By Patrick M. Reed
In general, I’ve made an effort to keep this blog focused on the technical topics that have helped my students tackle various issues big and small. It helps with collaborative learning and maintaining our exploration of new ideas.
This post, however, represents a bit of departure from our normal posts in response to some requests for a suggested reading guide for my Fall 2019 AGU Paul A. Witherspoon Lecture entitled “Conflict, Coordination, and Control in Water Resources Systems Confronting Change” (on Youtube should you have interest).
The intent is to take a step back and zoom out a bit to get the bigger picture behind what we’re doing as research group and much of the original motivation in initiating the Water Programming Blog itself. So below, I’ll provide a summary of papers that related to the topics covered in the lecture sequenced by the talk’s focal points at various points in time. Hope this provides some interesting reading for folks. My intent here is to keep this informal. The highlighted reading resources were helpful to me and are not meant to be a formal review of any form.
So let’s first highlight Paul Witherspoon himself, a truly exceptional scientist and leader (7 minute marker, slides 2-3).
- His biographical profile
- A summary of his legacy
- The LBL Memo Creating the Earth Sciences Division (an example of institutional change)
Next stop, do we understand coordination, control, and conflicting objectives in our institutionally complex river basins (10 minute marker, slides 6-8)? Some examples and a complex systems perspective.
- The NY Times Bomb Cyclone Example
- Interactive ProPublica and The Texas Tribune Interactive Boomtown, Flood Town (note this was written before Hurricane Harvey hit Houston)
- A Perspective on Interactions, Multiple Stressors, and Complex Systems (NCA4)
Does your scientific workflow define the scope of your hypotheses? Or do your hypotheses define how you need to advance your workflow (13 minute marker, slide 9)? How should we collaborate and network in our science?
- Dewey, J. (1958), Experience and Nature, Courier Corporation.
- Dewey, J. (1929), The quest for certainty: A study of the relation of knowledge and action.
- Hand, E. (2010), ‘Big science’ spurs collaborative trend: complicated projects mean that science is becoming ever more globalized–and Europe is leading the way, Nature, 463(7279), 282-283.
- Merali, Z. (2010), Error: Why Scientific Programming Does Not Compute, Nature, 467(October 14), 775-777.
- Cummings, J., and S. Kiesler (2007), Coordination costs and project outcomes in multi-university collaborations, Research Policy, 36, 1620-1634.
- National Research Council (2014), Convergence: facilitating transdisciplinary integration of life sciences, physical sciences, engineering, and beyond, National Academies Press.
- Wilkinson, M. D., M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J.-W. Boiten, L. B. da Silva Santos, and P. E. Bourne (2016), The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3.
- Cash, D. W., W. C. Clark, F. Alcock, N. M. Dickson, N. Eckley, D. H. Guston, J. Jäger, and R. B. Mitchell (2003), Knowledge systems for sustainable development, Proceedings of the National Academy of Sciences, 100(14), 8086-8091.
Perspectives and background on Artificial Intelligence (15 minute marker, slides 10-16)
- Simon, H. A. (2019), The sciences of the artificial, MIT press.
- AI Knowledge Map: How To Classify AI Technologies
- Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing Company, Reading, MA
- Coello Coello, C., G. B. Lamont, and D. A. Van Veldhuizen (2007), Evolutionary Algorithms for Solving Multi-Objective Problems, 2 ed., Springer, New York, NY.
- Hadka, D. M. (2013), Robust, Adaptable Many-Objective Optimization: The Foundations, Parallelization and Application of the Borg MOEA.
The Wicked Problems Debate (~22 minute marker, slides 17-19) and the emergence of post normal science and decision making under deep uncertainty.
- Rittel, H., and M. Webber (1973), Dilemmas in a General Theory of Planning, Policy Sciences, 4, 155-169.
- Buchanan, R. (1992), Wicked problems in design thinking, Design Issues, 8(2), 5-21.
- Kwakkel, J. H., W. E. Walker, and M. Haasnoot (2016), Coping with the Wickedness of Public Policy Problems: Approaches for Decision Making under Deep Uncertainty, Journal of Water Resources Planning and Management, 01816001.
- Ravetz, J. R., and S. Funtowicz (1993), Science for the post-normal age, Futures, 25(7), 735-755.
- Turnpenny, J., M. Jones, and I. Lorenzoni (2011), Where now for post-normal science?: a critical review of its development, definitions, and uses, Science, Technology, & Human Values, 36(3), 287-306.
- Marchau, V. A., W. E. Walker, P. J. Bloemen, and S. W. Popper (2019), Decision making under deep uncertainty, Springer.
- Mitchell, M. (2009), Complexity: A guided tour, Oxford University Press.
Lastly, the Vietnam and North Carolina application examples.
- Quinn, J. D., P. M. Reed, M. Giuliani, and A. Castelletti (2019), What Is Controlling Our Control Rules? Opening the Black Box of Multireservoir Operating Policies Using Time-Varying Sensitivity Analysis, Water Resources Research, 55(7), 5962-5984.
- Quinn, J. D., P. M. Reed, M. Giuliani, A. Castelletti, J. W. Oyler, and R. E. Nicholas (2018), Exploring How Changing Monsoonal Dynamics and Human Pressures Challenge Multireservoir Management for Flood Protection, Hydropower Production, and Agricultural Water Supply, Water Resources Research, 54(7), 4638-4662.
- Trindade, B. C., P. M. Reed, and G. W. Characklis (2019), Deeply uncertain pathways: Integrated multi-city regional water supply infrastructure investment and portfolio management, Advances in Water Resources, 134, 103442.
- Gold, D., Reed, P. M., Trindade, B., and Characklis, G., “Identifying Actionable Compromises: Navigating Multi-City Robustness Conflicts to Discover Cooperative Safe Operating Spaces for Regional Water Supply Portfolios.“, Water Resources Research, v55, no. 11, DOI:10.1029/2019WR025462, 9024-9050, 2019.