Collaboration in networks with randomly chosen agents

Zhiwei Cui; Rui Wang

doi:10.1016/j.jebo.2016.06.015

Collaboration in networks with randomly chosen agents

Zhiwei Cui^*
, Rui Wang

^*Corresponding author for this work

School of Computer Science and Engineering

Beihang University

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

The present paper considers a finite population of agents located in an arbitrary, fixed network. In each period, a small proportion of agents are randomly chosen to play a minimum effort game. They learn from both their own and their neighbors’ experiences and imitate the most successful choices, though they may occasionally make mistakes. We show that in the long run all agents will choose the highest effort level provided that each agent's neighborhood is large.

Original language	English
Pages (from-to)	129-141
Number of pages	13
Journal	Journal of Economic Behavior and Organization
Volume	129
DOIs	https://doi.org/10.1016/j.jebo.2016.06.015
State	Published - 1 Sep 2016

Keywords

Imitation
Local observation
Minimum effort game
Rare interaction

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10.1016/j.jebo.2016.06.015

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title = "Collaboration in networks with randomly chosen agents",

abstract = "The present paper considers a finite population of agents located in an arbitrary, fixed network. In each period, a small proportion of agents are randomly chosen to play a minimum effort game. They learn from both their own and their neighbors{\textquoteright} experiences and imitate the most successful choices, though they may occasionally make mistakes. We show that in the long run all agents will choose the highest effort level provided that each agent's neighborhood is large.",

keywords = "Imitation, Local observation, Minimum effort game, Rare interaction",

author = "Zhiwei Cui and Rui Wang",

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year = "2016",

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AU - Cui, Zhiwei

AU - Wang, Rui

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AB - The present paper considers a finite population of agents located in an arbitrary, fixed network. In each period, a small proportion of agents are randomly chosen to play a minimum effort game. They learn from both their own and their neighbors’ experiences and imitate the most successful choices, though they may occasionally make mistakes. We show that in the long run all agents will choose the highest effort level provided that each agent's neighborhood is large.

KW - Imitation

KW - Local observation

KW - Minimum effort game

KW - Rare interaction

UR - https://www.scopus.com/pages/publications/84978539173

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DO - 10.1016/j.jebo.2016.06.015

M3 - 文章

AN - SCOPUS:84978539173

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VL - 129

SP - 129

EP - 141

JO - Journal of Economic Behavior and Organization

JF - Journal of Economic Behavior and Organization

ER -

Collaboration in networks with randomly chosen agents

Abstract

Keywords

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