An Agent-Based Approach to Providing Tourism Planning Support
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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 it