FELICITAS revolutionizes the way we think about preferences, skills, and other latent personal attributes (PSAs), estimates their heterogeneity along with its determinants, and provides policy implications. PSAs are key drivers of a myriad of decisions which combine to create an individual’s life story. Differences in PSAs, together with constraints and luck, underlie inequalities in outcomes. Knowledge of PSAs is essential for policymakers to design effective public policy. Self-knowledge of PSAs is crucial for individuals to sort into occupations, activities, and relationships which enable them to flourish. However, unobserved PSAs are only noisily revealed by observed behavior. I develop a decision model which separately identifies noise due to imperfect self-knowledge and endogenous effort. I quantify their respective roles in different choice settings, de-bias estimates of PSAs, and assess their importance in essential life outcomes. I use these insights, along with an innovative discrete choice framework in which respondents choose between pairs of realistic life stories, to provide causal estimates of distributions in preferences (valuations) for policy-relevant life outcomes (longevity, health, family structure) in the United States and in Europe. I link the estimated heterogeneity to culture, demographics, and other PSAs. Finally, I examine a new latent personal attribute - an individual's propensity to perceive time in a distorted manner. In a twist to received wisdom that time flows faster when one is engaged in a more enjoyable activity, I propose that utility obtained from an activity can be inferred using differences between “felt” and elapsed time. I conjecture that the perceived duration of a task may both be the relevant decision variable, which reflects an individual's exerted effort on a task, and a determinant of required hourly wages. If empirically validated, we obtain a cardinal measure of utility which will transform its measurement.

My ERC-CoG project has advanced the methodological frontier in solving and estimating macroeconomic equilibrium models, enabling analysis of the interplay between business cycles and inequality. To transform this innovation into a benefit for society, policymakers must be able to work with models with ease. This will allow them to comprehend how the policies they propose impact both the macroeconomy and income/wealth distribution. For this purpose, policymakers require a software tool that allows for swift development and analysis of models. The ERC-CoG-funded research has produced a pilot software that can theoretically achieve this, alongside fundamental methodological research. However, it lacks the versatility and user-friendliness required by policymakers who must develop answers to policy questions under time pressure. The aims of the BASEforHANK project include the further development of the pilot software into a more user-friendly and versatile tool. If successful, this development will enable policymakers worldwide to understand the impacts of their policies better, resulting in more socially cohesive and stable growth. The development follows the model of the Dynare software package, which made it possible to use macroeconomic equilibrium models with representative agents that are now pervasive in policy institutions.

Medieval Islamic Archaeology in Southern Greater Syria is relevant to the sustainable agricultural intensification discourse due the implementation of state intensive land use for profit that affected the lives of peasants and the ecology of semi-arid regions. Medieval Islamic agricultural intensification practices included irrigation, fertilizers, overgrazing, deforestation and cash cropping, which increase agricultural yields. The project “Human Landscapes: agricultural intensification and peasant resilience in medieval Southern Greater Syria” (HumanLand), offers a deep-time perspective on these issues. According to surveys and historic resources, the later medieval Islamic eras were marked by the collapse of the Mamluk state, the decline of major agricultural centers, and a shift to a seasonal basis occupation partly due to climatic stressors of the end of the 14th and 15th centuries, as well as the lack of state resources and support. HumanLand will use new environmental and textual data for medieval land-use to investigate these three objectives: 1) To identify changes in agricultural intensification in relation to climatic, political and economic shifts that took place in the late 14th and much of the 15th centuries, 2) To investigate the use of sustainable strategies and impact of imperial regimes on medieval communities, and 3) To investigate the impact of political structures on the ecology in semi-arid regions. The project will use botanical micro remains (phytolith, starches and spherulites) and stable isotope data from crop remains to understand agricultural practices of six medieval communities of Jordan and Israel. This evidence will be evaluated with information on land-use in medieval texts. These methods combined, provide evidence for intensified agriculture making this research one of the few studies done, which combine the different approaches proposed to approach the big picture of medieval Islamic agricultural history.

Visual data association aims to find task-specific mappings involving visual data. Two significant examples are the mapping of physics models to complex scenes for planning overtaking manoeuvrers in autonomous driving, or matching collections of 3D shapes for medical analysis. Despite the high relevance of visual data association, its progress has not kept pace with the revolutionary developments fuelled by recent deep learning advances: existing data association machinery lacks theoretical guarantees (e.g. global optimality, or structure such as geometric consistency in 3D shape matching) that are critical for high-stakes settings, or suffers from poor scalability. Moreover, current procedures fall short of understanding complex interconnections across different observable entities (collections of e.g. objects or scenes). The vision of Harmony is to tackle these shortcomings by harmonising the complex interconnections between observable entities and underlying fundamental principles (e.g. geometry, or physics). This research direction is challenging, largely unexplored and will require to break substantially new ground at conceptual, algorithmic and practical levels simultaneously. Harmony is organised into four complementary challenges: Challenge A addresses global optimality and scalability for 3D shape matching; Challenge B addresses structure and dynamics inference from static images; Challenge C addresses non-linear synchronisation in data collections defined over graphs; Challenge D will exploit synergies and cross-fertilise insights across Harmony. Overall, Harmony will benefit both researchers and practitioners by providing solutions to more complex tasks in practically relevant settings (e.g. geometrically consistent medical shape analysis, or physics-based scene understanding).

Quantum field theory (QFT) is the formalism that underlies modern particle and condensed matter physics. Standard perturbative methods in QFT have been extraordinarily successful in explaining physical phenomena involving weakly-interacting quantum fields. On the other hand many fundamental phenomena, including phase transitions and nuclear interactions, are described by strongly coupled QFTs for which perturbative techniques are insufficient and a rigorous, predictive theoretical formulation is lacking. Heuristic arguments indicate that a full non-perturbative formulation of QFT must include extended degrees of freedom (a prototypical example being the flux tubes that bind quarks inside the nucleus). My proposal describes a novel approach for studying extended objects in a wide range of QFTs, based on two recent conceptual breakthroughs: first, my research on a special class of theories (the six-dimensional SCFTs) has brought to light a rich algebraic structure that captures the properties of its stringlike excitations; and second, new developments in mathematics and physics point to the existence of a vast generalization of this structure, which is perfectly suited to describe the extended objects of a much wider range of QFTs. This program is organized along three directions: analyze the families of QFTs that can be studied by string-theoretic and geometric methods, and gradually uncover the algebraic structures that describe their extended degrees of freedom; exploit these algebraic structures to obtain novel principles that govern the dynamics of strongly-interacting QFTs; and determine the new mathematical structures that arise from the combination of the geometric and algebraic description of the extended objects. An ERC starting grant will allow me to undertake this ambitious project whose pursuit will lead to a much deeper understanding of extended degrees of freedom, or their role in QFT, and of the mathematical structures that describe them.