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System Dynamics, Machine Learning and Structural Validation
Intellect , 289 (2024); https://doi.org/10.1386/9781789387926_15
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References

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  27. Martin Jr., Donald, Prabhakaran, Vinodkumar, Kuhlberg, Jill, Smart, Andrew and Isaac, William S. (2020a), ‘Extending the machine learning abstraction boundary: A complex systems approach to incorporate societal context’, arXiv preprint, 2006:09663.
  28. Martin Jr., Donald, Prabhakaran, Vinodkumar, Kuhlberg, Jill, Smart, Andrew and Isaac, William S. (2020b), ‘Participatory problem formulation for fairer machine learning through community based system dynamics’, arXiv preprint, 2005:07572.
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  33. Montavon, Grégoire , Samek, Wojciech and Müller, Klaus-Robert (2018), ‘Methods for interpreting and understanding deep neural networks’, Digital Signal Processing, 73, pp. 1–15.
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  34. Moxnes, Erling and Davidsen, Pål I. (2016), ‘Intuitive understanding of steady-state and transient behaviors’, System Dynamics Review, 32:2, pp. 128–53.
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  35. Naumov, Sergey and Oliva, Rogelio (2019), Structural Dominance Analysis Toolset, System Dynamics Group, Cambridge, MA: Massachusetts Institute of Technology.
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  36. Nestor, Maslej, Fattorini, Loredana, Brynjolfsson, Erik, Etchemendy, John, Ligett, Katrina, Lyons, Terah, Manyika, James, Ngo, Helen, Niebles, Juan Carlos, Parli, Vanessa, Shoham, Yoav, Wald, Russell, Clark, Jack and Perrault, Raymond (2023), ‘The AI Index 2023 Annual Report’, AI Index Steering Committee, Institute for Human-Centered AI, Stanford: Stanford University, April, https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report_2023.pdf. Accessed 11 May 2023.
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  38. Oliva, Rogelio (2016), ‘Structural dominance analysis of large and stochastic models’, System Dynamics Review, 32:1, pp. 26–51.
     [Google Scholar]
  39. Oliva, Rogelio (2020), ‘On structural dominance analysis’, System Dynamics Review, 36:1, pp. 8–28.
     [Google Scholar]
  40. Payne-Sturges, Devon C., Ballard, Ellis, Cory-Slechta, Deborah A., Thomas, Stephen B. and Hovmand, Peter (2023), ‘Making the invisible visible: Using a qualitative system dynamics model to map disparities in cumulative environmental stressors and children's neurodevelopment’, Environmental Research, 21:115295.
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  42. Randers, Jorgen and Goluke, Ulrich (2020), ‘An earth system model shows self-sustained thawing of permafrost even if all man-made GHG emissions stop in 2020’, Scientific Reports, 10:1, pp. 1–9.
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  43. Richardson, George P. (1991), Feedback Thought in Social Science and Systems Theory, Philadelphia, PA: University of Pennsylvania Press.
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  44. Richardson, George P. (1995), ‘Loop polarity, loop dominance, and the concept of dominant polarity’, System Dynamics Review, 11:1, pp. 67–88.
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  45. Runge, Jakob , Bathiany, Sebastian , Bollt, Erik , Camps-Valls, Gustau , Coumou, Dim , Deyle, Ethan , Glymour, Clark , Kretschmer, Marlene , Mahecha, Miguel D. , Muñoz-Marí, Jordi , van Nes, Egbert H. , Peters, Jonas , Quax, Rick , Reichstein, Markus , Scheffer, Marten , Schölkopf, Bernhard , Spirtes, Peter , Sugihara, George , Sun, Jie , Zhang, Kun and Zscheischler, Jakob (2019), ‘Inferring causation from time series in Earth system sciences’, Nature Communications, 10:1, p. 2553.308
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  46. Saleh, Mohamed M. (2002), ‘The characterization of model behavior and its causal foundation’, Ph.D. dissertation, Bergen: University of Bergen.
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  47. Saleh, Mohamed , Oliva, Rogelio , Kampmann, Christian Erik and Davidsen, Pål (2010), ‘A comprehensive analytical approach for policy analysis of system dynamics models’, European Journal of Operational Research, 203:3, pp. 673–83.
     [Google Scholar]
  48. Sato, Jeremy B. (2016), ‘State space analysis of dominant structures in dynamic social systems’, Ph.D. dissertation, St. Louis, MO: Washington University in St. Louis.
