Job description
Department of Economics
Full-Time, Fixed Term (for 36 months)
Applications are invited for the post of Postdoctoral Research Assistant in the Department of Economics.
We are looking for a highly motivated individual to join the research project Social Mechanisms and Allocations led by the Principal Investigator (PI), Professor David Levine.
The main goal of this project is to study social mechanisms and their allocations. Social norms are crucial for groups to succeed: from political parties, to labour unions, to special interest groups, even to criminal gangs. Each of these groups faces a free-rider problem: it is in the interest of each individual in the group to let the others do the work of advancing the common interest. Norms are equally pivotal in persuading individuals not to engage in anti-social behaviour, from littering, to thievery, and worse, where again a divide appears between individual goals and larger interests.
The project aims to link work in game theory to the operation of social norms in a heterogenous agents’ framework. The project will involve economists, psychologists, and computer scientists at Royal Holloway to understand how social norms develop and sustain themselves. The project will explain and illustrate how a willingness of individuals to bear small costs for the sake of society can be leveraged into system-wide institutional changes. The results will address both large questions that have defined the modern era – how can individuals be convinced to obey social rules that are not normally in their self-interest to follow? – and smaller puzzles of how institutions can better function. Levine does not foresee one-size-fits-all solutions. The norms that support some groups such as volunteer organizations providing health assistance to the poor promote the greater good, while in other cases, such as with special interest lobbying organizations, they can do harm. Public policies should therefore aim to foster the formation of useful social norms in some cases and to transform existing norms in other cases.
Levine’s earlier work on dynamic games has laid out the rules in economics for when self-interested agents will sacrifice current benefits for longer-term gains, provides the theoretical bridge to the present project. The team will explore more concretely how agents who interact dynamically come to learn norms and how they build productive norms in the first place. In the last decades, there has been emerging interest in algorithms to make predictions and decisions in financial markets. The project intends to add a new dimension by studying the interaction between machines and humans.
To answer these questions, a considerable innovation of the project is the use of economic experiments along with algorithmic simulations.
The project has both theoretical and empirical focusses along with subject experiments. Proven research ability with experimental economics is desirable as is knowledge of Python or related languages. Moreover, data management skills and a high level of competence in econometrics and machine learning are desired, as would some experience with working with economic theory. Most of all, the successful candidate will have a great enthusiasm to conduct research on this topic.
This is a project funded by the Leverhulme Trust. The successful candidate will work 1/3 time directly on the project and 2/3 of the time on individual grant related research.
The successful candidate will work closely with the PI, Professor David Levine (Economics), and the research team including: Professors Michael Mandler (Economics), Francesco Feri (Economics), Kostas Stathis (Computer Science) and Ryan McKay (Psychology). They are expected to engage as a full member of the research team contributing to the theoretical and/or empirical analysis of social norms and economic institutions, using theoretical models, simulations, and designing and coding economic experiments, analysing causal relations, assisting with the development of the project’s output, including the preparation of outputs for journal submission.
The successful candidate will possess as many as possible of the following:
- PhD (or near completion of) in relevant subject area (economics, computer science, mathematics / statistics, psychology, or related);
- Training in using key software, including Python, at advanced level is a plus;
- Demonstrable comprehensive experience of research in heterogenous agent modelling;
- Designing economic experiments, setting up and coding economic experiment using major online platforms;
- Experience of coding complex structural estimations, simulations;
- Experience with constructing research funding proposals;
- Ability to keep accurate records, strong communication skills, experience with interacting with research users and with researchers from related disciplines, and ability to work independently under regular supervision as well as within a team setting.
The post is based in Egham, Surrey, where the College is situated in a beautiful, leafy campus near to Windsor Great Park and within commuting distance from London. Meetings with the team will take place at Royal Holloway. It is expected that the successful candidate will work on campus Economic experiments will be carried out in a dedicated lab of the Egham Campus. There might be the possibility to use facilities in Central London based on project requirements.
For queries on the application process the Human Resources Department can be contacted by email at: [email protected]
Please quote the reference: 0123-003-R
Closing Date: 23:59, 11 April 2023
Interview Date: To be confirmed
This position is not eligible for hybrid working.
Royal Holloway is committed to equality, diversity and inclusion (EDI), and encourages applications from all people regardless of age, disability, gender, marital status, parental status, race, religion or belief, sexual orientation, or trans status or history. More information on our structures and initiatives around EDI, including information on staff diversity networks, can be found on our Equality and Diversity Intranet page.