A Technology-Enabled Tiered Accommodation System with Crisis Ambassador Support

Running Monte Carlo Cost-Benefit Analysis in Google Colab Overview This notebook compares six accommodation system designs for theme parks, evaluating economic viability through 10,000 Monte Carlo simulation iterations under realistic parameter uncertainty. Quick Start Open Google Colab: Navigate to https://colab.research.google.com Create new notebook: File → New notebook Copy and paste the code 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 volume Fraud rate: Beta(2,8) distribution, mean ~20% for voluntary systems Market expansion: Beta(1.5,6), negatively correlated with fraud (ρ=-0.4) The Gaussian copula models realistic correlation: high fraud deters legitimate new customers. Six Scenarios Tested Baseline Professional: Professional ambassadors at 1:8-10 (Tier 3) and 1:3-5 (Tier 2) ratios. Full-time staff with benefits, training ($8K-15K), overhead (1.15-1.35×). Peer Volunteer Support: Parent volunteers ($100/day stipend) supervised by professional coordinators (1:10 ratio). Reduced training costs ($500-1K), higher family ratios (1:12 Tier 3, 1:6 Tier 2). Technology-First: Mobile app handles 80% of navigation/sensory management/scheduling. Crisis ambassadors only (1:50 families). $500K development amortized over 5 years, $100K annual maintenance. Premium Paid Service: Tier 3 charges $200/day per family (60% participation rate). Professional staffing but revenue-funded. Tier 1/2 remain free for ADA compliance. Capacity-Limited Pilot: Cap Tier 3 at 20 daily slots. Reduces cost exposure but also reduces litigation benefit (×0.7) and market expansion (×0.5). IBCCES-Verified Tier 3: Third-party disability verification through IBCCES Accessibility Card reduces Tier 3 fraud to ~5% (Beta(1,19)). Tier 2 remains voluntary. Same technology costs as Tech-First plus $50K annual IBCCES administrative overhead for card verification processing. Benefits Modeled Litigation risk reduction: Lognormal(μ=ln(1.2M), σ=0.8) — fat-tailed distribution capturing rare expensive lawsuits Market expansion revenue: New families × $5K average spending per trip Premium revenue (Scenario 3 only): Tier 3 users × $200/day × 3-day visits Output Metrics P(Net > 0): Probability of positive returns Mean/Median Net Benefit: Expected value and typical outcome VaR(5%): 5th percentile downside risk Mean Cost / P95 Cost: Budget planning statistics Reading the Results The code prints a comparison table showing all six scenarios, then detailed statistics for the IBCCES-verified model. Key findings: Technology-First: 99.3% viability, $1.81M mean benefit, $342K cost IBCCES-Verified Tier 3: 99.0% viability, $1.76M mean benefit, $386K cost Baseline Professional: 3.0% viability, -$4.8M mean loss, $7.0M cost IBCCES verification provides marginally lower returns than pure technology but critically reduces operational risk through verified fraud rates and predictable staffing requirements. Modifying Parameters To test different assumptions, edit: python # Demand tier3_daily = np.random.triangular(left=66, mode=115, right=165, size=n_sims) # Baseline costs amb_salary = truncnorm(mean=45_000, sd=5_000, low=30_000, high=65_000, size=n_sims) # IBCCES verification fraud_rate_t3_ibcces = stats.beta(a=1, b=19).rvs(size=n_sims) # ~5% mean ibcces_admin_cost = 50_000 # annual administrative overhead # 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, SciPy No authentication needed: Works in free Colab tier Runtime: ~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 Paper This analysis demonstrates that labor-intensive accommodation models are economically unsustainable at theme park scale regardless of configuration. Only technology-enabled architectures achieve viability, with IBCCES verification providing operational advantages (verified fraud rates, predictable staffing) that justify slightly higher costs ($386K vs $342K) for comparable returns. The Monte Carlo approach strengthens the argument by demonstrating robustness under parameter uncertainty rather than relying on point estimates vulnerable to specification critique.