Modeling the transfer of land and water from agricultural to urban uses in the Middle Rio Grande Basin, New Mexico.

Jarratt, Janet; Passell, Howard David; Kelly, Susan; Malczynski, Leonard A.; Chermak, Janie; Waanders, P; McNamara, Laura A.; Tidwell, Vincent Carroll; Pallachula, Kiran; Turnley, Jessica Glicken; Kobos, Peter Holmes; Newman, Gretchen Carr

doi:10.2172/920125

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…This reflected various levels of debate among disciplines represented on the modeling teams. As the final report from one project noted, "The quantitative scientists were focused to the end on making the model work as a measure of project success" (McNamara et al, 2004) and employing economic data was simpler than dealing with the messy qualitative data that the social scientists were considering. In other projects some team members resisted using any qualitative data or even accepting any non-economic expertise or data.…”

Section: Modeling the Socialmentioning

confidence: 99%

Reflections on SES modeling: Stop me if you’ve heard this

Cockerill

2024

SESMO

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Key lessons about and limits to social-ecological systems (SES) modeling are widely available and frustratingly consistent over time. Prominent challenges include outdated perspectives about systems and models along with persistent disciplinary hegemony. The inherent complexity in SES means that an emphasis on discrete prediction is misplaced and has potentially reduced model efficacy for decision-making. Although computer models are definitely the tool to use to identify the complex relationships within SES, humans are messy and hence the ‘social’ in SES is often ignored, glossed over, or reduced to simplistic economic or demographic variables. This combination of factors has perpetuated biases in what is worth pursuing and/or publishing. In (re)visiting issues in SES modeling, including debates about model capabilities, data selection, and challenges in working across disciplinary lines, this reflection explores how the author’s experience aligns with extant literature as well as raises issues about what is absent from that body of work. The available lessons suggest that scholars and practitioners need to re-think how, why, and when to employ SES modeling in regulatory or other decision-making contexts.

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Section: Modeling the Socialmentioning

confidence: 99%

Reflections on SES modeling: Stop me if you’ve heard this

Cockerill

2024

SESMO

View full text Add to dashboard Cite

show abstract

Modeling the transfer of land and water from agricultural to urban uses in the Middle Rio Grande Basin, New Mexico.

Cited by 1 publication

References 13 publications

Reflections on SES modeling: Stop me if you’ve heard this

Reflections on SES modeling: Stop me if you’ve heard this

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