مطالب

When analyzing the histories of nations, it becomes clear that times of crisis often give rise to various figures who position themselves as problem solvers. Among these, authoritarian leaders are particularly notable. While certain aspects of their childhoods are frequently cited as significant, the geopolitical contexts and global shifts during their rise to power are equally crucial. These leaders typically emerge in countries experiencing similar external pressures, regional conflicts, or transformations in the international balance of power. By examining these shared geopolitical circumstances alongside personal histories, we gain valuable insight into the emergence and appeal of such figures during turbulent periods. This study identifies eight widely recognized cases from 1919 to 2011, including Adolf Hitler, Manuel Noriega, Idi Amin, Muammar Gaddafi, Benito Mussolini, Saddam Hussein, Pol Pot, and Tojo Hideki. Using a descriptive, analytical, and comparative approach, it seeks to answer the question: how do authoritarian figures exploit geopolitical environments to consolidate and expand their power? The findings suggest that such leaders often use ongoing crises to legitimize their actions, manipulate public sentiment, and push territorial or ideological agendas. To enhance accuracy and pattern recognition, artificial intelligence (AI) techniques were employed in the analysis phase. These tools helped reveal recurring behavioral and geopolitical patterns that may otherwise remain unnoticed. The study emphasizes the importance of identifying these dynamics early to prevent future international crises.

Several research investigations have indicated that asset returns exhibit notable skewness and kurtosis, which have a substantial impact on the utility function of investors. Additionally, it has been observed that Average Value-at-Risk (AVaR) provides a more accurate estimation of risk compared to variance. This study focuses on the computational challenge associated with portfolio optimization in an uncertain context, employing the Mean-AVaR-skewness-kurtosis paradigm.The uncertainty around the total return is con-sidered and analyzed in the context of the challenge of selecting an optimal portfolio. The concepts of Value-at-Risk (VaR), Average Value-at-Risk (AVaR), skewness, and kurtosis are initially introduced to describe uncertain variables. These concepts are then further explored to identify and analyse relevant aspects within specific distributions. The outcomes of this study will convert the existing models into deterministic forms and uncertain mean-AVaR-skewness-kurtosis optimization models for portfolio selection. These models are designed to cater to the demands of investors and mitigate their apprehensions.

Despite the growth of financial technologies (FinTechs) and their impact on transforming financial services, the implementation of fintech for gaining a competitive advantage in banks appears essential. The aim of this article is to model and determine the relationships and relative importance of the dimensions and components of implementing fintech in the financial-banking system. To this end, the research was conducted with a qualitative and quantitative approach in 2023. The research community included 12 experts, comprising managers and senior specialists from state banks, who were selected purposefully and theoretically. The data collection tools were semi-structured interviews for the qualitative section and a pairwise comparison questionnaire for the quantitative section. The results of the qualitative section, analysed through content analysis, indicated that the model includes dimensions such as "infrastructure and technical", "organizational policies", "products and services", "market", "marketing and sales", "economic", "human resources" and "ecosystem." The quantitative results, analysed using fuzzy DEMATEL, showed that "products and services" is the most influential and critical dimension in the model, suggesting that the success of implementing the model depends on improving this dimension. "Ecosystem" is the most impactful dimension with the strongest relationship in implementing the model, leading to the model's improvement. The findings of the network analysis process also indicated that the components of "product and service diversity", "security and privacy protection", "product and service adaptability" and "creating a platform and sharing it" are the most important. Based on the results, it is recommended that banks invest in those financial technologies (fintech) that can quickly respond to the desires and needs of their customers for successful fintech implementation.