
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
Want to become a Master in Game Theory and Open doors to a variety of careers across different fields ?
Take a look at this course where you will
Not only learn about the Game Theory in depth interactively with a lot of examples including Modeling the Game, Classifying the Game, Analyzing Strategic Interactions, Solving the Game, Considering Repeated Games, Considering Stochastic Games, Applying Equilibrium Concepts, and Contextual Analysis including Game Theory Economics but also
Open doors to a variety of careers across different fields like Economist, Financial Analyst, Policy Analyst, Strategic Consultant, Business Analyst, Product Manager, Algorithm Designer, Data Scientist, Artificial Intelligence Researcher, Political Analyst, Diplomat/Foreign Service Officer, Policy Advisor, Legal Analyst, Regulatory Consultant, Operations Research Analyst, Logistics Manager, Health Economist, Behavioral Scientist, Professor/Researcher, Research Scientist, Evolutionary Biologist, Environmental Economist, Marketing Strategist, Sales Manager
Preview many lectures for free to see the content for yourself
My first exposure to Game Theory happened very early in life in my childhood when I started playing Chess which taught me to think a few moves ahead of your opponent and decide your own move to ultimately win the game. Looking back, I learnt about considering all the probable move my opponent would make and then decide my own move to pre-empt the opponent
Formally introduction to Game Theory happened in 1979-81 when doing my MBA at Indian Institute of Management, Bangalore where I learnt how to apply these concepts in making right decision in management
The journey to learn practically more about Game Theory continued over my working life since 1981 till 2016 when I refreshed my learning about Game Theory to teach and coach the MBA students at Indian Institute of Management, Udaipur.
I bring in this course my learnings from this journey and share with you how can you also Master Game Theory
Look at what are students like you saying about this course
"Wonderful. Learnt so many new things about how Game Theory can help make better decisions strategically"
"well experienced lecturer"
Preview for yourself many lectures free. If you like the content, enrol for the course, enjoy and skill yourself to become a Master in Game Theory! If don't like the content, please message about how can we modify it to meet your expectations.
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)