Computational Social Science (BSc, Hons) @ University College Dublin
What is Computational Social Science
Computational Social Science (CSS) is a field concerned with understanding how social systems behave when they are treated as data generating processes. It’s multidisciplinary, sitting at the intersection of sociology, economics, statistics, mathematics, and computer science, but it is not a simple blend of those disciplines. Its core aim is not prediction for its own sake, but responsible modelling using data and computation to analyse power, incentives, institutions, and behaviour, while remaining explicit about assumptions, limits, and bias.
CSS treats models as arguments with data being something produced by social and institutional systems rather than neutral facts, and computation as a tool that is always embedded in political and social context. The emphasis is on explanation, accountability, and interpretability, especially in domains where models influence real decisions.
My Training Within CSS
Within this framework, my own training has been anchored in sociology and economics, with computation and statistics serving those lenses rather than replacing them. Sociological theory grounds my understanding of power, inequality, race, migration, surveillance, and institutions, while economics provides structured models of incentives, markets, labour, growth, games, and policy trade offs.
Computation, statistics, and mathematics were treated as enabling tools rather than ends in themselves. This meant learning to code, model, and analyse, not to optimise benchmarks, but to test claims, stress assumptions, and make social systems legible without stripping away their complexity. As a result, I’d say that my undergrad academic profile is best described as applied economic and sociological research with strong computational fluency, rather than generic data science or pure theory.
In actual practicality, this means working with large, messy datasets, building transparent econometric and computational models, testing institutional or policy claims against data, and communicating results clearly to non technical or policy stakeholders.
What This Trained Me to Do - Explicitly - in bullet points
- Frame social and economic questions precisely - for effective modelling.
- Choose appropriate methods (statistical, computational, or theoretical) rather than defaulting to a single tool.
- Work with messy, biased, real world data and document its limitations.
- Interpret quantitative results through institutional and historical context.
- Communicate technical findings clearly to technical and nontechnical audiences alike.
Where My Interests Now Sit and Looking Forward
Over the course of the programme, my interests gradually settled and became more concrete. I found myself caring less about various modelling techniques in isolation and more about how they are taken up inside institutions.
Learning across econometrics, labour markets, growth, surveillance, migration, and computational modelling painted a very clear picture. Technical systems rarely stay technical. They become administrative tools, policy inputs, or administrative defaults. At that point questions of uncertainty, incentives, and governance matter as much as accuracy.
This orientation, with my startup tech background (amongst other reasons), has led me to concentrate on AI safety and governance as a primary area of interest.