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  49. Schoenberg, William ([2019] 2020), ‘Feedback system neural networks for inferring causality in directed cyclic graphs’, arXiv preprint, 1908:10336, https://arxiv.org/abs/1908.10336. Accessed 24 March 2021 .
  50. Schoenberg, William , Davidsen, Pål and Eberlein, Robert (2020), ‘Understanding model behavior using the loops that matter method’, System Dynamics Review, 36:2, pp. 158–90.
     [Google Scholar]
  51. Schoenberg, William , Hayward, John and Eberlein, Robert (2023), ‘Improving loops that matter’, Systems Dynamics Review, 39:2, pp. 140–51.
     [Google Scholar]
  52. Schoenberg, William and Swartz, Jeremy (2022), ‘Building more robust system dynamics models through validation’, in International Conference of the System Dynamics Society 2021, vol. 2, Red Hook, NY: Curran Associates, Inc., pp. 923–39.
     [Google Scholar]
  53. Schölkopf, Bernhard and Smola, Alexander J. (2008), Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, Cambridge, MA: MIT Press.
     [Google Scholar]
  54. Senge, Peter M. ([1990] 2006), The Fifth Discipline: The Art & Practice of the Learning Organization, Revised and Updated Edition, New York: Doubleday.
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  55. Spirtes, Peter and Kun Zhang (2016), ‘Causal discovery and inference: Concepts and recent methodological advances’, Applied Informatics, 3:1, p. 3.
     [Google Scholar]
  56. Sterman, John (2000), Business Dynamics: Systems Thinking and Modeling for a Complex World, New York: Irwin/McGraw-Hill.
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  57. Stone, Peter, Brooks, Rodney, Brynjolfsson, Erik, Calo, Ryan, Etzioni, Oren, Hager, Greg, Hirschberg, Julia, Kalyanakrishnan, Shivaram, Kamar, Ece, Kraus, Sarit, Leyton-Brown, Kevin, Parkes, David, Press, William, Saxenian, AnnaLee, Shah, Julie, Tambe, Milind and Teller, Astro (2016), ‘Artificial intelligence and life in 2030: The one hundred year study on artificial intelligence’, Report of the 2015–2016 Study Panel, Stanford: Stanford University, September, https://ai100.stanford.edu/sites/g/files/sbiybj18871/files/media/file/ai100report10032016fnl_singles.pdf. Accessed 11 May 2023.
  58. Swartz, Jeremy , Wasko, Janet , Marvin, Carolyn , Logan, Robert K. and Coleman, Beth (2019), ‘Philosophy of technology: Who is in the saddle?’, Journalism & Mass Communication Quarterly, 96:2, pp. 351–66.
     [Google Scholar]
  59. Wiener, Norbert (1956), ‘The theory of prediction’, in E. F. Beckenbach (ed.), Modern Mathematics for the Engineer: First Series, New York: McGraw-Hill, pp. 165–90.
     [Google Scholar]

References

  1. Barlas, Yaman (1996), ‘Formal aspects of model validity and validation in system dynamics’, System Dynamics Review, 12:3, pp. 183–210.
     [Google Scholar]
  2. Box, George E. P. (1979), ‘Robustness in the strategy of scientific model building’, in R. L. Launer and G. N. Wilkinson (eds), Robustness in Statistics, New York: Academic Press, Inc., pp. 201–36.
     [Google Scholar]
  3. Cybenko, George (1989), ‘Approximation by superpositions of a sigmoidal function’, Mathematics of Control, Signals and Systems, 2:4, pp. 303–14.
     [Google Scholar]
  4. Davidsen, Pål I. (1991), The Structure-Behavior Graph, Cambridge, MA: MIT Press, The System Dynamics Group.
     [Google Scholar]
  5. Eberlein, Robert Larry (1984), ‘Simplifying dynamic models by retaining selected behavior modes’, Ph.D. dissertation, Cambridge, MA: Massachusetts Institute of Technology.
     [Google Scholar]
  6. Ford, David N. (1999), ‘A behavioral approach to feedback loop dominance analysis’, System Dynamics Review, 15:1, pp. 3–36.
