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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 ...

Grasshopper Optimization Algorithm (G.O.A.) Step-by-Step with Numerical ...

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Grasshopper Optimization Algorithm (G.O.A.)  Grasshoppers are also known as pests. They destroy fields and crop production. Grasshopper lifecycle contains Eggs, Nymph Phases, and Adult Grasshopper. Grasshopper Optimization Algorithm is a Nature-inspired swarm-based optimization algorithm. Grasshopper Optimization Algorithm (GOA) is inspired by the foraging and swarming behavior of grasshoppers in nature. The grasshopper optimization algorithm is basically inspired by the behavior of adult grasshoppers in nature. Adult grasshoppers can make sudden jumps and cover long-range as compare to nymphs. This is the mathematical model used to represent grasshopper behavior in this algorithm : 𝑥_𝑖 = 𝑆_𝑖 + 𝐺_𝑖 + 𝐴_i GrasshopperCurrentPosition = Social Interaction in the group + Force of gravity + Wind Direction. Normally distributed random values are used in the grasshopper optimization algorithm for grasshopper random behavior in nature. Grasshopper Optimization Algorithm Steps. 1.) Pa...
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