Strategic Firm Capacity Selection under the Network Effects: A Computational Model of Capacity Investment Dynamics in the ICT Industry

This project consists of two main components:

• Game-theoretical Analysis - Two-stage game models for capacity selection and pricing
• Dynamic Approach - Agent-based computational models with consumer networks

A two-stage game analyzing the capacity selection and pricing strategies of firms in the ICT industry.

Key components:

• Consumer threshold analysis
• Nash Equilibrium computation for capacity and pricing decisions
• Comparative statics and sensitivity analysis

Implementation files (in I-Game-theoretical Analysis folder):

1. 1a-Consumer Threshold-Exog Functionality.ipynb - Consumer threshold analysis with exogenous functionality
2. 1b-Consumer Threshold-Endog Functionality.nb - Consumer threshold analysis with endogenous functionality (Mathematica)
3. 2-Exporting Nash Equilibrium.nb - Nash Equilibrium computation and export (Mathematica)
4. 3-Game Theoretical Analysis.ipynb - Comprehensive game-theoretical analysis and results

An agent-based computational model integrating consumer networks to analyze market dynamics and outcomes in the ICT industry.

Key components:

• Monte Carlo simulations of firm competition and capacity decisions
• Consumer network effects and switching behavior
• Policy intervention analysis and comparative experiments
• Statistical analysis of market outcomes

Implementation files (in II-Dynamic Approach folder):

1. 1-Simulation Analysis.ipynb - Core simulation model and market outcome analysis
2. 2a-Policy Visualization.ipynb - Visualization of policy intervention effects
3. 2b-Policy Tests.ipynb - Statistical tests for policy effectiveness
4. selected_param.xlsx - Four key cases showing parameter values

Strategic-Firm-Capacity-Selection-under-the-Network-Effects/
├── I-Game-theoretical Analysis/
│   ├── 1a-Consumer Threshold-Exog Functionality.ipynb
│   ├── 1b-Consumer Threshold-Endog Functionality.nb
│   ├── 2-Exporting Nash Equilibrium.nb
│   ├── 3-Game Theoretical Analysis.ipynb
│   └── Strategic capacity240521_3.csv
│
├── II-Dynamic Approach/
│   ├── 1-Simulation Analysis.ipynb
│   ├── 2a-Policy Visualization.ipynb
│   ├── 2b-Policy Tests.ipynb
│   ├── selected_param.xlsx
│   └── Data/
│
├── requirements.txt
└── README.md

Main Directories:

• I-Game-theoretical Analysis/ - Game-theoretical models and Nash Equilibrium analysis
• II-Dynamic Approach/ - Agent-based simulations and policy analysis

Wolfram Mathematica is required for the game-theoretical analysis, specifically for computing and exporting Nash Equilibrium solutions. The relevant Mathematica code can be found in the I-Game-theoretical Analysis folder.

The following Python packages are required to run the computational models:

• numpy - Numerical computing and array operations
• pandas - Data manipulation and analysis
• scipy - Scientific computing (optimization, statistics, linear algebra)

• matplotlib - Plotting and visualization
• seaborn - Statistical data visualization
• PIL (Pillow) - Image processing

• networkx - Network analysis and graph theory
• ray - Distributed computing and parallel processing
• numba - JIT compilation for performance optimization

• ipywidgets - Interactive widgets for Jupyter notebooks
• IPython - Enhanced interactive Python shell

• statsmodels - Econometric and statistical modeling
• arch - Time series analysis and bootstrap methods

• more_itertools - Additional iteration tools
• openpyxl - Excel file handling (for pandas)

Install all required packages using the requirements file:

pip install -r requirements.txt

This will install all packages with their specified minimum versions (see requirements.txt for details).

This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956107, "Economic Policy in Complex Environments (EPOC)". Many thanks for the guidance of Prof. Herbert Dawid (UNIBI), Prof. Domenico Delli Gatti (UCSC) and Prof. Christiane Fuchs (UNIBI).