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Barsha Saha
In this 2-hour long project-based course, you will learn the game theoretic concepts of Two player Static and Dynamic Games, Pure and Mixed strategy Nash Equilibria for static games (illustrations with unique and multiple solutions), Example of Axelrod...
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In this 2-hour long project-based course, you will learn the game theoretic concepts of Two player Static and Dynamic Games, Pure and Mixed strategy Nash Equilibria for static games (illustrations with unique and multiple solutions), Example of Axelrod tournament. You will be building two player Nash games and analyze them using Python packages Nashpy and Axelrod, especially built for game theoretic analyses. Also, you will gain the understanding of computational mechanisms related to the aforementioned concepts. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops a foundation in game theory, including static and dynamic games, Nash equilibrium, and tournament analysis
Utilizes Python packages specifically designed for game theoretic analyses, enhancing practical understanding
Suitable for learners with a background in game theory or related fields
May not be accessible for those without basic programming knowledge

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Reviews summary

Learn python with game theory

This course provides a solid foundation in game theory concepts and their practical implementation with Python. While the audio quality and some technical issues may need improvement, learners appreciate the interactive exercises and clear explanations, making it a good choice for beginners interested in applying game theory in Python.
Engaging and practical exercises
"exercises are very well structerd"
"pace is easy to follow"
Introduces core concepts clearly
"Great course"
"learned the game theoretic concepts"
"Good introduction"
Some technical issues with display
"The images in the notebook did show up"
"Rhyme platform is a train wreck"
Audio quality can be improved
"Bad audio track, not easy to follow"
"voice is not natural"
"sound quality is not great"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Game Theory with Python with these activities:
Review basic probability and statistics
Review fundamental concepts to strengthen understanding of game theory concepts.
Browse courses on Probability
Show steps
  • Review probability distributions and their properties.
  • Practice calculating expected values and variances.
  • Review statistical inference concepts like hypothesis testing and confidence intervals.
Read 'Game Theory: An Introduction' by Ken Binmore
Gain a comprehensive understanding of game theory fundamentals through a well-regarded textbook.
Show steps
  • Purchase or borrow the book 'Game Theory: An Introduction' by Ken Binmore.
  • Allocate dedicated time for reading and studying the material.
  • Take notes, highlight key concepts, and engage in active recall.
  • Complete the end-of-chapter exercises to test your understanding.
Follow online tutorials on dynamic game theory
Expand knowledge by exploring dynamic game theory concepts through structured tutorials.
Show steps
  • Identify reputable online platforms or courses offering tutorials on dynamic game theory.
  • Enroll in or access the tutorials and follow the provided instructions.
  • Take notes, complete exercises, and actively engage with the material.
  • Seek clarification or ask questions in discussion forums or with the course instructors.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Analyze game scenarios using Nashpy
Gain proficiency in applying Nashpy to analyze game scenarios and identify Nash Equilibria.
Browse courses on Nash Equilibrium
Show steps
  • Explore the Nashpy library and its functionality.
  • Implement simple two-player games in Python and analyze their Nash Equilibria using Nashpy.
  • Experiment with different game scenarios and analyze the impact on Nash Equilibria.
Create a blog post explaining static game concepts
Develop a deeper understanding by explaining static game concepts to a wider audience.
Show steps
  • Research and summarize key concepts related to static games, including Nash Equilibrium.
  • Craft a well-structured blog post that clearly explains these concepts.
  • Provide real-life examples or scenarios to illustrate the concepts in a relatable manner.
  • Seek feedback from peers or instructors to refine the blog post for accuracy and clarity.
Contribute to open-source game theory projects
Engage with the game theory community by contributing to open-source projects and sharing knowledge.
Browse courses on Community Involvement
Show steps
  • Identify open-source game theory projects on platforms like GitHub or GitLab.
  • Review the project documentation and identify areas where you can contribute.
  • Fork the project, make your changes, and submit a pull request.
  • Engage with the project maintainers and community members to discuss your contributions.
Attend a workshop on game theory applications
Gain insights from experts and engage in discussions on practical applications of game theory.
Browse courses on Case Studies
Show steps
  • Research and identify workshops focused on game theory applications.
  • Register and actively participate in the workshop, engaging with speakers and attendees.
  • Take detailed notes, ask questions, and contribute to discussions.
  • Reflect on the knowledge gained and explore ways to apply it in real-world scenarios.
Volunteer as a tutor or mentor for game theory concepts
Solidify understanding by explaining concepts to others and guiding their learning journey.
Browse courses on Mentoring
Show steps
  • Offer your services as a volunteer tutor or mentor through platforms or educational institutions.
  • Prepare lesson plans and materials to effectively convey game theory concepts.
  • Engage with students, answer their questions, and provide personalized guidance.
  • Seek feedback from students to improve your teaching approach.

