Employing the Characterization and Parameterization framework (CAP) to develop an empirical innovation-diffusion agent-based model of green infrastructure adoption on private residential yards.
Bibliographic record
Abstract
Incorporating social-science theories to represent human decision-making in data-driven models is required, particularly when modeling the underlying social determinants of adopting pro-environmental innovations. Our agent-based model (ABM) investigates households' adoption of stormwater green infrastructure (GI) - e.g., rain gardens - on private residential yards in Kitchener/Waterloo, Ontario. We propose combining Rogers's diffusion of innovation theory (DOI) (2003) with Smajgl Barreteau (2017) characterization and parameterization framework (CAP) to develop an empirical ABM, DRAIIN. At the micro-level, we use a municipal survey (Aquafor Beech Ltd., Freeman Associates 2015) to parameterize agents' attributes (e.g., ownership status) and behavioral data inputs (e.g., yard maintenance willingness). Additionally, a local survey (Defields, 2013) feeds into a multinomial logit model to calibrate agents' adoption decisions. Since the sample data is representative of the case study population, we apply cloning to upscale the sampled agents proportionally. Consistent with comparable case studies (Nassauer, Wang, Dayrell, 2009), both the municipal and our local surveys suggest that peer influences primarily influence households' landscape decisions. Similar market dynamics are observed in the solar panels market (Bollinger, Gillingham, 2012; Rode Weber, 2016). Thus, we postulate that DRAIIN follows the generalized S-curve for innovation diffusion that was reported in solar panels' cumulative market growth (Islam, 2014; Palmer, Sorda, Madlener, 2015). At the macro-level, we seek qualitative validation by examining whether DRAIIN generates the diffusion S-curve (see Rand Rust, 2011). Following the CAP framework, we incorporate expert knowledge from city representatives, stormwater consultants, and a local not-for-profit to co-validate DRAIIN's structure and outputs. Integrating the DOI and the CAP framework is a context-independent approach that is potentially applicable to other cases of innovation-diffusion ABMs beyond DRAIIN. Further, we suggest that incorporating empirical data into Rogers's innovation-decision stages should enhance the predictive power of innovation-diffusion models, which inherently lack post-diffusion validation data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".