Decoding Market Dynamics: How Atlanta's Leading Firms Shape Economic Networks.

As many of you who have been following me on #Porandu for a while know, I have been living in the US for a few weeks now. That is why, in the process of understanding the new economic scenario that surrounds me, I took a few hours to analyze how business networks behave in Atlanta, GA. It took me a few hours of working with R and some algorithms to draw some conclusions that are not visible to the naked eye, but are intuited. I share the analysis with you.

The R script analyzes a commercial network consisting of 200 companies based in Atlanta, GA, focusing on their interactions and the role of major firms within this network. Initially, a representative sample of companies was acquired from an information provider, based on statistical data from Georgia. This dataset includes interactions among 200 companies, including 5 major ones (Coca-Cola, Home Depot, Delta, UPS, CNN) and 195 smaller or medium-sized firms. To extend the dataset and gain deeper insights, synthetic data was generated using Generative Adversarial Networks (GANs), which allows for a more comprehensive view of the network.

The dataset contains 250 interactions among these companies. Interactions were randomly sampled, but to avoid unrealistic data, self-loops (where a company interacts with itself) were removed. Furthermore, the dataset was adjusted so that approximately 30% of the interactions involve major companies, reflecting their dominant position within the network.

Using the igraph package, a directed network graph was constructed from this interaction data. The igraph package is instrumental for analyzing network metrics such as node degrees, which represent the number of direct connections a company has. The major companies are highlighted in red and labeled in uppercase for enhanced visibility, while smaller companies are displayed in light blue. The size of each node is proportional to its degree, providing a visual representation of each company's influence in the network.

The network was visualized with the ggraph package, which builds on ggplot2 to offer advanced graphing capabilities. The visualization includes arrows indicating the direction of interactions, nodes representing companies, and labels for identification. The size of nodes reflects their number of connections, and different colors distinguish between major and smaller companies.

Major companies, as central nodes, shape market dynamics through their strategic connections.

Generative Adversarial Networks (GANs) were employed to create synthetic data, an approach that involves a generator and a discriminator working in tandem. The generator produces synthetic data that mimics the characteristics of the real dataset, while the discriminator evaluates its realism. This method allows for the expansion of the dataset while maintaining accuracy and relevance, providing a richer basis for analysis.

Real and synthetic data analyze a commercial network in Atlanta, highlighting major companies' central roles.

In economic terms, network economics examines how the structure and dynamics of networks influence economic behavior and outcomes. In this network, major companies serve as central nodes, suggesting their significant role in shaping interactions. Their numerous connections indicate a high degree of influence and strategic importance. These central players can leverage their position to affect market dynamics, such as negotiating power and market influence.

Network economics and game theory illustrate how central companies influence market equilibrium.

From a game theory perspective, the behavior of these major companies can be analyzed through strategic interaction models. In this context, game theory explores how companies' decisions impact each other and the overall market equilibrium. Major firms, being central nodes, are likely to engage in strategic behavior to maximize their advantage. They may influence smaller companies' decisions, form alliances, or engage in competitive strategies to maintain their market position. This interaction reflects game theory principles where the strategies of key players impact the outcomes for all participants in the network.

At in the end of the day, the integration of real and synthetic data through GANs offers a robust framework for network analysis. By examining the central role of major companies and their strategic interactions, this analysis provides valuable insights into market dynamics and competitive strategies. The use of network economics and game theory principles enhances our understanding of how central players influence business outcomes and economic stability, offering a comprehensive view of the commercial network's structure and behavior.

Next Steps for Analyzing the Data: Expanding Insights through Advanced Economic and Game Theory Models

In the evolving landscape of commercial networks, understanding the interplay between firms requires more than basic visualization. Our recent analysis of a network consisting of 250 companies in Atlanta, GA, provides a foundation for deeper investigation. Here, I outline the next steps to enhance our insights through advanced economic modeling and game theory.

1. Modeling Based on Game Theory

To comprehend the strategic decisions firms make within the network, we will employ game theory. This approach involves several key steps:

• Defining the Game: Each company will be treated as a player with specific strategies, such as varying investment levels, pricing models, or cooperation versus competition. The outcomes or payoffs associated with these strategies will be quantified, representing potential benefits or costs.
• Applying Game Theory Algorithms: We will use algorithms to identify Nash equilibria, where no company benefits from unilaterally changing its strategy. Both cooperative and non-cooperative games will be modeled to explore potential collaborations or conflicts among firms.

R Libraries for Game Theory:

• "GameTheory" for modeling and analyzing game-theoretic scenarios.
• "gtools" for generating strategy combinations and calculating corresponding payoffs.
• "nashpy" for Nash equilibrium calculations, integrated into R via reticulate.

2. Analyzing Strategies in Networks

Understanding how a company's position in the network affects its strategies is crucial. This involves:

• Calculating Network Metrics: Metrics such as centrality (degree, betweenness, closeness) will be calculated to identify influential firms and their roles within the network. The overall structure of the network, including connected components and clusters, will be assessed to understand the impact on strategic interactions.
• Simulating Strategies: We will conduct simulations to observe the effects of different strategies over time. Adaptive models will be employed, allowing firms to adjust their strategies based on competitor behavior.

R Libraries for Network Analysis:

• "igraph" for network construction, metric calculation, and visualization.
• "network" and "sna" for advanced network analysis and structural measures.
• "deSolve" for solving differential equations, useful in modeling dynamic, repeated games.

3. Implementation and Validation

To ensure robustness and relevance, the following steps will be taken:

• Model Validation: Results will be compared against real data to validate the models. Sensitivity analyses will be performed to understand how changes in parameters affect outcomes.
• Documentation and Reporting: Findings will be documented with detailed reports and visualizations, highlighting strategic implications for firms. Results will be communicated effectively through presentations, emphasizing the strategic insights derived from the models.

These steps represent a comprehensive approach to expanding our understanding of commercial networks using advanced economic and game theory models. By integrating these methodologies, we aim to uncover deeper insights into the strategic dynamics of firms and their interactions within the network.

So stay tuned, and anyone who wants to contribute is welcome.