Social network learning: Uncertainty, heterogeneity, and the application in principal–agent relationships

This paper employs algebraic transformation to describe complex social network learning (SNL) behaviors under continuous expected payoff. Three distinct algorithms are then introduced that factor in uncertainty and heterogeneity. We find that individuals' strategies tend to converge through SNL...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Mathematical methods in the applied sciences 2024-04, Vol.47 (6), p.4697-4733
Hauptverfasser: Hong, Yilin, Ding, Chuan, Liu, Peng
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper employs algebraic transformation to describe complex social network learning (SNL) behaviors under continuous expected payoff. Three distinct algorithms are then introduced that factor in uncertainty and heterogeneity. We find that individuals' strategies tend to converge through SNL. We then construct a framework for studying the convergence process in the principal–agent relationship by applying our SNL algorithms to distinct scenarios. Our results show that network topology plays a significant role in changes in the payoffs and the convergence speed of individuals' strategies. We also evaluate the impacts of uncertainty, heterogeneity, agents' output efficiency and risk aversion, and individual's centrality on the effectiveness of SNL.
ISSN:0170-4214
1099-1476
DOI:10.1002/mma.9834