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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 ...

Invasive Weed Optimization (IWO) Algorithm Step-by-Step with Numerical E...

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Invasive Weed Optimization (IWO) Algorithm with Example Invasive Weed Optimization The invasive weed optimization algorithm (IWO) is a population-based metaheuristic optimization method inspired by the behavior of weed colonies. Weeds are u nwanted plants (plant in the wrong place). Weeds can change their behavior according to the environment and gets fitter. Weeds plant can be easily found in: Parks, Fields, Garden, and Lawns Invasive Weed Optimization Algorithm Steps. 1.) Initialization Phase Initialize all important parameters. 2.) Initialize Population. The initial population is created by spreading the finite number of seeds randomly in the search space. 3.) Compute Fitness Values.  Every seed will grow into a flowering plant and produce seeds. [Reproduction].  Seed production is based on fitness values so compute: Individual Fitness Value Best Fitness Value Worst Fitness Value 4.) Random distribution of germinated seeds. Determine new positions of seeds in the search sp...
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