Smart Contract Adoption under Discrete Overdispersed Demand: A Negative Binomial Optimization Perspective

Battle Count: 53

While the paper is categorized under q-fin.CP, its primary focus is on supply chain management and procurement optimization rather than financial markets or trading. The Negative Binomial modeling approach and overdispersion concepts have tangential relevance to modeling discrete financial events (e.g., trade counts, order arrivals), and the optimization framework shares methodological similarities with inventory-theoretic approaches in market making. However, the paper does not directly address trading strategies, asset pricing, or financial risk management.

The framework requires: (1) Maximum likelihood estimation for Negative Binomial parameters, (2) AR(1) process estimation for temporal dynamics, (3) Monte Carlo simulation with 10,000 replications per scenario, (4) Grid search over adoption levels and order quantities, (5) Multiple cost/penalty function evaluations. Computational requirements include 16 CPU cores, 12-16 GB RAM per process, and 4-6 hours wall-clock time per complete grid search. The mathematical formulation is moderately complex with multiple interacting cost components, but the simulation-based approach avoids analytical intractability.

All data, Python code, figures, and derived output files are available in a public Kaggle repository. Random seeds are fixed (0, 42, 1234, 2023) for reproducibility. Simulations were repeated across multiple seeds with results consistent within 0.5% tolerance. Complete preprocessing scripts implemented in Python 3.10 using pandas and numpy. However, some parameter calibration sources reference industry benchmarks and managerial estimates that may not be fully transparent.