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.
This post puts together some informal thoughts on how to get the most out of an academic paper. I’m grateful to discussions with Pat Reed, Thorsten Wagener, and Klaus Keller through the years that have given me some of these ideas.
How to Find Good Papers
- Use Web of Knowledge and/or Google Scholar to search for the most relevant citations. You can even start with a general topic, such as “water supply planning”. There will be 1,000s of citations of course. But “sort by times cited” and you will likely find the most important benchmark papers everyone has read. Download these (at least) and read them (preferably). You’ll be expected to know these references!
- Make good use of review articles. Did you know that Nicklow et al. (2010) reviewed applications of evolutionary algorithms in water resources? (find it here: http://link.aip.org/link/doi/10.1061/(ASCE)WR.1943-5452.0000053) Review articles like this are great resources for learning a lot about a field. There are similar reviews for hydro-economic modeling (Harou et al., http://dx.doi.org/10.1016/j.jhydrol.2009.06.037) and multi-reservoir operations (Labadie, http://dx.doi.org/10.1061/(ASCE)0733-9496(2004)130:2(93)). I’m sure there are good examples for your field too.
- Look at group websites to collect more than one paper from the same author. Our group website is a great example of course.
- Use literature reviews in other papers and theses. A lot of times, other papers, dissertations, and theses do some of the work for you by reviewing the literature in a particular field. Use these resources and download the papers cited by these other authors. Of course, do not plagiarize their words. If you’re borrowing ideas from a list of literature from Smith (2012), you can even cite Smith (2012) by saying “As reviewed by Smith (2012)…”
How to Read a Paper
You’ve found some good papers to read. So you get yourself a cup of tea, print out a paper, and start out at page 1. That’s not really the best way to go about reading the paper! What if this paper isn’t one that you actually need to read? Let’s face it, you will probably have to cite 100 papers in your thesis and it is difficult to read every single one, especially in one sitting. What if the important info doesn’t start until page 15? The human attention span is not very long, and you could get yourself lost.
Instead, try this approach:
- Read the abstract. A good abstract will tell you what the paper aims to achieve, what methods the authors used to achieve those aims, and the implications of the results. A great abstract will also discuss the limitations of prior work in the field, and how the presented work could be expanded to other studies or other fields.
- Does the abstract seem relevant and interesting? Great. Now Look at the figures. What types of analysis are the authors presenting? Do the figures make sense, and do the captions explain what you’re supposed to look for? When you’re reading the full text later, you’ll want to use the figures as a roadmap. It’s helpful to know what’s coming so that you’ve seen it before you get there.
- Is the paper still keeping your interest? Wonderful. Time to read the conclusion. The conclusion should give the authors’ insight on what it is that they actually did. This should give you the take-home message that you should, well, take home when you read the work.
- Now you can Start at the beginning and read the paper. Pay particular attention to the methodology — if the paper talks about a basin in Malaysia, it probably uses a model or analysis technique that you could apply to your own basin. It’s not a good enough excuse to say “Oh, well the authors aren’t working on a problem that’s exactly like mine.” You should try to be familiar with papers that are from all sorts of different fields.
Remember that you can get a lot out of the first few steps of the process. So if you look at the abstract, the figures, and introduction, you may get enough out of it to save the paper for a more careful treatment later. It’s better to be familiar with a whole lot of references from many different authors and groups, in my opinion, than get tunnel vision on one paper. Especially since you will get more out of a paper if you revisit it later after you’ve learned more about the field.
Some Tasks to Try
A lot of people need to “do” something when reading to make sure they get the jist of the paper effectively. Here are some suggestions:
- Highlighting. This is pretty self explanatory, but try the features in Adobe Reader or the free FoxIt reader (see http://www.foxitsoftware.com/) Also question things that you don’t understand or don’t agree with in the margins of the paper (i.e., “What were they thinking?”). This really helps when you revisit the paper later.
- Write a one-sentence summary. This is harder that it would seem at first. How do you distill a 20 page paper down to a single sentence? This is a good habit to get into for every paper you read, especially since you will probably need to do it when you’re writing the literature review in your thesis. Most papers will put a sentence like this right in the abstract, so adapt it from there.
- Write a 500 word summary. Again, it’s harder than it initially seems. This page gives some helpful hints on writing summaries. Always do this without plagiarizing the original material. Writing a succinct summary of something can be a valuable skill, especially when adapting your own work in different venues.
Download Reed and Minsker (2004) “Striking the Balance: Long-Term Groundwater Monitoring Design for Conflicting Objectives” here: http://link.aip.org/link/doi/10.1061/(ASCE)0733-9496(2004)130:2(140). It’s a foundational paper for our field, since it’s one of the first applications of a many-objective (4 or more) optimization problem in water. Fulfill the following tasks:
- Write a one-sentence summary of the paper.
- Write a 500-word summary of the paper, making sure you hit the most important results presented there.
- Provide a brief critique, including one thing the paper did well and one thing it did poorly or you want to see expanded.
- List the most important 3 references cited in the paper and discuss their relevance to the study. Does the current study expand or improve on these references?
As always feel free to add comments or questions below!