
Introduction to the instructor and the course
At the end of this lecture, you will learn the following
•A case study of careers taken after learning Game Theory
At the end of this section, you will learn the following
•What is Game Theory
At the end of this section, you will learn the following
•What careers can you take after learning Game Theory
At the end of this section, you will learn the following
•Game Theory Framework
At the end of this section, you will learn the following
•An example of Game Theory application
At the end of this course, you will learn the following
•Identifying Players: Determine who the decision-makers are in the scenario
At the end of this course, you will learn the following
•Defining Strategies: List all possible actions or strategies each player can take
At the end of this course, you will learn the following
•Specifying Payoffs: Quantify the rewards or outcomes for each combination of strategies chosen by the players
At the end of this course, you will learn the following
An example of Modeling The Game
•Identification the Decision-Maker
At the end of this course, you will learn the following
An example of Modeling The Game
•Strategies Identification
At the end of this course, you will learn the following
An example of Modeling The Game
•Quantifying Payoffs
At the end of this lecture, you will learn the following
•How to determine whether the game is cooperative or non-cooperative, symmetric or asymmetric, zero-sum or non-zero-sum, and simultaneous or sequential
At the end of this lecture, you will learn the following
•An example of determining whether the game is cooperative or non-cooperative, symmetric or asymmetric, zero-sum or non-zero-sum, and simultaneous or sequential
At the end of this course, you will learn the following
•How to choose between normal (strategic) form and extensive form. The normal form uses matrices to represent payoffs, while the extensive form uses game trees to show sequential decisions
•An example of choosing between normal (strategic) form and extensive form. The normal form uses matrices to represent payoffs, while the extensive form uses game trees to show sequential decisions
At the end of this lecture, you will learn the following
•How to identify if any player has a dominant strategy, which is the best action regardless of what others do
At the end of this lecture, you will learn the following
•How to find the set of strategies where no player can benefit by changing their strategy unilaterally. This represents a stable state where players' strategies are mutual best responses
At the end of this lecture, you will learn the following
•For games without pure strategy equilibria, how to consider mixed strategies where players randomize over possible actions
A real-life case study of Analyzing Strategic Interactions using Game Theory
At the end of this lecture, you will learn the following
•In sequential games, how to use backward induction to determine optimal strategies by analyzing the game from the end to the beginning
At the end of this lecture, you will learn the following
•In some games, how to iteratively eliminate dominated strategies (strategies that are always worse than another strategy) to simplify the analysis
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
Analyze Equilibria
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
Analyze Equilibria
How to ensure the strategy is an equilibrium in every subgame of the repeated
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
How to Use Folk Theorem
Key Concepts
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
•How to Use Folk Theorem
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
How to Consider History-Dependent Strategies
Evaluate how past actions influence current decisions
Understanding History-Dependent Strategies
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
How to Consider History-Dependent Strategies
How to evaluate how past actions influence current decisions
Setting Up the Framework
Analyzing the Impact of History on Current Decisions
Evaluating Specific Strategies
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
•How to Consider History-Dependent Strategies
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
How to Consider History-Dependent Strategies
Detailed Evaluation of Common Strategies- Grim Trigger
Detailed Evaluation of Common Strategies- Pavlov Strategy
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
•How to Consider History-Dependent Strategies
Formal Analysis Using Mathematical Tools
How to use dynamic programming to evaluate the value function
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
•How to Consider History-Dependent Strategies
Formal Analysis Using Mathematical Tools
How to use dynamic programming to evaluate the value function
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
•How to Consider History-Dependent Strategies
Formal Analysis Using Mathematical Tools
How to use dynamic programming to evaluate the value function- An Example
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
•How to Consider History-Dependent Strategies
How to evaluate how past actions influence current decisions
Evaluating Through Simulation
Monte Carlo Simulations
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
•How to Consider History-Dependent Strategies
How to evaluate how past actions influence current decisions
Evaluating Through Simulation
Statistical Analysis
At the end of this lecture, you will learn the following
How to analyze strategies in games that are played multiple times, considering the impact of past actions on future decisions and outcomes
•How to Consider History-Dependent Strategies
Use the history of play to determine future strategies
At the end of this lecture, you will learn the following
•How to account for random events and probabilistic transitions between states in dynamic strategic interactions
At the end of this lecture, you will learn the following
•How to account for random events and probabilistic transitions between states in dynamic strategic interactions
Solving MDP
Value Iteration
At the end of this lecture, you will learn the following
How to account for random events and probabilistic transitions between states in dynamic strategic interactions
Solving MDP
An example of Value Iteration
At the end of this lecture, you will learn the following
How to account for random events and probabilistic transitions between states in dynamic strategic interactions
Solving MDP
Policy Iteration
At the end of this lecture, you will learn the following
•How to account for random events and probabilistic transitions between states in dynamic strategic interactions
Solving MDP
Policy Iteration Example
At the end of this lecture, you will learn the following
How to account for random events and probabilistic transitions between states in dynamic strategic interactions
Stochastic Games
At the end of this lecture, you will learn the following
How to account for random events and probabilistic transitions between states in dynamic strategic interactions
Stochastic Games Example
At the end of this lecture, you will learn the following
•Evaluating if the outcomes are Pareto efficient, meaning no player can be made better off without making another player worse off.
