An Agent-Based Approach to Providing Tourism Planning Support
Bibliographic record
Abstract
Agent-based modeling (ABM) is a computer simulation approach that can be used to represent real-world systems and create planning scenarios to examine possible future outcomes of present-day decisions. This approach can be applied in tourism planning, where destinations are exposed to a variety of externalities, and must develop strategies to adapt to changing operational conditions. We describe the development of TourSim, an ABM of tourism dynamics set in the Canadian province of Nova Scotia. We present an overview of the data sources and techniques used to inform agent behavior and the destination landscape, as well as consider aspects of system representation and validation and how these may affect the use of TourSim. TourSim is used to generate three scenarios of tourism dynamics; a base-case scenario, one that simulates the effect of a decrease in visitation from American markets as a result of economic crisis, and the use of advertising as a response to this lower level of visitation. These scenarios are used to evaluate ABM in comparison with other computer-based methods of modeling tourism, namely geographic information systems and system dynamics models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".