Climate policy and legislation has a significant influence on both domestic and global responses to the pressing environmental challenges of our time. The effectiveness of such climate legislation is closely tied to the complex dynamics among elected officials, a dynamic significantly shaped by the relentless efforts of lobbying. This project aims to develop a novel compartmental model to forecast the trajectory of climate legislation within the United States. By understanding the dynamics surrounding floor votes, the ramifications of lobbying, and the flow of campaign donations within the chambers of the U.S. Congress, we aim to validate our model through a comprehensive case study of the American Clean Energy and Security Act (ACESA). Our model adeptly captures the nonlinear dynamics among diverse legislative factions, including centrists, ardent supporters, and vocal opponents of the bill, culminating in a rich dynamics of final voting outcomes. We conduct a stability analysis of the model, estimating parameters from public lobbying records and a robust body of existing literature. The numerical verification against the pivotal 2009 ACESA vote, alongside contemporary research, underscores the models promising potential as a tool to understand the dynamics of climate lobbying. We also analyse the pathways of the model that aims to guide future legislative endeavors in the pursuit of effective climate action.

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macro-economic factors (MEF), including an inflation metric (CPI), US treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine Learning allows for the discrimination of two periods within the dataset. Unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models' relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties.

In this paper we analyze how market prices change in response to information processing among the market participants and how non-linear information dynamics drive market price movement. We analyze historical data of the SP 500 market for the period 1950 -2025 using the logistic Continuous Wavelet Transformation method. This approach allows us to identify various patterns in market dynamics. These patterns are conceptualized using a new theory of reflexive communication of information in a market consisting of heterogeneous agents who assign meaning to information from different perspectives. This allows us to describe market dynamics and make forecasts of its development using the most general mechanisms of information circulation within the content-free approach.

This paper investigates the relationship between job mobility and earnings growth in the UK labour market, with a focus on gender differences in the returns to switching jobs. Using data from the Annual Survey of Hours and Earnings (ASHE) between 2011 and 2023, the analysis compares wage progression for job switchers and stayers, controlling for individual and job characteristics. The findings show that job mobility is associated with higher earnings growth, but women experience smaller gains than men, with occupational mobility and age further widening this gap. However, the study finds no statistically significant evidence that changes in occupation, sector, or working time pattern influence this gender gap. The results highlight the importance of addressing gender disparities in the returns to job mobility and provide valuable evidence for developing policy interventions aimed at promoting more equitable labour market outcomes.

This research examines the empowerment of women entrepreneurs in the context of entrepreneurial financing in France. It explores the factors that allow some women entrepreneurs to access certain categories of external finance more easily. The theoretical framework used is based on the concept of empowerment, explored through its personal and relational dimensions. The study relies on a quantitative approach, using data from a representative of women entrepreneurs. The results show that the status of a founder affects access to external finance in different ways: it increases the chances of successful fundraising, but reduces the chances of obtaining bank finance. This finding highlights the importance of empowerment dynamics, which vary according to the type of financing. In addition, characteristics such as the presence of a spouse in the business, high income, membership of a professional network and the diversity of this network complete the analysis of inequalities in access. This study, the first of its kind in France, suggests ways of enriching our understanding of the diversity of situations experienced by female founders, thus helping to deconstruct the homogeneous image of women's entrepreneurship.

Dynamic input-output models are standard tools for understanding inter-industry dependencies and how economies respond to shocks like disasters and pandemics. However, traditional approaches often assume fixed prices, limiting their ability to capture realistic economic behavior. Here, we introduce an adaptive extension to dynamic input-output recovery models where producers respond to shocks through simultaneous price and quantity adjustments. Our framework preserves the economic constraints of the Leontief input-output model while converging towards equilibrium configurations based on sector-specific behavioral parameters. When applied to input-output data, the model allows us to compute behavioral metrics indicating whether specific sectors predominantly favor price or quantity adjustments. Using the World Input-Output Database, we identify strong, consistent regional and sector-specific behavioral patterns. These findings provide insights into how different regions employ distinct strategies to manage shocks, thereby influencing economic resilience and recovery dynamics.

Portfolio optimization involves selecting asset weights to minimize a risk-reward objective, such as the portfolio variance in the classical minimum-variance framework. Sparse portfolio selection extends this by imposing a cardinality constraint: only $k$ assets from a universe of $p$ may be included. The standard approach models this problem as a mixed-integer quadratic program and relies on commercial solvers to find the optimal solution. However, the computational costs of such methods increase exponentially with $k$ and $p$, making them too slow for problems of even moderate size. We propose a fast and scalable gradient-based approach that transforms the combinatorial sparse selection problem into a constrained continuous optimization task via Boolean relaxation, while preserving equivalence with the original problem on the set of binary points. Our algorithm employs a tunable parameter that transmutes the auxiliary objective from a convex to a concave function. This allows a stable convex starting point, followed by a controlled path toward a sparse binary solution as the tuning parameter increases and the objective moves toward concavity. In practice, our method matches commercial solvers in asset selection for most instances and, in rare instances, the solution differs by a few assets whilst showing a negligible error in portfolio variance.

For many economic questions, the empirical results are not interesting unless they are strong. For these questions, theorizing before the results are known is not always optimal. Instead, the optimal sequencing of theory and empirics trades off a ``Darwinian Learning'' effect from theorizing first with a ``Statistical Learning'' effect from examining the data first. This short paper formalizes the tradeoff in a Bayesian model. In the modern era of mature economic theory and enormous datasets, I argue that post hoc theorizing is typically optimal.

The study of probabilistic models for the analysis of complex networks represents a flourishing research field. Among the former, Exponential Random Graphs (ERGs) have gained increasing attention over the years. So far, only linear ERGs have been extensively employed to gain insight into the structural organisation of real-world complex networks. None, however, is capable of accounting for the variance of the empirical degree distribution. To this aim, non-linear ERGs must be considered. After showing that the usual mean-field approximation forces the degree-corrected version of the two-star model to degenerate, we define a fitness-induced variant of it. Such a `softened' model is capable of reproducing the sample variance, while retaining the explanatory power of its linear counterpart, within a purely canonical framework.