At the end of this lecture, you will learn the following
Evaluating if the outcomes are Pareto efficient, meaning no player can be made better off without making another player worse off
•How to make graphical representation of Pareto efficient outcomes
At the end of this lecture, you will learn the following
How to, in extensive form games, ensure that strategies constitute a Nash equilibrium in every subgame?
At the end of this lecture, you will learn the following
For games with incomplete information where players have beliefs about unknown factors, how to use Bayesian Nash equilibrium to incorporate probabilistic reasoning?
At the end of this lecture, you will learn the following
Example of Bayesian Nash Equilibrium
At the end of this lecture, you will learn the following
•Incorporate psychological and behavioral factors that may influence decision-making, such as bounded rationality, fairness, and cooperation.
At the end of this lecture, you will learn the following
Incorporate psychological and behavioral factors that may influence decision-making, such as bounded rationality, fairness, and cooperation
•Example: Market Entry Game with Simplified Strategies
At the end of this lecture, you will learn the following
Incorporate psychological and behavioral factors that may influence decision-making, such as bounded rationality, fairness, and cooperation
How to incorporate bounded rationality?
How to approach modeling players as making decisions probabilistically
At the end of this lecture, you will learn the following
Incorporate psychological and behavioral factors that may influence decision-making, such as bounded rationality, fairness, and cooperation
•Example: Market Entry Game with QRE
At the end of this lecture, you will learn the following
Incorporate psychological and behavioral factors that may influence decision-making, such as bounded rationality, fairness, and cooperation
•How to use Level-k Thinking for making decisions in games?
At the end of this lecture, you will learn the following
Incorporate psychological and behavioral factors that may influence decision-making, such as bounded rationality, fairness, and cooperation
•How can Fairness influence player decisions?
At the end of this lecture, you will learn the following
Incorporate psychological and behavioral factors that may influence decision-making, such as bounded rationality, fairness, and cooperation
•Fairness
At the end of this lecture, you will learn the following
Incorporate psychological and behavioral factors that may influence decision-making, such as bounded rationality, fairness, and cooperation
•How to incorporate Cooperation?
At the end of this lecture, you will learn the following
Incorporate psychological and behavioral factors that may influence decision-making, such as bounded rationality, fairness, and cooperation
•How to incorporate Cooperation in Social Preferences Models?
At the end of this lecture, you will learn the following
Incorporate psychological and behavioral factors that may influence decision-making, such as bounded rationality, fairness, and cooperation
•Example of Incorporating These Factors
At the end of this lecture, you will learn the following
In biological or social contexts, analyze how strategies evolve over time based on their success and replication
At the end of this lecture, you will learn the following
In biological or social contexts, analyze how strategies evolve over time based on their success and replication
At the end of this lecture, you will learn the following
In biological or social contexts, analyze how strategies evolve over time based on their success and replication
At the end of this lecture, you will learn the following
Identify the fixed points of the replicator dynamics, where the proportions of strategies do not change over time
•How analyze the stability of these points to understand if they are attractors (stable equilibria) or repellors (unstable equilibria).
At the end of this lecture, you will learn the following
How to, In biological or social contexts, analyze how strategies evolve over time based on their success and replication?
•Simulation and Numerical Analysis
At the end of this lecture, you will learn the following
How to, In biological or social contexts, analyze how strategies evolve over time based on their success and replication?
•Apply numerical methods to solve the replicator equations and analyze the trajectories of strategy frequencies
At the end of this lecture, you will learn the following
How to, In biological or social contexts, analyze how strategies evolve over time based on their success and replication
•How to apply numerical methods to solve the replicator equations and analyze the trajectories of strategy frequencies?
At the end of this lecture, you will learn the following
How to, In biological or social contexts, analyze how strategies evolve over time based on their success and replication
•Linearize the system around equilibrium points
At the end of this lecture, you will learn the following
In biological or social contexts, analyze how strategies evolve over time based on their success and replication
•How to create phase diagrams to visualize the dynamics in the strategy space?
