Behavioral finance emerged as a response to the limitations of classical financial theory, revealing that investor behavior is shaped by cognitive biases, heuristics, and emotional influences rather than pure rationality. The rapid diffusion of artificial intelligence (AI) across financial systems now challenges this foundation, introducing new dynamics in how decisions are made, information is processed, and markets evolve. This paper examines the theoretical implications of AI for behavioral finance, proposing that algorithmic systems act not merely as analytical tools but as active participants in financial cognition. Through concepts such as computational rationality, algorithmic irrationality, and algorithmic nudges, the study explores how technology both mitigates and amplifies human biases, creating hybrid decision environments where psychological and algorithmic factors interact. The framework extends the Adaptive Market Hypothesis (AMH) to an AI-driven context, conceptualizing market efficiency as a co-evolutionary process shaped by human learning and machine adaptation. While AI enhances information assimilation and decision precision, the convergence of similar algorithmic models introduces new systemic risks, including volatility, feedback loops, and loss of market diversity. By integrating insights from behavioral economics, cognitive psychology, and computational finance, this paper advances a revised theoretical model of financial behavior in the algorithmic era. It argues that understanding the cognitive-algorithmic interface is essential for scholars, practitioners, and regulators seeking to interpret market dynamics in increasingly automated and data-driven environments.

This article examines the prospective Association Agreement between the Republic of San Marino and the European Union, focusing on the economic sustainability of regulatory convergence in a microstate context. While alignment with the EU acquis has strengthened supervisory credibility and financial stability, it has also imposed sizable fixed compliance costs on a small and highly open financial system. The article asks whether, and under what conditions, these costs can be offset by integration-related benefits once effective market access is activated. Thus, the article pursues two objectives. First, it provides a legal and institutional analysis of the reforms undertaken by San Marino in the financial sector as part of EU regulatory alignment. Second, it develops and applies an indicator-based quantitative framework that links regulatory compliance costs to potential market-access benefits. Using quarterly system-level balance-sheet data from the Central Bank of San Marino, the framework constructs a Regulatory Compliance Index (RCI), a Market Benefit Index (MBI), and their difference, the Integration Gap Index (IGI). At the end of the sample (2025Q2), the IGI appears to be positive, but moderate (approximately 0.08), indicating that compliance costs still exceed integration benefits. Scenario-based projections show that closing this gap within eight quarters requires an average quarterly increase in the MBI of about 0.01, a threshold already exceeded by recent observed dynamics (around 0.017–0.018). Under conservative and baseline scenarios, the IGI converges toward zero within two years, while an optimistic scenario implies faster convergence. The results suggest that regulatory convergence can be economically sustainable for San Marino, provided that effective EU market access materializes. The proposed framework intends to be transparent, replicable, and applicable to other microstates engaged in differentiated integration with the European Union.

This study investigates the relationship between public healthcare funding and healthcare infrastructure development in a universalistic (Beveridge-type) healthcare system. Focusing on Italy over the period 2000–2023, the analysis compares the public and accredited private healthcare sectors by examining annual changes in the number of hospitals and available beds, used as indicators of healthcare infrastructure capacity. A robust Seemingly Unrelated Regression (SUR) model is employed, together with a dummy variable capturing the effects of the COVID-19 pandemic. Given the observational nature of the data, the empirical analysis is intended to identify statistical associations rather than establish causal effects. The findings indicate that increases in public funding are positively associated with changes in the number of public hospitals, whereas no statistically significant relationship emerges between public funding and changes in the number of accredited private hospitals. These results suggest that public funding is more closely associated with healthcare infrastructure development in the public sector than in the accredited private sector. Furthermore, the COVID-19 pandemic is significantly associated with changes in public healthcare infrastructure, reflecting the exceptional pressures placed on healthcare systems during the emergency period. Although the estimated relationships should be interpreted as descriptive associations rather than causal effects, the findings provide evidence that public healthcare funding is systematically related to key indicators of healthcare infrastructure development within the Italian National Health Service. Overall, the results highlight the importance of examining not only the amount of public resources allocated to healthcare but also the mechanisms through which those resources are distributed and utilized. Strengthening monitoring and evaluation mechanisms may therefore contribute to improving the effectiveness, transparency, and accountability of public resource allocation across healthcare providers.