Visioning local futures: agent-based modeling as a tourism planning support system
Bibliographic record
Abstract
Often operating within a complicated, poorly understood environment, the process of tourism planning can be a difficult and challenging task. Technological aides have often been applied to the problems of planning, with varying levels of success. One computer simulation method used to study complex human social systems, agent-based modeling (ABM), is an approach that is increasingly used as a way to explore planning-relevant problems. Despite this promise, little work has explored the use of ABM to represent tourism dynamics, or as a support for planning, as evaluated by planners themselves. This research addresses these gaps in three steps: 1) by developing the concept of tourism as a phenomenon that is fundamentally individual-based. 2) Formalizing this conceptual framework into an ABM of tourism dynamics set in the tourism-centric Canadian province of Nova Scotia. 3) Providing this ABM for evaluation by tourism planning professionals as a step to identify the specific planning tasks to which this model adds greatest value and individual, technical, and organizational constraints to adoption. The results of this research indicate that the use of an ABM-based planning support system (PSS) is strongest as a scenario development tool, providing an environment for formulating 'what if' style questions, data analysis, and a way to communicate results to community members and decision-makers. Despite these benefits, limitations to the ability of modelers to develop highly detailed and validated models emphasize the position of ABM as an emerging technology. Users also reported a lack of transparency in the model. This was attributed to design choices that were intended to make the ABM user-friendly, but also hid the functioning of the model from users. The application of ABM in planning is still a novelty, and this inhibits the confidence planners may have in using an ABM. This serves to illuminate a disconnect between the needs of the planner and what can be delivered with an ABM approach. Reducing this gap is a task that can be completed through further joint development efforts between planners and ABM modelers to more actively build on identified strengths of the ABM approach within planning.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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; both teacher heads agree on what is shown here.
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".