A Neural Model of Bilateral Negotiation Consisting of One and Two Issues

Abstract

This thesis demonstrates that neural technology may be successfully employed to mimic some of the thought processes of a negotiator during a bilateral negotiation. Using the constraint satisfaction paradigm, originally developed to explore parallel distributed processing, a neural network is proposed to simulate the thought process of a buyer who negotiates the purchase of a good based on price and quality. The findings on this thesis suggest that continued research in neural networks to replicate the mental model of the negotiator holds great promise. The ability to model true beliefs and evaluation methods has an advantage over more traditionally prescriptive models. The neural network model allows incorporation of human irrationally and provides an ability to assess how that irrationality affects the negotiation outcome.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1991
Accession Number
ADA246819

Entities

People

  • Neil B. Strand

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Agreements
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Character Recognition
  • Computers
  • Content Addressable Memory
  • Expert Systems
  • Game Theory
  • Information Systems
  • Machine Learning
  • Negotiations
  • Network Architecture
  • Neural Networks
  • Personality
  • Systems Engineering
  • Training

Readers

  • International Relations and European Studies
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks