Find out more about this MPhil/PhD opportunity, including how to apply by clicking the headings below to reveal more information.
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People who achieve significant power run the risk of succumbing to its proverbially corrupting tendencies and developing dangerous, ultimately destructive, behaviours. The striking uniformity of this phenomenon led Owen and Davidson (2009) to formulate it in psychiatric terms as a ‘disorder of power’, which they termed the Hubris syndrome (HS).
This PhD will employ knowledge graph methodology to represent the multiple factors (including genetic, developmental, educational, behavioural and experiential) and their interplay, which influence powerful people’s development or avoidance of HS.
Machine learning will be used to identify the factors that predispose to or protect from this destructive behavioural phenomenon.
The aim of the project is to create an integrated representation of all data relevant to the tenure of all forms of power. The density and interconnectedness of this novel representational format - a knowledge graph (KG) - means that artificial intelligence techniques can be used to characterise the complex of factors responsible for the development of leadership Hubris, as well as to deduce new information, generate predictions and test hypotheses about its origins and mechanism.
The student will identify a sample of individuals who held power across different occupational categories in the 20th Century. Personal and biographical characteristics, data relevant to changes in behaviour and language, and the specific events preceding these changes will be included. Data will be harvested from online information resources, and from print and broadcast media. Features relevant to the representation of power will include developmental and educational history, behavioural traits, professional experience, and documented behaviours. Markers of behaviour at different time points will be based on documented descriptions, on language used in speeches or other official communication, activity on social media and/or in the press, books and arti les.
The classification of individuals as having displayed HS (or not) during their tenure of power will be based on changes of behaviour meeting Owen and Davidson’s criteria and/or the occurrence of damaging outcomes from their decisions.
The student will acquire or develop skills in data engineering
(building and populating a ‘Hubris knowledge graph’), machine learning
(interrogating the knowledge graph and quantifying the extent to which the model can accurately predict the development of HS), and leadership theory
(the classification and behavioural correlates of different types of leadership across multiple domains, including those of politics, business, military and professional).
The student will develop or acquire his/her skills in computer programming in order to achieve the project’s objectives.
Applicants should normally have an upper second-class honours degree (2:1) of a UK university or an overseas qualification of an equivalent standard obtained after a course of study extending over not less than 3 years in a university (or educational institution of university rank), in a subject appropriate to that of the course to be followed.
The successful applicant will receive a stipend for a three-year period and will have their fees paid for them during this time.
The studentship is open to both Home and Overseas students. However, please note that, if the successful candidate is overseas, they will be required to fund the difference between the Home fee rate and the Overseas fee rate.
For 21/22 the Home rate is £4,486 and the Overseas rate is £25,750 (we do not yet have the figures for later years).
Please send a completed application form (Word) to email@example.com by 5pm on Wednesday 1 December 2021.
An equal opportunities form (Word) should also be submitted as a separate document.
You should ask your referees to complete the reference form (Word) and submit them to the same email address by the same deadline, if possible.
Shortlisting and interviews
Applications will undergo shortlisting and successful applicants will then be invited to interview on the afternoon of Tuesday 7 December 2021 and is currently planned to be held online.
The successful candidate will be given a verbal offer and once it has been accepted, will be sent a formal offer letter and a registration pack with joining information.
Unsuccessful candidates will be contacted with their outcomes at the earliest opportunity and will be able to request feedback if required.