IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Norm Emergence with Biased Agents

Norm Emergence with Biased Agents
View Sample PDF
Author(s): Partha Mukherjee (University of Tulsa, USA), Sandip Sen (University of Tulsa, USA) and Stephane Airiau (University of Amsterdam, The Netherlands)
Copyright: 2009
Volume: 1
Issue: 2
Pages: 14
Source title: International Journal of Agent Technologies and Systems (IJATS)
DOI: 10.4018/jats.2009040105

Purchase

View Norm Emergence with Biased Agents on the publisher's website for pricing and purchasing information.

Abstract

Effective norms can significantly enhance performance of individual agents and agent societies. We consider individual agents that repeatedly interact over instances of a given scenario. Each interaction is framed as a stage game where multiple action combinations yield the same optimal payoff. An agent learns to play the game over repeated interactions with multiple, unknown, agents. The key research question is to find out whether a consistent norm emerges when all agents are learning at the same time. In real-life, agents may have pre-formed biases or preferences which may hinder or even preclude norm emergence. We study the success and speed of norm emergence when different subsets of the population have different initial biases. In particular we characterize the relative speed of norm emergence under varying biases and the success of majority/minority groups in enforcing their biases on the rest of the population given different bias strengths.

Related Content

Pinki Sharma, Jyotsna Sengupta, P. K. Suri. © 2019. 17 pages.
Sukhdev Singh, Chander Kant. © 2019. 7 pages.
Sahil Chhabra, Neeraj Kumar Jain, Vipin Tomar. © 2019. 9 pages.
Alankrita Aggarwal, Deepak Chatha. © 2019. 10 pages.
Smiley Gupta, Jagtar Singh. © 2019. 10 pages.
Meenakshi Sharma, Sonia Thind. © 2019. 10 pages.
Jimmy Singla. © 2019. 9 pages.
Body Bottom