At the end of this lecture, you will learn the following
In biological or social contexts, analyze how strategies evolve over time based on their success and replication
•How to study how changes in parameters affect the stability and behavior of the system
At the end of this lecture, you will learn the following
In biological or social contexts, analyze how strategies evolve over time based on their success and replication
•How to introduce a small probability of mutation, where individuals may randomly switch strategies
At the end of this lecture, you will learn the following
In biological or social contexts, analyze how strategies evolve over time based on their success and replication
•How to consider stochastic models where randomness in payoffs or strategy adoption is included
At the end of this lecture, you will learn the following
In biological or social contexts, analyze how strategies evolve over time based on their success and replication
•How to gather empirical data on the strategies and their payoffs
At the end of this lecture, you will learn the following
In biological or social contexts, analyze how strategies evolve over time based on their success and replication
•Example of data collection for empirical validation
At the end of this lecture, you will learn the following
In biological or social contexts, analyze how strategies evolve over time based on their success and replication
•How to fit your theoretical model to the empirical data
At the end of this lecture, you will learn the following
In biological or social contexts, analyze how strategies evolve over time based on their success and replication
•How to fit your theoretical model to the empirical data
Example Workflow
Would you like to unlock career opportunities across business, economics, AI and strategy, but don't know which strategic skills employers value most?
One of the most valuable and transferable skills across these careers is the ability to analyze strategic situations, anticipate the actions of competitors and stakeholders, and consistently make better decisions. That is exactly what Game Theory helps you develop.
Whether you aspire to become a Strategy Consultant, Business Analyst, Product Manager, Economist, Financial Analyst, Data Scientist, AI Professional, Policy Analyst, Operations Research Analyst or Marketing Strategist, mastering Game Theory can give you a significant competitive advantage throughout your career.
In this course, you will not simply learn Game Theory concepts. You will master the complete Game Theory decision framework that enables you to systematically model strategic situations, analyze alternatives, predict outcomes and make optimal decisions across business, economics, artificial intelligence and strategy.
Unlike many Game Theory courses that explain individual concepts or mathematical models in isolation, this course provides a complete end-to-end decision framework that guides you from understanding a strategic problem to confidently selecting and validating the best possible decision. You will learn not only what each concept means, but more importantly why, when and how to apply it in real-world situations.
Throughout this course, you will progressively build the capabilities required to solve increasingly complex strategic decisions by learning how to:
Model strategic situations by identifying decision makers, players, strategies, incentives and payoffs.
Classify different types of games and determine the most appropriate analytical approach.
Analyze strategic interactions to anticipate competitor and stakeholder behaviour.
Solve sequential, repeated and stochastic games using proven analytical techniques.
Apply Nash Equilibrium, Bayesian Games and advanced equilibrium concepts to improve decision quality.
Evaluate uncertainty, cooperation, fairness and evolving competitive behaviour through advanced contextual analysis.
Apply the complete Game Theory decision framework to solve practical business, economics, AI and strategic decision-making challenges.
The curriculum has been carefully designed to take you from fundamental concepts to advanced applications through a logical, step-by-step learning journey. You will master Modeling the Game, Strategic Interaction Analysis, Sequential Games, Repeated Games, Stochastic Games, Dynamic Programming, Markov Decision Processes, Nash Equilibrium, Bayesian Games, Evolutionary Game Theory and Contextual Analysis as one integrated decision-making framework rather than as disconnected topics.
To help you confidently apply what you learn, the course includes practical examples, worked illustrations, case studies and structured explanations that demonstrate how Game Theory is used to solve real strategic problems across multiple industries and professional environments.
By the end of this course, you will possess one of the most valuable strategic decision-making capabilities sought across business, economics, AI and strategy. More importantly, you will have a practical framework that you can confidently apply throughout your career to analyze complex situations, predict competitive behaviour and make smarter strategic decisions.
If you are ready to unlock career opportunities across business, economics, AI and strategy by mastering the complete Game Theory decision framework, I look forward to welcoming you into the course.
This Course is Part of a Structured Learning Path
Learning Path: PROBLEM SOLVING & DECISION MAKING PATH (Starter → Builder → Advanced)
This course is your BUILDER step.
Next Recommended Courses
After completing this course, continue your growth with:
Problem Solving (Starter)
Systems Thinking (Builder)
Excellence in Case Analysis (Advanced)
AI for Problem Solving (Advanced)
Learning Path: CONSULTING PATH (Starter → Builder → Advanced)
This course is your BUILDER step.
Next Recommended Courses
After completing this course, continue your growth with:
Guesstimates and Consulting Cases (Starter)
Systems Thinking (Builder)
Business Strategy and Planning (Builder)
AI for Business Optimization (Advanced)