Project description
Understanding complex nonlinear phenomena in the social sciences
In cross-sectional research, variables like the inclination to share fake news are closely linked with human perception, creating intricate patterns that defy linear analyses. Longitudinal studies, tracking the nonstationary nature of temporal social processes, further compound the issue. Conventional methods do not capture this complexity. The ERC-funded NONLINEARSCIENCE project, a pioneering initiative, leverages Bayesian Gaussian processes to address this. The project proposes flexible nonparametric methodologies, allowing researchers to grasp complex nonlinear shapes, integrate prior knowledge and test theories. New user-friendly software will ensure broad accessibility, with tailored extensions for diverse data types. NONLINEARSCIENCE will empower scientists to comprehend intricate nonlinear mechanisms, track temporal evolution and make accurate predictions in the dynamic landscape of social science.
Objective
Nonlinearity is ubiquitous in the social sciences. In cross-sectional research, nonlinearity naturally follows from the fact that variables often depend on human perception. The tendency to share fake news, for example, depends in a complex nonlinear manner on peoples’ personality and political preferences. In longitudinal research, nonlinearity follows from the fact that temporal social processes are nonstationary by nature. For instance, stressful life events (e.g. unemployment, pandemic) have a complex nonlinear impact on well-being over time. To study these nonlinear phenomena, much more data are needed than in linear analyses. Therefore, researchers increasingly rely on technological innovations to collect rich data, such as panel data via online surveys, experience sampling data via mobile apps, or temporal social network data using digital communication (e.g. email). In addition, prior information (e.g. from experts) is often available to inform us about plausible nonlinear shapes. A crucial problem is however that statistical approaches for learning nonlinearity still heavily rely on old-fashioned techniques which can only model simple (curvilinear) effects and are unable to include external prior information. Our understanding about nonlinear phenomena therefore remains limited. This project aims to resolve these shortcomings by developing cutting-edge methods for nonlinear social science using Bayesian Gaussian processes. With this nonparametric methodology, we can learn complex nonlinear shapes, add prior knowledge, and test nonlinear theories. Implementation in user-friendly software will ensure general utilization. Tailor-made extensions will be developed for cross-sectional data, panel data, experience sampling data, and temporal social network data. After this project, we will be able to truly understand complex nonlinear mechanisms, to learn how these unfold over time, and to make accurate predictions (e.g. of well-being after life events).
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been validated by the project's team.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been validated by the project's team.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2022-COG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
5037 AB Tilburg
Netherlands
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