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Applying Math with Python

You're reading from   Applying Math with Python Practical recipes for solving computational math problems using Python programming and its libraries

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781838989750
Length 358 pages
Edition 1st Edition
Languages
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Authors (2):
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Sam Morley Sam Morley
Author Profile Icon Sam Morley
Sam Morley
Sam Morley Sam Morley
Author Profile Icon Sam Morley
Sam Morley
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Table of Contents (12) Chapters Close

Preface 1. Basic Packages, Functions, and Concepts 2. Mathematical Plotting with Matplotlib FREE CHAPTER 3. Calculus and Differential Equations 4. Working with Randomness and Probability 5. Working with Trees and Networks 6. Working with Data and Statistics 7. Regression and Forecasting 8. Geometric Problems 9. Finding Optimal Solutions 10. Miscellaneous Topics 11. 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...

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