An iterative process to construct an interdisciplinary ABM using MR POTATOHEAD: An application to Housing Market Models in touristic areas
Bibliographic record
Abstract
This work demonstrates an interdisciplinary modeling process between economists and computer scientists to formulate an Agent-Based Model of Land Use and Cover Change (ABM/LUCC). The MR POTATOHEAD (Model Representing Potential Objects That Appear in The Ontology of Human - Environmental Actions & Decisions) generic modeling methodology for ABM/LUCC process building is used to construct the interdisciplinary ABM. In our paper, this methodology is applied to an ABM/LUCC describing a collection of tourist areas that have faced an intense development leading to huge pressure on land prices as well as to land-use conflicts (local residential market, tourist rental investments, agricultural production). The process moves from the conceptual template description, to conceptual model, to its computer model counterpart. The NetLogo Design Pattern is used to efficiently integrate the spatial aspects of the ABM. The paper highlights benefits offered by this software technique of engineering to design modular, extensible and scalable ABM/LUCC computer models. We present preliminary work aiming to better model the dynamical complex system of ABM/LUCC. Preliminary results show that the MR POTATOHEAD modeling process facilitates both interdisciplinary dialogs and computer model formulation. This conceptual template approach facilitates interdisciplinary communication and interaction between economists and computer scientists.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".