Information cascade on networks
Masato Hisakado, Shintaro Mori

TL;DR
This paper investigates how different network structures influence voting behavior and information cascades, revealing that network topology affects convergence speed but not the fundamental phase transition of information cascades.
Contribution
It introduces a voting model with herders and independents on various networks, analyzing phase transitions and showing limited impact of network hubs on information cascades.
Findings
Information cascade transition depends on herder strength.
Network topology affects convergence speed, not cascade occurrence.
Hubs have limited influence on voters' perceptions.
Abstract
In this paper, we discuss a voting model by considering three different kinds of networks: a random graph, the Barab\'{a}si-Albert(BA) model, and a fitness model. A voting model represents the way in which public perceptions are conveyed to voters. Our voting model is constructed by using two types of voters--herders and independents--and two candidates. Independents conduct voting based on their fundamental values; on the other hand, herders base their voting on the number of previous votes. Hence, herders vote for the majority candidates and obtain information relating to previous votes from their networks. We discuss the difference between the phases on which the networks depend. Two kinds of phase transitions, an information cascade transition and a super-normal transition, were identified. The first of these is a transition between a state in which most voters make the correct…
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