Analysing the effects of social influence on decision making

UoM administered thesis: Phd

  • Authors:
  • Lei Ni

Abstract

In recent years, researchers have shown an increased interest in monitoring and quantifying social influence in practical decision-making processes. Many of them focus on influence identification and maximisation in social networks with much reliance on social networking data. However, the common features in decision making, that is, the presence of uncertainties and multiple criteria, have not been considered in previous studies when investigating socially influenced decision making. Given the lack of a framework that jointly captures social influence in decision making, uncertainty and multiple criteria, this thesis tackles this gap in a two-stage process. Firstly, a belief-based model is proposed to quantitatively characterise the effects of social influence when individuals make decisions under uncertainty. Belief functions are used in order to represent individuals’ uncertainties toward the decision. Meanwhile, social influence is regarded as a source of decision information with uncertainty; hence, the decision-making process is characterised as a series of evidential reasoning by individuals after they receive any influence from social neighbours. By doing so, the evolving process of individuals’ decisions under uncertainty in the context of influence spreading in social networks is captured in a quantitative way. Secondly, a model that considers the effects of social influence on individuals’ decisions with multiple criteria is proposed. Specifically, this thesis investigates how social influence can be characterised in the context of multiple criteria decision analysis (MCDA), where voluntary vaccination decision making is selected as a case study. An integrated model is proposed, in which an individual’s subjective judgements on the decision criteria of vaccine uptake are formulated in the framework of MCDA, while the spread of social influence is further incorporated in the decision-making process by using the evidential reasoning approach. Finally, this thesis further explores the possibility of considering social influence from a macro/global perspective in the specific context of vaccination in England; specifically, the interplay between internal migration and overall vaccination behaviours. As social networks play a central role in information dissemination and opinion formation, any change in local communities is likely to cause variations in group behaviours. Thus, considering that migration can explicitly lead to changes in population structure, and therefore the social networks of multiple individuals, a regression analysis is performed in order to identify the level of association that migration can have in the resulting vaccination coverage rate of different local authorities across England. The result indicates a significant association of internal migration with vaccination behaviours. The contributions of this thesis to existing studies are: 1) it takes into account the characteristics of the decision-making environments, including uncertainties and multiple criteria associated with decision problems when analysing the effects of social influence on decision making; 2) it provides new insights into socially influenced vaccination decision by formulating individuals’ decisions in the context of MCDA framework and incorporating social influence into the framework; 3) it analyses socially influenced vaccination decision from macro/global behaviours and identifies the association between internal migration and vaccination behaviours. In the future work, this thesis can be linked with epidemic models in order to characterise the interplay between disease spreading and socially influenced vaccination behaviours in consideration that vaccination impedes disease transmission while disease severity is a significant criterion for vaccination decisions. Also, in terms of the proposed belief-based models, different evidence combination rules can be considered to explore their efficacy in characterising the uncertainties existing in the evaluation of both decision criteria and social influence.

Details

Original languageEnglish
Awarding Institution
Supervisors/Advisors
Award date1 Aug 2020