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The Model and the River: infrastructure and deliberation in California water policy

The Model and the River: infrastructure and deliberation in California water policy. Arizona State University October 28, 2005 Steven Jackson Assistant Professor and Research Investigator School of Information University of Michigan http://www.si.umich.edu. Overview.

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The Model and the River: infrastructure and deliberation in California water policy

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  1. The Model and the River: infrastructure and deliberation in California water policy Arizona State University October 28, 2005 Steven Jackson Assistant Professor and Research Investigator School of Information University of Michigan http://www.si.umich.edu

  2. Overview • Modeling, prediction, and water management in California (CALSIM) Some themes… • Numbers and democracy (Porter, Ezrahi, Rose) • Archives and government (Derrida, professional archives lit) • Deliberative infrastructure / CSDW

  3. …and fields of application. Environmental (or geo-)informatics: “the assemblage of visioning, processing, and archiving techniques built around the network resources of post-war computing, dedicated to the production and refinement of ‘earthly knowledges’” (computer models, GIS, satellite / remote sensing, data management, cyberinfrastructure and ‘E-science’ initiatives, etc.)

  4. Conceptual Map – Environmental Informatics Policy Science Society

  5. California water: a primer 20th-century history of massive systems development (urban extensions, Hoover Dam, the CVP, the SWP), from ‘total development’ to ‘public trust’ …renovations in the field of knowledge and representation… …assembling the river.

  6. Conceptual and numeric models… V = (1.49R2/3s1/2)/n

  7. Physical models(‘scale analogs’)… (Berkeley Hydraulics Lab, ca. 1942)

  8. Electric models(‘process’ analogs)… (Berkeley, ca. 1950s)

  9. Some (social) epistemologies of modeling… Epistemic status: Theory? Experiment? Description? Story? The validity of ‘analogical reasoning’? Modeler’s regress and the problem of open-world modeling? The character of witnessing? “Heuristic devices” or “predictive truth machines”? (Shackley and Wynne, 1996)

  10. Simulation in water management 1950s/70s – • First introductions (though following on numeric traditions dating to early 20th-century); built around mass balance equations (i.e. simple stock and flow models); technical form: hard-wired and spaghetti coding (non-transparent) 1980s/90s – • Coalescence around agency in-house models: DWRSIM and PROSIM • Models in the public sphere: new EI requirements, tightening system constraints, increasingly restive and technically engaged ‘lay experts,’ inter-agency conflict

  11. CALSIM – a fragile peace? The ‘consensus model’ – a joint DWR/BR effort Some technical details: • FORTRAN- and JAVA-based, through interface of WRESL (Water Resource and Environmental Simulation Language) • Object-oriented, structured, and soft-wired (‘data driven’) programming principles • Open source, freely downloadable*, and (in principle) transportable and transparent • User interface: spreadsheet and file processing inputs (pre-processors), spreadsheet and graphing output functions (post-processors) • Monthly timesteps, solving iteratively according to system of user-specified objectives and constraints • NB: NOT visually rich

  12. CALSIM II Operation (simplified) Model Inputs Simulation / Optimization(single (monthly) time-step) Model Outputs State variables(input or pre-processed) Data interfaces(spreadsheet / file management) Mixed Integer LinearProgramming (MIP) Solver… State variables(raw or post-processed) Objectives(weights andpriorities) Constraints(hard, soft, or conditional) Othermodels(DSM2, IGSM2, CALAG) (static and/ordynamic (ANN)) Simulation reports and diagnostics …iterative across multiplesimulation cycles. WRESL(natural language interface) Operating policies and procedures (regulatory, con- tractual, etc.)