     [Google Scholar]
  7. Forrester, Jay W. (1961), Industrial Dynamics, Cambridge, MA: MIT Press.
     [Google Scholar]
  8. Forrester, Jay W. (1969), Urban Dynamics, Cambridge, MA: MIT Press.
     [Google Scholar]
  9. Forrester, Jay W. (1971), World Dynamics, Cambridge, MA: Wright-Allen Press.
     [Google Scholar]
  10. Forrester, Jay W. (1994), ‘System dynamics, systems thinking, and soft OR’, System Dynamics Review, 10:2&3, pp. 245–56.
     [Google Scholar]
  11. Forrester, Jay W. (2016), ‘Learning through system dynamics as preparation for the 21st century’, System Dynamics Review 32:3&4, pp. 187–203.
     [Google Scholar]
  12. Forrester, Nathan Blair (1982), ‘A dynamic synthesis of basic macroeconomic theory: Implications for stabilization policy analysis’, Ph.D. dissertation, Cambridge, MA: Massachusetts Institute of Technology.
     [Google Scholar]
  13. Ghaffarzadegan, Navid, Lyneis, John and Richardson, George P. (2011), ‘How small system dynamics models can help the public policy process’, System Dynamics Review, 27:1, pp. 22–44 .
  14. Ghahramani, Zoubin (2015), ‘Probabilistic machine learning and artificial intelligence’, Nature, 521:7553, p. 452.
     [Google Scholar]
  15. Gonçalves, Paulo (2009), ‘Behavior modes, pathways and overall trajectories: Eigenvector and eigenvalue analysis of dynamic systems’, System Dynamics Review, 25:1, pp. 35–62.
     [Google Scholar]
  16. Goodfellow, Ian , Bengio, Yoshua and Courville, Aaron (2016), Deep Learning, Cambridge, MA: MIT Press.
     [Google Scholar]
  17. Graham, Alan Karl (1977), ‘Principles of the relationship between structure and behavior of dynamic systems’, Ph.D. dissertation, Cambridge, MA: Massachusetts Institute of Technology.306
    [Google Scholar]
  18. Granger, Clive W. J. (1969), ‘Investigating causal relations by econometric models and cross-spectral methods’, Econometrica: Journal of the Econometric Society, 37, pp. 424–38.
     [Google Scholar]
  19. Gullett, Heidi L., Brown, Gregory L., Collins, Delores, Halko, Martha, Gotler, Robin, Stange, Kurt C. and Hovmand, Peter S. (2022), ‘Using Community-Based System Dynamics to Address Structural Racism in Community Health Improvement’, Journal of Public Health Management and Practice 28: 4(Supp.), pp. S130–37.
  20. Hayward, John and Boswell, Graeme P. (2014), ‘Model behaviour and the concept of loop impact: A practical method’, System Dynamics Review, 30:1, pp. 29–57.
     [Google Scholar]
  21. Hayward, John and Roach, Paul A. (2017), ‘Newton's laws as an interpretive framework in system dynamics’, System Dynamics Review, 33:3&4, pp. 183–218.
     [Google Scholar]
  22. Herrington, Gaya (2020), ‘Update to limits to growth: Comparing the World3 model with empirical data’, Journal of Industrial Ecology, 25:3, pp. 614–26.
     [Google Scholar]
  23. Hovmand, Peter S. (2014), Group Model Building and Community-based System Dynamics Process, New York: Springer Science+Business Media.
  24. Kampmann, Christian Erik (2012), ‘Feedback loop gains and system behaviour’, System Dynamics Review, 28:4, pp. 370–95. [First published in Proceedings of the 1996 International System Dynamics Conference, Cambridge, MA: Systems Dynamics Society, pp. 21–25.]
     [Google Scholar]
  25. Király, Gábor and Miskolczi, Péter (2019), ‘Dynamics of participation: System dynamics and participation—An empirical review’, Systems Research and Behavioral Science, 36:2, pp. 199–210.
  26. Littman, Michael L., Ajunwa, Ifeoma, Berger, Guy, Boutilier, Craig, Currie, Morgan, Doshi-Velez, Finale, Hadfield, Gillian, Horowitz, Michael C., Isbell, Charles, Kitano, Hiroaki, Levy, Karen, Lyons, Terah, Mitchell, Melanie, Shah, Julie, Sloman, Steven, Vallor, Shannon and Walsh, Toby (2021), ‘Gathering strength, gathering storms: The one hundred year study on artificial intelligence (AI100)’, 2021 Study Panel Report, Stanford: Stanford University, September, https://ai100.stanford.edu/sites/g/files/sbiybj18871/files/media/file/AI100Report_MT_10.pdf. Accessed 11 May 2023.
