An ERC Starting Grant is a personal grant of about €1.5 million and provides support to talented researchers for a period of five years.
Serious mental illnesses – such as depression, bipolar and psychotic disorders – are a major cause of suffering worldwide and disproportionately affect people of non-European descent. In addition, mental illnesses are also frequently accompanied by comorbid health conditions. The two most important co-morbidities are substance abuse and cardiovascular disease. Whether these are due to causal relationships is unclear. The causal direction is also not yet clear: do comorbidities lead to comorbidities and/or do comorbidities increase the risk of mental illness? Jorien Treur, an expert in the field of epidemiological and genetic causal research methods, will bring together innovative approaches in her project to unravel the relationship between mental illness, substance abuse and cardiovascular disease.
When people hear about ‘algorithmic decision-making’, they typically think of a computer system and the data sets it calculates. In reality, all algorithmic decisions are made in collaboration with humans: it is us who create them, evaluate them, follow their outcomes or deviate from them. Van Voorst’s project is an anthropological study of the collaboration between humans and algorithmic systems in the field of global public health – a field of unprecedented growth in datafication and automation. In six country cases, the project will look at how doctors, programmers and algorithms make decisions together, for example in the fields of DNA genetic research or preventive health care.
Generalisations are fundamental to every scientific discipline: ‘Every cell has a plasma membrane’, ‘Every electron has a negative charge’, ‘Every natural number has a unique successor’. They are the building blocks of virtually every scientific theory, and therefore essential to understanding, explaining and making predictions. The most basic and best understood form of generalisation is generalisation about objects (e.g. cells, electrons, numbers). But many theoretical contexts require generalisation into sentence and predicate positions, a high-level form of generalisation where we make a general statement about a class of statements (e.g. mathematical induction, laws of logic). There are two competing methods for achieving this form of generality (i.e. higher-order logic and self-applicable theories of truth, properties and sets, respectively). As both methods come with their own ideological and ontological commitments, it makes a substantial difference which one is chosen as the framework for formulating our mathematical, scientific and philosophical theories. Some research has been done in this direction but it is still very much in its early stages. This research project will provide the first sustained systematic investigation of the two methods from a unified perspective and develop novel formal tools to articulate deductively strong theories.