Games and Information Course | Advanced Game Theory | IIT Bombay
Course Details
| Exam Registration | 460 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Computer Science and Engineering, Data Science, Artificial Intelligence |
| Credit Points | 3 |
| Level | Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 18 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Mastering Strategic Decision-Making: An In-Depth Look at IIT Bombay's "Games and Information" Course
In today's interconnected world, from financial markets to ride-sharing platforms, strategic interactions underpin countless systems. Understanding these dynamics is no longer just for economists; it's a critical skill for engineers, data scientists, and business leaders. At the forefront of this interdisciplinary field is the advanced postgraduate course "Games and Information", offered at the prestigious Indian Institute of Technology Bombay (IITB) and taught by the distinguished Prof. Ankur A. Kulkarni.
Course Overview: Bridging Theory and Real-World Complexity
This 12-week intensive program is designed for graduate students in engineering and economics. It moves beyond introductory game theory to focus intensely on the role of information—how what players know, don't know, or believe shapes strategic outcomes. Approximately 65% of the curriculum is dedicated to dynamic games, where information structures, asymmetry, and beliefs take center stage.
The course journey begins with static games and Nash equilibrium before rapidly advancing into the core themes of informational complexity. Students explore Bayesian games, mechanism design, signaling, screening, and the cutting-edge area of information design. It culminates with practical applications in finance, demonstrating the direct relevance of these theoretical tools.
Meet the Instructor: Prof. Ankur A. Kulkarni
The course is led by Prof. Ankur A. Kulkarni, an accomplished systems theorist and the Kelkar Family Chair in Quantitative Finance at IIT Bombay. His research sits at the nexus of game theory, information theory, control theory, and machine learning, with a focus on strategic inference, privacy, and nudging.
Prof. Kulkarni's expertise is not confined to academia. His significant industry and regulatory experience includes:
- Consultant to SEBI: Solely responsible for suggesting regulatory interventions for high-frequency algorithmic trading.
- SEBI IT-Project Advisory Board: Advising on the use of AI/ML, data analytics, and data policies.
- Advisor to TCS & Maha-IT: Providing technical guidance to major corporate and government entities.
- Previous Consultancies: Tackling real-world problems for HDFC Life (sales incentives), Kotak Mahindra Bank (anti-money laundering), and Bank of Baroda (cash management).
His accolades include being an Associate of the Indian Academy of Sciences (an honor for top scientists under 35), an INSPIRE Faculty Award, and several Best Paper and Excellence in Teaching awards.
Detailed Course Curriculum: A 12-Week Journey
The course layout is meticulously structured to build a deep, sequential understanding of strategic interactions with informational complexities.
| Week | Key Topics Covered |
|---|---|
| Weeks 1-2 | Foundations: Game definition, Nash Equilibrium, Dominated Strategies, Aumann's model of incomplete information, Common Knowledge. |
| Weeks 3-5 | Zero-Sum Games & Existence: Security strategies, Saddle points, Von Neumann's minimax theorem, Computation of equilibria, Existence proofs via Kakutani's and Brouwer's fixed-point theorems. |
| Weeks 6-9 | Dynamic Games & Information Structures: Extensive form, Standard normal form, Threat equilibrium, Single and Multi-Act Games, Feedback Nash Equilibrium, Mixed vs. Behavioral strategies (Kuhn's Theorem). |
| Weeks 10-12 | Incomplete Information & Applications: Bayesian Nash Equilibrium, Stackelberg games, Principal-Agent models (Moral Hazard, Adverse Selection), Mechanism Design, Correlated Equilibrium, Revelation Principle. |
Who Should Take This Course and Industry Relevance
Intended Audience: The course is ideally suited for postgraduate students and professionals in Computer Science, Data Science, Artificial Intelligence, Economics, and Quantitative Finance who seek a rigorous mathematical foundation in strategic thinking.
Industry Support & Applications: The tools taught are directly applicable in sectors recognized as industry supporters:
- Finance: Algorithmic trading, market design, and regulatory policy (as seen in Prof. Kulkarni's SEBI work).
- Platform Economies: Designing pricing, matching, and incentive systems for companies like Ola, Uber, and Amazon.
- Technology: AI agent design, multi-agent systems, and strategic data privacy.
Essential Reading and Resources
The course draws from seminal texts in the field, providing students with a comprehensive library for deep reference:
- Myerson, R., “Game theory: Analysis of Conflict”
- Basar, T. and Olsder, G., “Dynamic Noncooperative Game Theory”
- Fudenberg, D. and Tirole, J., “Game Theory”
- Maschler, M., Solan E. and Zamir, S., “Game Theory”
- Rasmusen, E., “Games and Information: An Introduction to Game Theory”
Conclusion: Why This Course Matters
"Games and Information" is more than an academic course; it's a training ground for the strategic minds needed to navigate and design the complex, information-rich systems of the 21st century. Under the guidance of a professor who bridges theory and practice, students gain not just mathematical tools, but a framework for understanding competition, cooperation, and information flow in technology, economics, and beyond. For anyone aiming to work at the highest levels of tech, finance, or strategy, mastering the concepts in this course is an invaluable investment.
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