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Nash Equilibrium In Game Theory ~xRay Pixy

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 Video Link  CLICK HERE... Learn Nash Equilibrium In Game Theory Step-By-Step Using Examples. Video Chapters: Nash Equilibrium  00:00 Introduction 00:19 Topics Covered 00:33 Nash Equilibrium  01:55 Example 1  02:30 Example 2 04:46 Game Core Elements 06:41 Types of Game Strategies 06:55  Prisoner’s Dilemma  07:17  Prisoner’s Dilemma Example 3 09:16 Dominated Strategy  10:56 Applications 11:34 Conclusion The Nash Equilibrium is a concept in game theory that describes a situation where no player can benefit by changing their strategy while the other players keep their strategies unchanged.  No player can increase their payoff by changing their choice alone while others keep theirs the same. Example : If Chrysler, Ford, and GM each choose their production levels so that no company can make more money by changing their choice, it’s a Nash Equilibrium Prisoner’s Dilemma : Two criminals are arrested and interrogated separately. Each has two ...
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Dwarf Mongoose Optimization Algorithm || Step-By-Step || ~xRay Pixy

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Video Chapters: Dwarf Mongoose Optimization Algorithm 00:00 Introduction 02:10 About Mongoose 03:55 Mongoose Communication 05:00 Mongoose Lifestyle 07:23 Dwarf Mongoose Optimization Algorithm Steps 11:29 Optimization Process Start 16:00 Conclusion New Metaheuristic Optimization Algorithm Dwarf Mongoose Optimization We can use this algorithm to solve different optimization problems as when this algorithm is tested on different continuous, discrete optimization problems it provides efficient results. So, we can use this algorithm to solve complex optimization problems This algorithm is basically inspired by the foraging behavior of mongooses in real life. Dwarf Mongoose Optimizer is introduced in 2022 by Jeffrey and all. It is a swarm intelligence-based optimization algorithm that we can use to solve complex optimization problems. This algorithm provides efficient results in comparison with seven different algorithms as you can see here Particle Swarm Optimizer, Gray wolf O...
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