Career center

Learners who complete Game Theory with Python will develop knowledge and skills that may be useful to these careers:
Operations Research Analyst
An Operations Research Analyst applies analytical methods to help organizations make better decisions. This course is a good fit for this role because it provides a foundation in game theory, which is a branch of mathematics that deals with decision-making in situations where there are multiple parties with conflicting interests.
Business Analyst
A Business Analyst helps organizations identify and solve problems. This course is a good fit for this role because it provides a foundation in game theory, which can be used to model and analyze business decisions.
Software Engineer
A Software Engineer designs, develops, and tests software. This course is a good fit for this role because it provides a foundation in game theory, which can be used to model and analyze complex software systems.
Data Scientist
A Data Scientist uses data to solve problems and make decisions. This course is a good fit for this role because it provides a foundation in game theory, which can be used to model and analyze complex decision-making problems.
Management Consultant
A Management Consultant helps organizations improve their performance. This course may be useful for this role because it provides a foundation in game theory, which can be used to model and analyze complex organizational decision-making processes.
Investment Analyst
An Investment Analyst helps individuals and organizations make investment decisions. This course may be useful for this role because it provides a foundation in game theory, which can be used to model and analyze complex financial markets.
Public Policy Analyst
A Public Policy Analyst helps government agencies develop and implement public policies. This course may be useful for this role because it provides a foundation in game theory, which can be used to model and analyze complex policy decisions.
Marketing Manager
A Marketing Manager develops and executes marketing campaigns. This course may be useful for this role because it provides a foundation in game theory, which can be used to model and analyze complex marketing decisions.
Financial Analyst
A Financial Analyst analyzes financial data and makes recommendations. This course may be useful for this role because it provides a foundation in game theory, which can be used to model and analyze complex financial decisions.
Operations Manager
An Operations Manager plans, organizes, and controls the operations of an organization. This course may be useful for this role because it provides a foundation in game theory, which can be used to model and analyze complex operational decisions.
Actuary
An Actuary analyzes financial risks and develops insurance policies. This course may be useful for this role because it provides a foundation in game theory, which can be used to model and analyze complex risk management decisions.
Economist
An Economist studies the production, distribution, and consumption of goods and services. This course may be useful for this role because it provides a foundation in game theory, which can be used to model and analyze complex economic decisions.
General Manager
A General Manager is responsible for the overall operation of an organization. This course may be useful for this role because it provides a foundation in game theory, which can be used to model and analyze complex management decisions.
Project Manager
A Project Manager plans, executes, and closes projects. This course may be useful for this role because it provides a foundation in game theory, which can be used to model and analyze complex project management decisions.
Statistician
A Statistician collects, analyzes, and interprets data. This course may be useful for this role because it provides a foundation in game theory, which can be used to model and analyze complex statistical problems.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Game Theory with Python.
Classic textbook on game theory that is widely used in academic institutions and by industry professionals. It provides a comprehensive overview of game theory, including both static and dynamic games.
Provides a broad overview of game theory, covering both static and dynamic games. It good reference for those who want to learn more about game theory or use it in their work.
Explores the evolution of cooperation using game theory. It must-read for anyone interested in the foundations of game theory and its applications to social and biological systems.
More advanced treatment of game theory that is written for economists and other social scientists. It provides a comprehensive overview of game theory and how it can be used to model economic and social phenomena.
Applies game theory to legal problems, providing insights into the strategic behavior of lawyers, judges, and other legal actors. It valuable resource for law students, legal practitioners, and anyone interested in the intersection of law and game theory.
Applies game theory to political science, providing insights into the strategic behavior of politicians and voters. It valuable resource for political scientists and anyone interested in the intersection of politics and game theory.
Explores the strategic aspects of conflict, providing insights into how rational actors can resolve conflicts peacefully. It valuable resource for anyone interested in the application of game theory to conflict resolution.
Challenges the traditional view of rationality in game theory, arguing that emotions play an important role in strategic decision-making. It valuable resource for anyone interested in the behavioral foundations of game theory.

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