Reading List¶

Here are some papers that are essential or useful reading for members of our group. Everyone should read the papers in the General section, and the others are good starting points if the topic area is relevant or of interest.

General ¶

Keller, K., Helgeson, C., & Srikrishnan, V. (2021). Climate Risk Management. Annual Review of Earth and Planetary Sciences, 49(1), 95–116. https://doi.org/10.1146/annurev-earth-080320-055847
Bankes, S. (1993). Exploratory Modeling for Policy Analysis. Operations Research, 41(3), 435–449. https://doi.org/10.1287/opre.41.3.435
Saltelli, A. (2019). A short comment on statistical versus mathematical modelling. Nature Communications, 10(1), 3870. https://doi.org/10.1038/s41467-019-11865-8
O’Hagan, T. (2004). Dicing with the unknown. Significance, 1(3), 132–133. https://doi.org/10.1111/j.1740-9713.2004.00050.x
Box, G. E. P. (1976). Science and Statistics. Journal of the American Statistical Association, 71(356), 791–799. https://doi.org/10.1080/01621459.1976.10480949
Oreskes, N., Shrader-Frechette, K., & Belitz, K. (1994). Verification, validation, and confirmation of numerical models in the Earth sciences. Science, 263(5147), 641–646. https://doi.org/10.1126/science.263.5147.641

Complex Systems and Wicked Problems¶

Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169. https://doi.org/10.1007/BF01405730
Carpenter, S. R., Brock, W. A., Folke, C., van Nes, E. H., & Scheffer, M. (2015). Allowing variance may enlarge the safe operating space for exploited ecosystems. Proceedings of the National Academy of Sciences of the United States of America, 112(46), 14384–14389. https://doi.org/10.1073/pnas.1511804112
Anderies, J. M., Rodriguez, A. A., Janssen, M. A., & Cifdaloz, O. (2007). Panaceas, uncertainty, and the robust control framework in sustainability science. Proceedings of the National Academy of Sciences of the United States of America, 104(39), 15194–15199. https://doi.org/10.1073/pnas.0702655104

Statistics and Uncertainty¶

Schneider, S. H. (2002). Can we Estimate the Likelihood of Climatic Changes at 2100? Climatic Change, 52(4), 441–451. https://doi.org/10.1023/A:1014276210717
Shmueli, G. (2010). To Explain or to Predict? Statistical Science, 25(3), 289–310. https://doi.org/10.1214/10-STS330
Kennedy, M. C., & O’Hagan, A. (2001). Bayesian calibration of computer models. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 63(3), 425–464. https://doi.org/10.1111/1467-9868.00294
Hargreaves, J., & Annan, J. (2002). Assimilation of paleo-data in a simple Earth system model. Climate Dynamics, 19(5), 371–381. https://doi.org/10.1007/s00382-002-0241-0
Draper, D. (1995). Assessment and Propagation of Model Uncertainty. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 57(1), 45–97. http://www.jstor.org/stable/2346087
Gelman, A., & Shalizi, C. R. (2013). Philosophy and the practice of Bayesian statistics. The British Journal of Mathematical and Statistical Psychology, 66(1), 8–38. https://doi.org/10.1111/j.2044-8317.2011.02037.x
Brynjarsdóttir, J., & OʼHagan, A. (2014). Learning about physical parameters: the importance of model discrepancy. Inverse Problems, 30, 114007. https://doi.org/10.1088/0266-5611/30/11/114007

Modeling and Model Diagnostics¶

Helgeson, C., Srikrishnan, V., Keller, K., & Tuana, N. (2021). Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions. Philosophy of Science. https://doi.org/10.1086/711501
Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J., et al. (2013). Characterising performance of environmental models. Environmental Modelling & Software, 40, 1–20. https://doi.org/10.1016/j.envsoft.2012.09.011
Iwanaga, T., Wang, H.-H., Hamilton, S. H., Grimm, V., Koralewski, T. E., Salado, A., et al. (2021). Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach. Environmental Modelling and Software, 135, 104885. https://doi.org/10.1016/j.envsoft.2020.104885
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1
Quinn, J. D., Reed, P. M., & Keller, K. (2017). Direct policy search for robust multi-objective management of deeply uncertain socio-ecological tipping points. Environmental Modelling & Software, 92, 125–141. https://doi.org/10.1016/j.envsoft.2017.02.017
Quinn, J. D., Reed, P. M., Giuliani, M., & Castelletti, A. (2017). Rival framings: A framework for discovering how problem formulation uncertainties shape risk management trade-offs in water resources systems. Water Resources Research, 53(8), 7208–7233. https://doi.org/10.1002/2017WR020524

Decision-Making Under Uncertainty¶

Oddo, P. C., Lee, B. S., Garner, G. G., Srikrishnan, V., Reed, P. M., Forest, C. E., & Keller, K. (2020). Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management. Risk Analysis, 40(1), 153–168. https://doi.org/10.1111/risa.12888
Herman, J. D., Quinn, J. D., Steinschneider, S., Giuliani, M., & Fletcher, S. (2020). Climate Adaptation as a Control Problem: Review and Perspectives on Dynamic Water Resources Planning Under Uncertainty. Water Resources Research, 56(2), 35. https://doi.org/10.1029/2019WR025502
Walker, W. E., Haasnoot, M., & Kwakkel, J. H. (2013). Adapt or Perish: A Review of Planning Approaches for Adaptation under Deep Uncertainty. Sustainability: Science Practice and Policy, 5(3), 955–979. https://doi.org/10.3390/su5030955

Wong-Parodi, G. (2020). When climate change adaptation becomes a “looming threat” to society: Exploring views and responses to California wildfires and public safety power shutoffs. Energy Research & Social Science, 70, 101757. https://doi.org/10.1016/j.erss.2020.101757
Kunreuther, H., Novemsky, N., & Kahneman, D. (2001). Making Low Probabilities Useful. Journal of Risk and Uncertainty, 23(2), 103–120. https://doi.org/10.1023/A:1011111601406
Mayer, L. A., Loa, K., Cwik, B., Tuana, N., Keller, K., Gonnerman, C., et al. (2017). Understanding scientists’ computational modeling decisions about climate risk management strategies using values-informed mental models. Global Environmental Change: Human and Policy Dimensions, 42, 107–116. https://doi.org/10.1016/j.gloenvcha.2016.12.007
Bessette, D. L., Mayer, L. A., Cwik, B., Vezér, M., Keller, K., Lempert, R. J., & Tuana, N. (2017). Building a Values-Informed Mental Model for New Orleans Climate Risk Management. Risk Analysis, 37(10), 1993–2004. https://doi.org/10.1111/risa.12743
Bessette, D. L., Wilson, R. S., & Arvai, J. L. (2019). Do people disagree with themselves? Exploring the internal consistency of complex, unfamiliar, and risky decisions. Journal of Risk Research, 1–13. https://doi.org/10.1080/13669877.2019.1569107

Last update: June 26, 2023