  13. CALSIM – a fragile peace? The ‘consensus model’ – a joint DWR/BR effort Some technical details: • FORTRAN- and JAVA-based, through interface of WRESL (Water Resource and Environmental Simulation Language) • Object-oriented, structured, and soft-wired (‘data driven’) programming principles • Open source, freely downloadable*, and (in principle) transportable and transparent • User interface: spreadsheet and file processing inputs (pre-processors), spreadsheet and graphing output functions (post-processors) • Monthly timesteps, solving iteratively according to system of user-specified objectives and constraints Science and the escape from politics?

  14. Controversy and deliberation (I): The 2002 SWP Reliability Report • CALSIM projection of the reliability of future SWP deliveries to contractors – quasi-legislative effects (Kuehl Bill) • Produced numbers widely held to be above all reasonable estimate – median delivery rates 50% higher than historically observed deliveries • Immediate public backlash – conservationists, but also legislators, water contractors, media, etc.

  15. Controversy and deliberation (II): 200? California Water Plan Update • Updated once every five years since 1957, the blueprint for statewide water planning / development • Projections derived largely from CALSIM and subsidiary models (agricultural, delta, climate, groundwater, demographic, economic, etc.) • Public advisory committee rejects the adequacy of CALSIM, advocates a phased development approach and new tool development

  16. Modeling as social technology… R1: I think the model should be built as if it’s going to court. Because if it’s not already there, it soon will be. - Interview transcript 9, p 11. R1: The problem is, there aren’t enough staff at DWR or the Bureau to do all the runs to study all the things people would like to see studied. So their needs get met first, and it’s harder to get environmentally-oriented modeling conducted. So then it’s really a matter of understanding the logistics, the weaknesses of the model. When the enviros come after CALSIM, it’s because they don’t like the results. You go after the model to undermine the conclusion it comes to. - Interview transcript 23, p 3.

  17. Modeling as social technology (cont’d) R1: The problem is, when people critique a model, they’re actually critiquing all things around a model. And because people don’t speak the same language, they talk past each other. So it’s hard to discern: what is their problem? - Interview transcript 31, p 9. R2: Generally speaking, people always have more trust in a model that tells them what they already think. R3: And a model they understand. R2: And a model they understand. Or a model that they feel is understood and trusted by people they trust, that's I think the biggest thing. Policy people trust technical results that come out of people they trust. Because they're never going to know that model, even if it's the simplest spreadsheet. INT: So as long as Joe at the Bureau who they know and trust says this is fine, we agree with the results, then that's enough? R2: Yes. And everybody has a different Joe, and a different organization. - Interview transcript 4a, p 5.

  18. Problem? What’s the problem? (Technocratically-speaking): A politically dangerous crisis of faith – Placing future development in jeopardy? Unraveling the peace-through-science settlement of the 1990s? Delegitimation of modeling expertise? (Deliberatively-speaking): An opportunity for constructive public engagement – Building broader and more realistic expectations of models? Putting modeling on a sounder (more sustainable) long-term footing? Engaging publics in the broader problematic(s) of California water?

  19. Observations and Conclusions Ontological sprawl:the hybrid ecologies of models (natural, technical, institutional, social) Models thrive within worlds made safe for computing:the fallacy of ‘computation in the wild’ The political language(s) of numbers (and pictures!):the power of thin description Deliberative geo-informatics: the democratic affordances of infrastructure; computer supported deliberative work

  20. Future research and collaborations… • U of Michigan School of Information (Comp Sci, Psych, Econ, Comm, History, Library Sci, STS) – human-computer interaction, computer-supported cooperative work, info analysis and retrieval, network analysis, natural language processing, archives and records management, social / community informatics • Information, policy, and governance – democratic implications, applications, and governance of advanced (or not) computational infrastructure

  21. Thank you – comments, questions, ideas, and suggestions all welcome… Steven Jackson Assistant Professor / Research Investigator School of Information, University of Michigan 301D West Hall, 1085 South University Avenue Ann Arbor, MI 48109-1107 Tel: 734-764-8058 Fax: 734-764-2475 sjackso@umich.edu http://www.si.umich.edu

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