Analyzed and Visualized Firm Profit Strategies

• Day: 2024-09-10
• Time: 00:25 to 00:45
• Project: Business
• Workspace: WP 1: Strategic / Growth & Development
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Game Theory, Profit Analysis, Python, Visualization, Collusion, Nash Equilibrium

Description

Session Goal: The session aimed to analyze and visualize firm profit strategies within the context of game theory, focusing on collusion, Nash equilibrium, and deviation strategies.

Key Activities:

• Conducted a detailed analysis of collusive profits and identified issues with negative profits, suggesting model assumption adjustments for realistic conditions.
• Developed Python code using matplotlib to visualize profits for two firms under different strategic scenarios, including Nash equilibrium, collusive, and deviation strategies.
• Adjusted profit calculations based on output quantities, ensuring accurate representation of Nash, collusive, and deviation profits.
• Simulated a two-spell duration model using a Weibull baseline hazard, generating covariates, parameters, and random effects with Python code.
• Analyzed the impact of random effects and covariates on individual hazard rates in a Weibull distribution model.

Achievements:

• Successfully visualized firm profits under various strategic scenarios using Python.
• Enhanced understanding of profit dynamics in game theory models through simulation and analysis.

Pending Tasks:

• Further refine model assumptions to address identified issues with negative profits in collusive scenarios.

Evidence

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• event_ids: []