The study of interacting networks of agents is an important area of research in computer science, applied mathematics, biology and the social sciences. The focus of this project is on group learning where agents update their beliefs based on evidence available to them and the opinions of others within their social network. Such models can incorporate the influence of opinion leaders, competing groups and sources of misinformation.
The project will use agent-based models to investigate how group learning takes place in various social contexts.
The research will seek to identify optimal strategies for group learning when
This will be achieved by carrying out extensive computer simulations to understand the dynamics of the mathematical models under investigation. In light of recent debates about the influence of conflicting sources of information / misinformation and the role of social media in the context of democratic elections and COVID-19, as well as the relevance of group learning to economics, applications in one or more of these areas will also be considered.
Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.
We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.
In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University offers the following levels of support:
The following scholarship options are available to applicants worldwide:
These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).
This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies. Further information on cost of living
References
S. Chen, D.H. Glass, M. McCartney (2016) Characteristics of successful opinion leaders in a bounded confidence model, Physica A 449, 426–436.
S. Chen, D.H. Glass, M. McCartney (2020) How opinion leaders affect others on seeking truth in a bounded confidence model, Symmetry 12(8), 1362. I.
Douven (2019) Optimizing group learning: An evolutionary computing approach, Artificial Intelligence, 275, 235–251. I.
Douven, R. Hegselmann (2021) Mis- and disinformation in a bounded confidence model, Artificial Intelligence, 291, 103415. C.A.
Glass, D.H. Glass (2020) Social influence of competing groups and leaders in opinion dynamics, Computational Economics http://dx.doi.org/10.1007/s10614-020-10049-7.
C.A. Glass, D.H. Glass (2021) Opinion Dynamics of Social Learning with a Conflicting Source, Physica A, 563(14), 125480.
Submission deadline
Friday 5 February 2021
12:00AM
Interview Date
Week Beginning 22nd March 2021
Preferred student start date
Mid-September 2021
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