Lecture 5: Nash Equilibrium
Skills:
Agent Foundations90%
MIT 14.12 Economic Applications of Game Theory, Fall 2025
Instructor: Ian Ball
View the complete course: https://ocw.mit.edu/courses/14-12-economic-applications-of-game-theory-fall-2025/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63quuKvMHCt3cmTmt0O2qpv
In this lecture, Ian Ball explains Nash equilibrium, a game theory idea that describes a situation where each person is choosing the best response to everyone else, so no one gains by changing alone. Using examples like two friends choosing between a Celtics game and a Red Sox game and a hide-and-seek game, the lesson also shows why people sometimes need to randomize their choices to avoid being predictable.
License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
More on: Agent Foundations
View skill →Related AI Lessons
⚡
⚡
⚡
⚡
Building an Agentic AI for Oracle Fusion Cloud GL Reconciliation
Medium · AI
Building an Agentic AI for Oracle Fusion Cloud GL Reconciliation
Medium · Python
OpenClaw's $1.3 Million OpenAI Bill: What AI Agents Actually Cost in Production
Dev.to · Tom Tokita
AI Agents Don't Have Permissions — Runtimes Do
Dev.to · Glendel Joubert Fyne Acosta
🎓
Tutor Explanation
DeepCamp AI