Analyzed Game Theory Strategies and Probabilities

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Analyzed Game Theory Strategies and Probabilities

  • Day: 2024-12-04
  • Time: 20:50 to 21:20
  • Project: Business
  • Workspace: WP 1: Strategic / Growth & Development
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Game Theory, Decision-Making, Probability, Strategy, Bayesian Reasoning

Description

Session Goal

The session aimed to explore and analyze decision-making strategies and probabilities in game theory, focusing on naive strategies, error resolution in code, and expected value calculations.

Key Activities

  • Discussed the practical relevance of naive strategies in decision-making under uncertainty, emphasizing their effectiveness in quick decision-making and robustness in low-data scenarios.
  • Resolved an argument mismatch error in the f_uniform() function, ensuring all required arguments are passed in future calls.
  • Analyzed optimal strategies in a three-player game, highlighting the selection of maximum value of x (1.0) for maximizing expected payoff.
  • Conducted detailed analysis of winning probabilities and expected values in game scenarios, focusing on player choices and expected payoffs.
  • Explored expected value calculations in two-agent scenarios, addressing key issues and proposing fixes for clarity and accuracy.
  • Analyzed win/lose probabilities and expected values, discussing strategic trade-offs based on player choices.
  • Implemented correct logic for calculating win/lose probabilities using Python, including plotting corrected probability curves.

Achievements

  • Clarified the role of naive strategies in decision-making and their application as benchmarks.
  • Resolved coding issues in the f_uniform() function, improving error handling.
  • Identified optimal strategies and calculated expected values in game theory scenarios.

Pending Tasks

  • Further refine the probability calculations and explore additional strategies in multi-agent scenarios.
  • Continue improving the clarity and accuracy of expected value representations.

Evidence

  • source_file=2024-12-04.sessions.jsonl, line_number=5, event_count=0, session_id=63fd071167539d851dcb77c07d005b8e1f68914ec800f500190f7ad2acccf7ce
  • event_ids: []

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