In this paper, we apply an Evolutionary Algo- rithm (EA) to solve the Rubinstein's Basic Alternating- Offer Bargaining Problem, and compare our experi- mental results with its analytic game-theoretic solution. The application of EA employs an alternative set of assumptions on the players' behaviors. Experimental outcomes suggest that the applied co-evolutionary algo- rithm, one of Evolutionary Algorithms, is able to gener- ate convincing approximations of the theoretic solutions. The major advantages of EA over the game-theoretic analysis are its flexibility and ease of application to vari- ants of Rubinstein Bargaining Problems and compli- cated bargaining situations for which theoretic solutions are unavailable.
|Publication status||Published - 2005|
|Event||IEEE Symposium on Computational Intelligence and Games - Essex University|
Duration: 1 Jan 2005 → …
|Conference||IEEE Symposium on Computational Intelligence and Games|
|Period||1/01/05 → …|