation: A primer on agent-based modeling. Social Work Research 35.1: 20–24.
  8. Railsback, S. F., and V. Grimm. 2012. Agent-based and individual-based modeling: A practical introduction. Princeton, NJ: Princeton Univ. Press.

Social network models

  1. Borgatti, S. P., A. Mehra, D. J. Brass, and G. Labianca. 2009. Network analysis in the social sciences. Science Magazine 323:892–895.
  2. Christakis, N. A., and J. H. Fowler. 2009. Social network visualization in epidemiology. Norsk Epidemiology 19.1: 5–16.
  3. Hawe, P., C. Webster, and A. Shiell. 2004. A glossary of terms for navigating the field of social network analysis. Journal of Epidemiological and Community Health 58:971–975.
  4. Knoke, D., and S. Yang. 2008. Social network analysis. 2nd ed. Thousand Oaks, CA: SAGE.
  5. Luke, D. A., and J. K. Harris. 2007. Social network analysis in public health: History, methods, and applications. Annual Review of Public Health 28:69–93.
  6. Smith, K. P., and N. A. Christakis. 2008. Social networks and health. Annual Review of Sociology 34:405–429.
  7. Valente, T. W. 2010. Social networks and health: Models, methods, and applications. New York: Oxford Univ. Press.
  8. Wasserman, S., and K. Faust. 1994. Social network analysis: Methods and applications. New York: Cambridge Univ. Press.

System dynamics models

  1. Forrester, J. W. 2007. System dynamics—a personal view of the first fifty years. System Dynamics Review 23.2–3: 345–358.
  2. Groesser, S. N., and M. Schwaninger. 2012. Contributions to model validation: Hierarchy, process, and cessation. System Dynamics Review 28.2: 157–181.
  3. Homer, J. B., and G. B. Hirsch. 2006. System dynamics modeling for public health: Background and opportunities. American Journal of Public Health 3:452–458.
  4. Rahmandad, H., and J. D. Sterman. 2012. Reporting guidelines for simulation-based research in social sciences. System Dynamics Review 28.4: 396–411.
  5. Richardson, G. P. 1991. Feedback thought in social science and systems theory. Waltham, MA: Pegasus.
  6. Sterman, J. D. 2000. Business dynamics: Systems thinking and modeling for a complex world. Boston: McGraw Hill.
  7. Sterman, J. D. 2006. Learning from evidence in a complex world. American Journal of Public Health 96.3: 505–514.

Underlying theoretical constructs

  1. Cederman, L.-K. 2005. Computational models of social forms: Advancing generative process theory. American Journal of Sociology 110:864–893.
  2. Centola, D., and M. Macy. 2007. Complex contagions and the weakness of long ties. American Journal of Sociology 113.3: 702–734.
  3. Granovetter, M. S. 1973. The strength of weak ties. American Journal of Sociology 78.6: 1360–1380.
  4. Groesser, S. N., and M. Schaffernicht. 2012. Mental models of dynamic systems: Taking stock and looking ahead. System Dynamics Review 28.1: 46–68.
  5. Hedstrom, P., and P. Ylikoski. 2010. Causal mechanisms in the social sciences. Annual Review of Sociology 36:49–67.
  6. Richardson, G. P. 2011. Reflections on the foundations of system dynamics. System Dynamics Review 27.3: 219–243.
  7. Watts, D. J. 2004. The “new” science of networks. Annual Review of Sociology 30:243–270.
  8. Wolstenholme, E. F. 1992. The definition and application of a stepwise approach to model conceptualisation and analysis. European Journal of Operational Research 59:123–136.

Comparing and combining methods

  1. Bobashev, G. V., J. M. Epstein, D. M. Goedecke, and F. Yu. 2007. A hybrid epidemic model: Combining the advantages of agent- based and equation-based approaches. In Proceedings of the Winter Simulation Conference , 9–12 December 2007, Washington, DC. Edited by S. G. Henderson, N. Niller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, 1532–1537. New York: Association for Computing Machinery. Source-listed link
  2. Borshchev, A., and A. Filippov. 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 , 25–29 July 2004, Oxford, England. Edited by M. Kennedy, G. W. Winch, R. S. Langer, J. I. Rowe, and J. M. Yanni. Albany, NY: Systems Dynamics Society. Source-listed link
  3. El-Sayed, A., P. Scarborough, L. Seemann, and S. Galea. 2012. Social network analysis and agent-based modeling in social epidemiology. Epidemiologic Perspectives and Innovations 9:1. Source-listed link
  4. Epstein, J. M., J. Parker, D. Cummings, and R. A. Hammond. 2008. Coupled dynamics of fear and disease: Mathematical and computational explorations. PLoS ONE 3.12: e3955. Source-listed link

Beyond the student list

The ISSS home resource hub links proceedings, a primer, the wiki, and contributed maps and learning accounts. Follow those as separately authored collections.