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Strategic use of payoff information in k-hop evolutionary Best-shot networked public goods game.

Authors :
Jin, Xing
Tao, Yuchen
Wang, Jingrui
Wang, Chao
Wang, Yongheng
Zhang, Zhouyang
Wang, Zhen
Source :
Applied Mathematics & Computation. Dec2023, Vol. 459, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

Globalization has led to increasingly interconnected interactions among individuals. Their payoffs are affected by the investment decision of themselves and their neighbors, which will cause conflicting interests between individual and social investment. Such problems can be modeled as a networked public goods game (NPGG). In this paper, we study the Best-shot NPGG model by introducing three mechanisms: k -hop, payoff information use strategy, and access cost. We use evolutionary game theory and present the k -hop evolutionary Best-shot networked public goods game (k -EBNPG) to explore the impact of these three mechanisms on social welfare. The results show that social welfare will increase with a diminishing margin as k increases while introducing the payoff information use strategy can significantly improve social welfare when k > 1. Finally, we study the impact of access cost on social welfare and surprisingly find that social welfare will achieve the highest when the access cost is half the investment cost. • We propose the k -hop evolutionary Best-shot networked public goods game (k -EBNPG) model. • We delineate the evolutionary process into learning and accessing stages. • The social welfare will increase with a diminishing margin as k increases. • The social welfare will achieve the highest when the access cost is half the investment cost. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00963003
Volume :
459
Database :
Academic Search Index
Journal :
Applied Mathematics & Computation
Publication Type :
Academic Journal
Accession number :
170721732
Full Text :
https://doi.org/10.1016/j.amc.2023.128271