The occurrence of therapeutic resistance is a major cause for the small effect on overall survival showed by targeted cancer therapies. Whilst experimental strategies to evaluate available treatments have been faced by an ever increasing number of possible combinations, computational approaches have been challenged by the lack of a framework able to model the multiple interactions encompassed by the three major factors affecting therapeutic resistance: selection of resistant clones, adaptability of gene signalling networks, and a protective and hypoxic tumour microenvironment. Here I propose a novel modelling framework, Agent-Based Modelling of Gene Networks, which brings together powerful computational modelling techniques and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict therapeutic resistance and guide effective treatment selection. Using triple negative breast cancer (TNBC) as a testing case (15% of breast cancers, lacks validated), I propose to: 1. Develop a computational model of the TNBC tumour microenvironment using in-vitro and in-vivo, including patient-derived, models and data from clinical samples. 2. Validate the ability of the model to predict driver genes conferring a survival advantage to cancer cells in a hypoxic microenvironment. 3. Predict combinations of druggable targets to tackle TNBC therapeutic resistance. 4. Select most effective drug combinations and validate pre-clinically. This project will deliver pre-clinically validated drug combinations, new therapeutic targets and a virtual environment to study individual tumours and predict therapeutic resistance. Complementing and empowering experimental models and assays, microC will offer a new powerful tool for diagnosis and therapy.

Model uncertainty is a key issue and an active research area in Macro-Finance. Its study, pioneered by Hansen and Sargent, substantially improves the treatment of uncertainty in Macro-Finance models and the robustness of their conclusions. The interest of central banks on problems related to model uncertainty is a clear signal of the relevance of this novel concept and the related theoretical framework. Model uncertainty in Macro-Finance and ambiguity in Decision Theory share a common insight that inspires empirical and theoretical developments: the agents’ ignorance about the “true” probabilistic model that governs the uncertain environments they face. With few exceptions, decision theorists have studied ambiguity mostly in static contexts that are insufficient for the analysis of the steady state and dynamic decision problems that characterize Macro-Finance. Hence, this field keeps relying on decision models that cannot cope with model uncertainty. Our research agenda aims to create a unified Macro-Finance and Decision Theory framework for the study of model uncertainty, which broadens the scope of Decision Theory and provides novel foundations for a common framework. We will build new steady state and dynamic decision models that are powerful enough for a general analysis of model uncertainty in Macro-Finance. We will also develop a self-confirming equilibrium analysis, which by leaving room for agents to have “wrong” views about models, can much more naturally confront agents with model uncertainty than the rational expectations approach. Our project will foster cross-fertilization and lead to a deeper understanding of the empirical and theoretical effects of uncertainty in Macro-Finance phenomena. Because model uncertainty is pervasive (e.g., which climate model to use? which is the correct production function for human capital?), we expect that our theoretical findings will push the research frontier and the analysis of the role of uncertainty in other fields.

Why is it so difficult to achieve further European political integration? This question motivates the first part of the project. The standard approach in economics presumes that integration of countries reflects a tradeoff between economic benefits and the cost of cultural heterogeneity. To assess this tradeoff, we exploit survey data to quantify cultural heterogeneity within and between EU countries, comparing it to the US. We also investigate time variation, to assess whether economic integration led to cultural convergence. Finally, exploiting regional variation, we seek to identify a cultural core and compare it to the economic core of the EU. We conjecture the following conclusion: although European economic integration has not led to cultural convergence, the primary obstacle to integration is not cultural heterogeneity per se, but the presence of other barriers, such as national identities or national institutions, which amplify its effects. The second part of the project studies the causes and implications of two related phenomena: the diffusion of nationalism and of political populism, with behavioral voters. We study nationalism as endogenous identification with one’s nation, and analyze how it interacts with political institutions and political processes in a setting of international policy coordination. We study populism as due to the reaction of disappointed voters who behave according to Prospect theory. Our main goal is to explain these behavioral phenomena, and to derive predictions about the effect of institutional reforms. The third part of the project examines Europe in the very long run. It studies the formation of clusters of creative élites within Europe, in a historical perspective. The main goal is to explain how local self-government institutions and the migration of upper tail human capital between different European cities contributed to the formation of clusters of innovation and creativity in the XI-XIX centuries.