A Technology-Enabled Tiered Accommodation System with Crisis Ambassador Support

Running Monte Carlo Cost-Benefit Analysis in Google Colab Overview This notebook compares five accommodation system designs for theme parks, from labor-intensive professional ambassadors to technology-enabled crisis support. The simulation runs 10,000 iterations to assess economic viability under realistic uncertainty. Quick Start Open Google Colab: Go to https://colab.research.google.com Create new notebook: File → New notebook Copy and paste the code from the provided .ipynb file into a code cell Run: Click the play button or press Shift+Enter What the Code Does Demand Modeling Tier 3 (high support needs): 66-165 guests daily (triangular distribution) Tier 2 (moderate support): 2× Tier 3 Fraud rate: Beta(2,8) distribution, mean ~20% Market expansion: Beta(1.5,6), negatively correlated with fraud (ρ=-0.4) Uses Gaussian copula to model realistic correlation: if fraud is high, legitimate new customers are deterred. Five Scenarios Tested Original (Baseline): Professional ambassadors at 1:8-10 (Tier 3) and 1:3-5 (Tier 2) ratios. Full-time staff with benefits, training, overhead. Scenario 1 - Peer Support: Parent volunteers ($100/day stipend) supervised by professional coordinators (1:10 ratio). Lower training costs, higher family ratios. Scenario 2 - Tech-First: Mobile app handles 80% of navigation/scheduling. Ambassadors only for crisis intervention (1:50 families). $500K development amortized over 5 years, $100K annual maintenance. Scenario 3 - Premium Paid: Tier 3 charges $200/day per family (60% participation rate). Professional staffing but revenue-funded. Tier 1/2 remain free for ADA compliance. Scenario 4 - Capacity-Limited Pilot: Cap Tier 3 at 20 slots daily. Reduces cost exposure but also reduces litigation benefit (×0.7) and market expansion (×0.5). Benefits Modeled Litigation risk reduction: Lognormal($1.2M, σ=0.8) — fat-tailed to capture rare expensive lawsuits Market expansion revenue: New families × $5K avg spending per trip Premium revenue (Scenario 3 only): Tier 3 users × $200/day × 3-day visits Output Metrics P(Net > 0): Probability system is profitable Mean/Median Net Benefit: Expected value and typical outcome VaR(5%): Worst-case 5th percentile (downside risk) Cost statistics: Mean and 95th percentile to assess budget risk Reading the Results The code prints a comparison table showing all five scenarios side-by-side, then detailed statistics for the best-performing option. Key insight from your run: Tech-First has 99.3% success probability and $1.8M mean annual benefit. Original baseline has only 3.4% success probability and loses $4.9M annually on average. Modifying Parameters To test different assumptions, edit these values: python # Demand tier3_daily = np.random.triangular(left=66, mode=115, right=165, size=n_sims) # Costs amb_salary = truncnorm(mean=45_000, sd=5_000, low=30_000, high=65_000, size=n_sims) # Benefits litigation_benefit = np.random.lognormal(mean=np.log(1.2e6), sigma=0.8, size=n_sims) Technical Requirements Pre-installed in Colab: NumPy, Pandas, SciPyNo authentication needed: Works in free Colab tierRuntime: ~30 seconds for 10,000 simulations Exporting Results Add this at the end to download CSV: python comparison.to_csv("scenario_comparison.csv") from google.colab import files files.download("scenario_comparison.csv") Interpretation for Your Paper This analysis demonstrates that only technology-enabled accommodation systems achieve economic viability at theme park scale. Labor-intensive models lose millions annually regardless of configuration. The paper should pivot from "professional ambassador services" to "technology-first design with crisis support backup." The Monte Carlo approach strengthens your argument by showing robustness under parameter uncertainty rather than point estimates that could be questioned.