DOI: 10.5281/zenodo.17256144
title: A Technology-Enabled Tiered Accommodation System with Crisis Ambassador Support

description: 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.