Computational Intelligence Method for Bargaining Problems with Outside Options

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Bargaining is fundamental in social activities. Game-theoretic method is ideal for providing theoretic solutions for simplistic models. Game-theoretic method always requires substantial human efforts in order to solve game-theoretic equilibriums. The analytic complexity increases rapidly when more determinants are considered. We attempt to use an alternative method: computational intelligence, in particular evolutionary algorithms to cope with the increased complexity due to the increase of determinants.

In this paper, we study a relatively complicated bargaining model, in which outside option and discount factor are taken into account. Empirical studies demonstrate that evolutionary algorithms are efficient in finding near-perfect solutions. Experimental results reflect the compound effects of discount factors and outside options upon bargaining outcomes. We argue that evolutionary algorithm is a practically alternative tool for generating reasonably good strategies for fairly complicated bargaining models.

Keywords: Bargaining Problems, Evolutionary Computation
Stream: Economics and Management, Technology and Applied Sciences, Research Methodologies, Quantitative and Qualitative Methods
Presentation Type: Paper Presentation in English
Paper: A paper has not yet been submitted.

Dr Nanlin Jin

Research Fellow, School of Geography, University of Leeds
Leeds, UK

I am working for Rural Economy & Land Use - Sustainable Uplands Project
My main responsibilities in this project are:

1. Establish social-economic models to simulate involvers’ decision-making;

2. Study farmers’ adaptive learning to dynamic environments;

3. Develop Agent-based model (ABM) system;

4. Integrate the social-economic model with the Biophysics model & Biodiversity model;

5. Support visualization and information retrieval for the Integrated System
Research Interests

Ref: I08P0769