Negotiating Constraints to the Adoption of Agent-Based Modeling in Tourism Planning
Bibliographic record
Abstract
Recent work exploring the use of agent-based models (ABMs) in a planning support role must be accompanied by an evaluation of the possible constraints that exist to the use of these models. This research presents an evaluation, from the perspective of professional tourism planners, of the potential for ABM of tourism dynamics to serve as a planning support system (PSS). Tourism is a phenomenon that is inherently individually based, with many interacting processes occurring at multiple scales, across space and time. This makes it a natural environment in which to test an ABM-based PSS. We conducted a series of interviews with tourism planners operating in the Canadian province of Nova Scotia, a region where tourism plays an important economic role. These interviews consisted of a general-needs overview, coupled with an assessment of a prototype model we developed, called TourSim. The interviews sought to uncover the specific planning tasks to which ABM would be best applied and identify areas of adoption constraint. The results of this research indicate that TourSim served as a scenario development tool, with a focus on data analysis and communication. Conversely, TourSim was reported to lack transparency, which affected the confidence that planners had in its results. This evaluation clarifies the path forward for developers looking to introduce ABM to planning practice.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".