Individual decisions can have a large impact on society as a whole. This is obvious for political decisions, but still true for small, daily decisions made by common citizens.
Individuals decide how to vote, whether or not to stay at home when they feel sick, to drive or to take the bus. In isolation, these individual decisions have a negligible social outcome, but collectively they determine the results of an election and the start of an epidemic. For many years, studying these processes was limited to observing the outcomes or to analysing small samples. New data sources and data analysis tools have made it possible to start studying the behaviour of large numbers of individuals, enabling the emergence of large-scale quantitative social research.
At the Data Science and Policy (S&P) group we are interested in understanding these decision-making events, expecting that this deeper knowledge will lead to a better understanding of human nature, and to improved public decisions.
In the past, we have been focusing mainly on two types of problems, both strongly dependent on both the behaviours of individuals (in what we call bottom-up collective processes), and of decision-makers (the top-down decisions).
The first is related with what we usually identify as political debate and deliberation and we try to answer questions such as:
- How are political or policy decisions made?
- What drives them?
- What are their impacts?
The second is disease dynamics, of both infections and non-infectious diseases, and we try to answer questions such as:
- Can we improve nowcasting and forecasting of several diseases?
- Can we reduce antibiotic over-prescription?
Despite the applied nature of the questions, we are also particularly interested in the underlying principles, both mathematical and behavioural, that can be generalized to different contexts.