To successfully complete secondary education, persistent learning behavior is essential. Why are some adolescents more resilient to setbacks at school than others? In addition to actual ability, students’ implicit beliefs about the nature of their abilities have major impact on their motivation and achievements. Ability beliefs range from viewing abilities as “entities” that cannot be improved much by effort (entity beliefs), to believing that they are incremental with effort and time (incremental beliefs). Importantly, ability beliefs shape which goals a student pursues at school; proving themselves (performance goals) or improving themselves (learning goals). The central aims of the proposal are to unravel 1) the underlying processing mechanisms of how beliefs and goals shape resilience to setbacks at school and 2) how to influence these mechanisms to stimulate persistent learning behavior. Functional brain research, including my own, has revealed the profound top-down influence of goals on selective information processing. Goals may thus determine which learning-related information is attended. Project 1 jointly investigates the essential psychological and neurobiological processes to unravel the longitudinal effects of beliefs and goals on how the brain prioritizes information during learning, and how this relates to school outcomes. Project 2 reveals how to influence this interplay with the aim to long-lastingly stimulate persistent learning behavior. I will move beyond existing approaches by introducing a novel intervention in which students experience their own learning-related brain activity and its malleability. The results will demonstrate how ability beliefs and goals shape functional brain development and school outcomes during adolescence, and how we can optimally stimulate this interplay. The research has high scientific impact as it bridges multiple disciplines and thereby provides a strong impulse to the emerging field of educational neuroscience.

Policymakers and governmental institutions rely every day on data to make important decisions that impact citizens’ quality of life. For instance, the availability of large amounts of time series data observed in many dimensions and levels of granularity requires the development of new techniques to model and draw conclusions from such complex systems. Multivariate time series data structures typically deal with time series and cross-sectional dimensions. Nowadays, much more complex data structures have appeared, requiring mathematical objects defined in higher dimensions and beyond continuous-valued measurements; e.g. time series of counts related to several types of crimes occurring in different cities. The final object is a three-dimensional data set of counts called a discrete tensor. This research proposal introduces new empirical, econometric models to describe and study such discrete tensor data. Although in social sciences many tensor data possess a discrete structure, the statistical theory for discrete tensors is still under development. These specific data cannot be accommodated by existing tensor models which are specifically tailored for continuous variables and are typically described by simple linear patterns. The goal of the project is to fill the existing gap in the literature by developing new models for discrete-valued tensor data that possess flexible cross-sectional and serial dependence structures. To this aim, we combine a multivariate copula framework for count data and non-linear score-driven models with the existing tensor literature; As a result, we obtain a new model class, called Integer-valued Matrix Autoregressive Score model (IMARS). We then successfully apply these new models to relevant empirical problems in key areas of interest to policymakers, like crime data, allowing to understand the distribution of different types of crimes across geographical areas, forecast their incidence and study spillover effects over a space-time grid.

Natural disasters cause substantial economic losses, which are expected to increase due to climate change. The insurance sector plays a central role in managing these risks. It is thus imperative that insurers accurately assess and address climate risks. Risk assessment methods currently fall short in accurately understanding climate risks because they focus on single hazards (e.g. floods) instead of correlated multi-hazard risks (e.g. windstorms and floods) affected by climate change. They also insufficiently account for human climate adaptation processes leading to risk reduction, which are characterized by bounded rationality and social interaction. Furthermore, additional insights are needed into how to design insurance arrangements that address climate change and stimulate policyholders to adapt to multi-hazard climate risks by limiting natural disaster damage. This project aims to design insurance arrangements to enhance policyholders’ adaptation to multi-hazard climate risks by combining financial coverage with comprehensive strategies for stimulating risk reduction. Its interdisciplinary approach goes beyond the state-of-the-art by creating novel multi-hazard climate risk assessments for insurance and by developing agent-based models (ABMs) that account for boundedly rational human adaptation decisions and social interaction. The ABMs evaluate natural disaster insurance arrangements for EU countries with diverse risk contexts and assess strategies for stimulating adaptation by policyholders, based on input from behavioural economic studies. Repeated and real-time surveys and economic experiments will identify comprehensive strategies for triggering behavioural change to stimulate adaptation to the multitude of climate change risks individuals face. These strategies linked to (public–private) insurance combine communication on risk and risk reduction measures, insurance incentives (e.g. deductibles, premium discounts), and nudges.

The forming of social bonds is an evolutionary imperative, and a rich target for empirical research. Social scientists have scrutinized the structure of the elaborate social networks that characterize today’s society. Neuroscientists have elucidated the brain mechanisms underlying our ability to navigate this social world. Yet, these research lines have been largely separated. This proposal aims to integrate social network research and social brain research, focusing on adolescence as the most dynamic phase shaping the interplay between social networks and the social brain. Social development in adolescents is clearly driven by maturation of specific social-cognitive functions; yet these functions are manifest in, and moulded by, interpersonal relationships within social networks. I aim to clarify how changes in the social brain relate to changes in social network position and structure during adolescent development. This can be achieved by using the quantitative tools of social network analysis in conjunction with the experimental approach of social neuroscience. I plan to investigate a cohort of approximately 1000 adolescents nested in 50 classes in a longitudinal design with 6 measurements over 3 years; fMRI investigating task-related functional activation and connectivity is conducted yearly in a subsample of 100. The neural and behavioural correlates of social cognition are investigated using experimental tasks tapping i) understanding others and ii) interacting with others; social behaviour is charted through ecological momentary assessment techniques; social networks are mapped using surveys and digital information acquired routinely via mobile phones (mobile sensing). This approach clarifies how during a crucial developmental phase the social brain shapes the social environment, and vice versa, the social environment influences maturation of the social brain.

Mathematics is a human activity, and as for all human activities ethical considerations arise. However, so far the ethics of mathematics has remained an under-explored topic both among philosophers and mathematicians. Because mathematics is a collective epistemic endeavour injustices in its social structure, such as biased refereeing practices, impact its epistemic output. These injustices have not yet received sustained critical reflection, even though they are both socially relevant, since they impact the careers and hence lives of mathematicians, and epistemologically relevant, because they shape publicly available mathematical knowledge. VaViM will study the virtues and vices that manifest in such injustices and develop an interventionist philosophy which supports concrete recommendations, such as policy advice. VaViM expands the theoretical frameworks provided by the virtue-theoretic literature to a study of cases of injustices in mathematical practices. This empirically informed philosophy will provide detailed investigations of how virtues (e.g. charity) and vices (e.g. egotism) manifest in mathematical knowledge-making. This will reveal points of connection between the ethics and epistemology of mathematical practices and open up a discursive field for philosophers and mathematicians to engage with the ethics of mathematics. As a European centre of excellence in socially relevant philosophy the VU Amsterdam is the perfect host for VaViM. The shared philosophical interest in the sciences ensures the two-way transfer of knowledge between VaViM and its host and provides ample opportunity for collaboration. The VU’s expertise with public philosophy and its research networks provide excellent means for dissemination for VaViM’s findings on the societal challenges mathematicians are facing in Europe in a changing world. Through VaViM I will enrich the European Research Area as a pioneer of a socially and epistemologically relevant philosophy of mathematics.