Help with shared constraints (game theory)

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SUMMARY

This discussion focuses on solving shared constraints in non-cooperative games involving two players. Player 1 aims to maximize the function f(x,y) while player 2 maximizes g(x,y), subject to the constraint x+y ≤ a. The recommended approach involves forming a Lagrangian for each player: L1 = f(x,y) + λ(a - x - y) for player 1 and L2 = g(x,y) + λ(a - x - y) for player 2, where λ is the shared Lagrange multiplier. The next step is to solve the first-order conditions (FOC) to find optimal strategies.

PREREQUISITES
  • Understanding of non-cooperative game theory
  • Familiarity with Lagrangian optimization techniques
  • Knowledge of first-order conditions (FOC)
  • Basic concepts of shared constraints in mathematical optimization
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  • Study the application of Lagrangian multipliers in game theory
  • Explore advanced topics in non-cooperative game theory
  • Learn about specific forms of functions f(x,y) and g(x,y) for linearity
  • Investigate alternative methods for solving shared constraint problems
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Game theorists, mathematicians, and economists interested in optimizing strategies in non-cooperative games with shared constraints.

ruzbayhhi
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I've been struggling with shared constraints problems for a while now. I have a game between two players with a shared constraint. For example, player 1 is trying to maximize f(x,y) by choosing x, and player 2 is trying to maximize g(x,y) by choosing y. The players are competing in a non-cooperative game.
There is a shared constraint such that x+y<= a.

If I understand the source below correctly (p. 18 onward), what I should do is form a Lagrangian: L1 = f(x,y)+λ(a-x-y) and L2=g(x,y)+λ(a-x-y)? (i.e., λ for player 1 is the same as for player 2) and then solve the FOC? Is this the right way to approach this?

Reference: http://www.dis.uniroma1.it/~facchinei/didattica/giochi/Lettura_3.pdf
 
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It is one way to approach it since both share this constraint. There might be other approaches if ##f,g## are known, i.e. have a certain form as linearity.
 

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