  27. Martin Jr., Donald, Prabhakaran, Vinodkumar, Kuhlberg, Jill, Smart, Andrew and Isaac, William S. (2020a), ‘Extending the machine learning abstraction boundary: A complex systems approach to incorporate societal context’, arXiv preprint, 2006:09663.
  28. Martin Jr., Donald, Prabhakaran, Vinodkumar, Kuhlberg, Jill, Smart, Andrew and Isaac, William S. (2020b), ‘Participatory problem formulation for fairer machine learning through community based system dynamics’, arXiv preprint, 2005:07572.
     [Google Scholar]
  29. Meadows, Donella H. , Meadows, Dennis L. , Randers, Jørgen and Behrens, William W. , III (1972), The Limits to Growth: A Report for the Club of Rome's Project on the Predicament of Mankind, New York: Universe Books.
     [Google Scholar]
  30. Meadows, Donella H. , Randers, Jørrgen and Meadows, Dennis L. (2004), Limits to Growth: The 30-Year Update, White River Junction, VT: Chelsea Green Publishing.
     [Google Scholar]
  31. Mojtahedzadeh, Mohammad T. (1996), Structural Analysis of the URBAN1 Model, Working Paper, Albany, NY: University at Albany State University of New York (SUNY).307
    [Google Scholar]
  32. Mojtahedzadeh, Mohammad , Andersen, David and Richardson, George P. (2004), ‘Using Digest to implement the pathway participation method for detecting influential system structure’, System Dynamics Review, 20:1, pp. 1–20.
     [Google Scholar]
  33. Montavon, Grégoire , Samek, Wojciech and Müller, Klaus-Robert (2018), ‘Methods for interpreting and understanding deep neural networks’, Digital Signal Processing, 73, pp. 1–15.
     [Google Scholar]
  34. Moxnes, Erling and Davidsen, Pål I. (2016), ‘Intuitive understanding of steady-state and transient behaviors’, System Dynamics Review, 32:2, pp. 128–53.
     [Google Scholar]
  35. Naumov, Sergey and Oliva, Rogelio (2019), Structural Dominance Analysis Toolset, System Dynamics Group, Cambridge, MA: Massachusetts Institute of Technology.
     [Google Scholar]
  36. Nestor, Maslej, Fattorini, Loredana, Brynjolfsson, Erik, Etchemendy, John, Ligett, Katrina, Lyons, Terah, Manyika, James, Ngo, Helen, Niebles, Juan Carlos, Parli, Vanessa, Shoham, Yoav, Wald, Russell, Clark, Jack and Perrault, Raymond (2023), ‘The AI Index 2023 Annual Report’, AI Index Steering Committee, Institute for Human-Centered AI, Stanford: Stanford University, April, https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report_2023.pdf. Accessed 11 May 2023.
  37. Nordhaus, William D. (1973), ‘World dynamics: Measurement without data’, The Economic Journal, 83:332, pp. 1156–83.
     [Google Scholar]
  38. Oliva, Rogelio (2016), ‘Structural dominance analysis of large and stochastic models’, System Dynamics Review, 32:1, pp. 26–51.
     [Google Scholar]
  39. Oliva, Rogelio (2020), ‘On structural dominance analysis’, System Dynamics Review, 36:1, pp. 8–28.
     [Google Scholar]
  40. Payne-Sturges, Devon C., Ballard, Ellis, Cory-Slechta, Deborah A., Thomas, Stephen B. and Hovmand, Peter (2023), ‘Making the invisible visible: Using a qualitative system dynamics model to map disparities in cumulative environmental stressors and children's neurodevelopment’, Environmental Research, 21:115295.
  41. Pearl, Judea (2009), ‘Causal inference in statistics: An overview’, Statistics Surveys, 3, pp. 96–146.
     [Google Scholar]
  42. Randers, Jorgen and Goluke, Ulrich (2020), ‘An earth system model shows self-sustained thawing of permafrost even if all man-made GHG emissions stop in 2020’, Scientific Reports, 10:1, pp. 1–9.
     [Google Scholar]
  43. Richardson, George P. (1991), Feedback Thought in Social Science and Systems Theory, Philadelphia, PA: University of Pennsylvania Press.
     [Google Scholar]
  44. Richardson, George P. (1995), ‘Loop polarity, loop dominance, and the concept of dominant polarity’, System Dynamics Review, 11:1, pp. 67–88.
