The centralization of verification power within pools affects blockchain security. We propose a game-theoretic model to compare the incentives of pools versus individual verifiers in both Proof-of-Work (PoW) and Proof-of-Stake (PoS). Pool centralization affects security by diminishing the security advantage of PoS over PoW and increases majority attack risks in both systems due to lower per-block profits compared to verifiers, yet it enhances resilience by sustaining high attack costs during market downturns. In general, expected post-attack value losses align pool actions with stability. Supported by numerical illustrations, our research underscores how incentives for pools determine blockchain security, offering practical insights.

This paper proposes a methodological architecture to integrate Q-learning into economic modelling systematically. It addresses a common gap: the lack of a shared framework linking economic foundations to Reinforcement Learning components. Rather than introducing a new algorithm, it specifies and reports how preferences, frictions, information structures, and time horizons map to the reward function, discount factor, and learning environment design. Equilibrium outcomes serve as benchmarks for comparing learned policies, not as imposed axioms. This approach interprets learning dynamics through standard economic categories and enables comparability across studies. The architecture organizes models along explicit dimensions: behavioural preferences, institutional frictions, economic environment class, information structure, learning and exploration mechanisms, and evaluation metrics. A simulation illustrates how variations in frictions, risk attitudes, and intertemporal preferences affect learned policies, their stability, and their relationship to static benchmarks. The paper aims to promote the cumulative use of Reinforcement Learning in applied economics by providing a general specification that improves interpretability, comparability, and reproducibility, turning deviations from theoretical equilibria into measurable diagnostics that refine economic fundamentals.

Background Generalized trust is a central component of social cohesion and development, yet its determinants remain diverse and contested. While much of the literature examines trust as a driver of growth, this study explores the structural, institutional, and behavioral conditions that enable trust to emerge. Methods This study examines three domains of generalized trust—trust in most people (TMP), trust in people met for the first time (TFT), and trust in neighbors (TN)—using an integrative framework that combines macro- and micro-level variables from the World Values Survey (2018–2022). The analysis covers 97,200 respondents across a diverse set of countries. Applying probit and ordered probit models, we incorporate structural factors such as national wealth, inequality, and institutional stability, alongside individual characteristics related to income, education, religiosity, and tolerance. Results Findings show that higher GDP per capita, national educational development, and personal income consistently increase trust across all domains. Conversely, income inequality and urbanization tend to erode trust. Tolerance emerges as a consistently positive predictor. A nuanced “religiosity paradox” is observed: higher religious importance shifts trust toward closer social circles (TN) while reducing openness to strangers (TFT). Furthermore, while national educational development fosters trust, individual schooling often fosters more cautious attitudes. Conclusions Trust is a multicausal construct shaped by the intersection of economic security, institutional legitimacy, and inclusive social norms. Strengthening generalized trust requires public policies that foster inclusive growth, equitable education, and civic values of openness and tolerance. These results offer one of the most comprehensive cross-national examinations of generalized trust to date, highlighting the importance of extending trust beyond familiar networks.