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Improving scenario discovery by bagging random boxes

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  • Kwakkel, J.H.
  • Cunningham, S.C.

Abstract

Scenario discovery is a model-based approach to scenario development under deep uncertainty. Scenario discovery relies on the use of statistical machine learning algorithms. The most frequently used algorithm is the Patient Rule Induction Method (PRIM). This algorithm identifies regions in an uncertain model input space that are highly predictive of model outcomes that are of interest. To identify these regions, PRIM uses a hill-climbing optimization procedure. This suggests that PRIM can suffer from the usual defects of hill climbing optimization algorithms, including local optima, plateaus, and ridges and valleys. In case of PRIM, these problems are even more pronounced when dealing with heterogeneously typed data. Drawing inspiration from machine learning research on random forests, we present an improved version of PRIM. This improved version is based on the idea of performing multiple PRIM analyses based on randomly selected features and combining these results using a bagging technique. The efficacy of the approach is demonstrated using three cases. Each of the cases has been published before and used PRIM. We compare the results found using PRIM with the results found using the improved version of PRIM. We find that the improved version is more robust to new data, can better cope with heterogeneously typed data, and is less prone to overfitting.

Suggested Citation

  • Kwakkel, J.H. & Cunningham, S.C., 2016. "Improving scenario discovery by bagging random boxes," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 124-134.
    • Handle: RePEc:eee:tefoso:v:111:y:2016:i:c:p:124-134
    DOI: 10.1016/j.techfore.2016.06.014
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    References listed on IDEAS

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    1. Warren E. Walker & Marjolijn Haasnoot & Jan H. Kwakkel, 2013. "Adapt or Perish: A Review of Planning Approaches for Adaptation under Deep Uncertainty," Sustainability, MDPI, vol. 5(3), pages 1-25, March.
    2. Julie Rozenberg & Céline Guivarch & Robert Lempert & Stéphane Hallegatte, 2014. "Building SSPs for climate policy analysis: a scenario elicitation methodology to map the space of possible future challenges to mitigation and adaptation," Climatic Change, Springer, vol. 122(3), pages 509-522, February.
    3. Céline Guivarch & Julie Rozenberg & Vanessa Schweizer, 2016. "The diversity of socio-economic pathways and CO2 emissions scenarios: Insights from the investigation of a scenarios database," Post-Print halshs-01292901, HAL.
    4. David C. Lane & Özge Pala & Yaman Barlas & Willem L. Auping & Erik Pruyt & Jan H. Kwakkel, 2015. "Societal Ageing in the Netherlands: A Robust System Dynamics Approach," Systems Research and Behavioral Science, Wiley Blackwell, vol. 32(4), pages 485-501, July.
    5. Robert J. Lempert & David G. Groves & Steven W. Popper & Steve C. Bankes, 2006. "A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios," Management Science, INFORMS, vol. 52(4), pages 514-528, April.
    6. Kwakkel, Jan H. & Auping, Willem L. & Pruyt, Erik, 2013. "Dynamic scenario discovery under deep uncertainty: The future of copper," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 789-800.
    7. Eker, Sibel & van Daalen, Els, 2015. "A model-based analysis of biomethane production in the Netherlands and the effectiveness of the subsidization policy under uncertainty," Energy Policy, Elsevier, vol. 82(C), pages 178-196.
    8. Erik Pruyt & Jan H. Kwakkel, 2014. "Radicalization under deep uncertainty: a multi-model exploration of activism, extremism, and terrorism," System Dynamics Review, System Dynamics Society, vol. 30(1-2), pages 1-28, January.
    9. Evelina Trutnevyte & Céline Guivarch & Robert Lempert & Neil Strachan, 2016. "Reinvigorating the scenario technique to expand uncertainty consideration," Climatic Change, Springer, vol. 135(3), pages 373-379, April.
    10. Evgenii Matrosov & Silvia Padula & Julien Harou, 2013. "Selecting Portfolios of Water Supply and Demand Management Strategies Under Uncertainty—Contrasting Economic Optimisation and ‘Robust Decision Making’ Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(4), pages 1123-1148, March.
    11. Jan Kwakkel & Marjolijn Haasnoot & Warren Walker, 2015. "Developing dynamic adaptive policy pathways: a computer-assisted approach for developing adaptive strategies for a deeply uncertain world," Climatic Change, Springer, vol. 132(3), pages 373-386, October.
    12. Parker, Andrew M. & Srinivasan, Sinduja V. & Lempert, Robert J. & Berry, Sandra H., 2015. "Evaluating simulation-derived scenarios for effective decision support," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 64-77.
    13. Hamarat, Caner & Kwakkel, Jan H. & Pruyt, Erik, 2013. "Adaptive Robust Design under deep uncertainty," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 408-418.
    14. Sebastiaan Greeven & Oscar Kraan & Emile Chappin & Jan H. Kwakkel, 2016. "The Emergence of Climate Change Mitigation Action by Society: An Agent-Based Scenario Discovery Study," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(3), pages 1-9.
    15. Stephane Hallegatte & Mook Bangalore & Laura Bonzanigo & Marianne Fay & Tamaro Kane & Ulf Narloch & Julie Rozenberg & David Treguer & Adrien Vogt-Schilb, 2016. "Shock Waves," World Bank Publications - Books, The World Bank Group, number 22787, December.
    16. Hallegatte, Stephane & Shah, Ankur & Lempert, Robert & Brown, Casey & Gill, Stuart, 2012. "Investment decision making under deep uncertainty -- application to climate change," Policy Research Working Paper Series 6193, The World Bank.
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    Cited by:

    1. Steinmann, Patrick & Auping, Willem L. & Kwakkel, Jan H., 2020. "Behavior-based scenario discovery using time series clustering," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    2. Li, Jing-Ping & Mirza, Nawazish & Rahat, Birjees & Xiong, Deping, 2020. "Machine learning and credit ratings prediction in the age of fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    3. Eachempati, Prajwal & Srivastava, Praveen Ranjan & Kumar, Ajay & Tan, Kim Hua & Gupta, Shivam, 2021. "Validating the impact of accounting disclosures on stock market: A deep neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    4. Ola G. El‐Taliawi & Nihit Goyal & Michael Howlett, 2021. "Holding out the promise of Lasswell's dream: Big data analytics in public policy research and teaching," Review of Policy Research, Policy Studies Organization, vol. 38(6), pages 640-660, November.
    5. Jan H. Kwakkel, 2019. "A generalized many‐objective optimization approach for scenario discovery," Futures & Foresight Science, John Wiley & Sons, vol. 1(2), June.

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