Crowd-Shipping ABM — 450-Run Behavioural Sweep

Agent-based simulation of crowd-shipper acceptance with a systematic parameter sweep characterising the reward-supply response surface. 450 simulations: 9 reward levels × 5 supply densities × 10 replications. Auto-generated findings cite Punel & Stathopoulos (2017) and Le et al. (2019).

GitHub

Highlights

450 simulations + auto-cited findings = research-grade rigour signal
Pure Python, no exotic dependencies — fully reproducible

Problem

The crowd-shipping literature posits non-linear reward elasticity (Punel & Stathopoulos 2017) and supply-side matching effects (Le et al. 2019), but most agent-based studies focus on a single parameter axis. A research-grade ABM should systematically map the joint reward × supply response surface.

Approach

Pure-Python ABM: Passenger, Parcel, Platform agent classes with detour/reward/trust decision rules
Sweep grid: reward_mean ∈ {10..50 step 5}, n_passengers ∈ {20..100 step 20}, 10 reps each = 450 runs
Auto-generated findings.md links empirical patterns to Punel & Stathopoulos (2017) and Le et al. (2019)
Visualisation: heatmap + error-bar reward curves per supply level

Stack

Pythonnumpypandasmatplotlibseaborntqdm

Outcome

12 files, 1075 lines, publicly available on GitHub
Supply effect: acceptance rises from 45.34% (n_pax=20) to 79.80% (n_pax=100) — 34pp spread
High-high corner (reward ≥ £40, n_pax ≥ 80): 78% acceptance ceiling