     [Google Scholar]
  45. Runge, Jakob , Bathiany, Sebastian , Bollt, Erik , Camps-Valls, Gustau , Coumou, Dim , Deyle, Ethan , Glymour, Clark , Kretschmer, Marlene , Mahecha, Miguel D. , Muñoz-Marí, Jordi , van Nes, Egbert H. , Peters, Jonas , Quax, Rick , Reichstein, Markus , Scheffer, Marten , Schölkopf, Bernhard , Spirtes, Peter , Sugihara, George , Sun, Jie , Zhang, Kun and Zscheischler, Jakob (2019), ‘Inferring causation from time series in Earth system sciences’, Nature Communications, 10:1, p. 2553.308
    [Google Scholar]
  46. Saleh, Mohamed M. (2002), ‘The characterization of model behavior and its causal foundation’, Ph.D. dissertation, Bergen: University of Bergen.
     [Google Scholar]
  47. Saleh, Mohamed , Oliva, Rogelio , Kampmann, Christian Erik and Davidsen, Pål (2010), ‘A comprehensive analytical approach for policy analysis of system dynamics models’, European Journal of Operational Research, 203:3, pp. 673–83.
     [Google Scholar]
  48. Sato, Jeremy B. (2016), ‘State space analysis of dominant structures in dynamic social systems’, Ph.D. dissertation, St. Louis, MO: Washington University in St. Louis.
     [Google Scholar]
  49. Schoenberg, William ([2019] 2020), ‘Feedback system neural networks for inferring causality in directed cyclic graphs’, arXiv preprint, 1908:10336, https://arxiv.org/abs/1908.10336. Accessed 24 March 2021 .
  50. Schoenberg, William , Davidsen, Pål and Eberlein, Robert (2020), ‘Understanding model behavior using the loops that matter method’, System Dynamics Review, 36:2, pp. 158–90.
     [Google Scholar]
  51. Schoenberg, William , Hayward, John and Eberlein, Robert (2023), ‘Improving loops that matter’, Systems Dynamics Review, 39:2, pp. 140–51.
     [Google Scholar]
  52. Schoenberg, William and Swartz, Jeremy (2022), ‘Building more robust system dynamics models through validation’, in International Conference of the System Dynamics Society 2021, vol. 2, Red Hook, NY: Curran Associates, Inc., pp. 923–39.
     [Google Scholar]
  53. Schölkopf, Bernhard and Smola, Alexander J. (2008), Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, Cambridge, MA: MIT Press.
     [Google Scholar]
  54. Senge, Peter M. ([1990] 2006), The Fifth Discipline: The Art & Practice of the Learning Organization, Revised and Updated Edition, New York: Doubleday.
     [Google Scholar]
  55. Spirtes, Peter and Kun Zhang (2016), ‘Causal discovery and inference: Concepts and recent methodological advances’, Applied Informatics, 3:1, p. 3.
     [Google Scholar]
  56. Sterman, John (2000), Business Dynamics: Systems Thinking and Modeling for a Complex World, New York: Irwin/McGraw-Hill.
     [Google Scholar]
  57. Stone, Peter, Brooks, Rodney, Brynjolfsson, Erik, Calo, Ryan, Etzioni, Oren, Hager, Greg, Hirschberg, Julia, Kalyanakrishnan, Shivaram, Kamar, Ece, Kraus, Sarit, Leyton-Brown, Kevin, Parkes, David, Press, William, Saxenian, AnnaLee, Shah, Julie, Tambe, Milind and Teller, Astro (2016), ‘Artificial intelligence and life in 2030: The one hundred year study on artificial intelligence’, Report of the 2015–2016 Study Panel, Stanford: Stanford University, September, https://ai100.stanford.edu/sites/g/files/sbiybj18871/files/media/file/ai100report10032016fnl_singles.pdf. Accessed 11 May 2023.
  58. Swartz, Jeremy , Wasko, Janet , Marvin, Carolyn , Logan, Robert K. and Coleman, Beth (2019), ‘Philosophy of technology: Who is in the saddle?’, Journalism & Mass Communication Quarterly, 96:2, pp. 351–66.
     [Google Scholar]
  59. Wiener, Norbert (1956), ‘The theory of prediction’, in E. F. Beckenbach (ed.), Modern Mathematics for the Engineer: First Series, New York: McGraw-Hill, pp. 165–90.
     [Google Scholar]

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