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Applying Math with Python

You're reading from   Applying Math with Python Over 70 practical recipes for solving real-world computational math problems

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804618370
Length 376 pages
Edition 2nd Edition
Languages
Concepts
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Author (1):
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Sam Morley Sam Morley
Author Profile Icon Sam Morley
Sam Morley
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: An Introduction to Basic Packages, Functions, and Concepts 2. Chapter 2: Mathematical Plotting with Matplotlib FREE CHAPTER 3. Chapter 3: Calculus and Differential Equations 4. Chapter 4: Working with Randomness and Probability 5. Chapter 5: Working with Trees and Networks 6. Chapter 6: Working with Data and Statistics 7. Chapter 7: Using Regression and Forecasting 8. Chapter 8: Geometric Problems 9. Chapter 9: Finding Optimal Solutions 10. Chapter 10: Improving Your Productivity 11. Index 12. Other Books You May Enjoy

Computing Nash equilibria

A Nash equilibrium is a two-player strategic game – similar to the one we saw in the Analyzing simple two-player games recipe – that represents a steady state in which every player sees the best possible outcome. However, this doesn’t mean that the outcome linked to a Nash equilibrium is the best overall. Nash equilibria are more subtle than this. An informal definition of a Nash equilibrium is as follows: an action profile in which no individual player can improve their outcome, assuming that all other players adhere to the profile.

We will explore the notion of a Nash equilibrium with the classic game of rock-paper-scissors. The rules are as follows. Each player can choose one of the options: rock, paper, or scissors. Rock beats scissors, but loses to paper; paper beats rock, but loses to scissors; scissors beats paper, but loses to rock. Any game in which both players make the same choice is a draw. Numerically, we represent a win...

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