مطالب

The social contract theory, as one of the important foundations of modern political philosophy, is based on the will and consent of humans in establishing political power. In contrast, the political thought of Imam Khomeini seeks the origin of legitimacy in the divine will. This study, with an interpretive approach and based on Skinner's hermeneutics, deals with the historical description of the development of the social contract theory and the context and time of the formation of the political thought of the Imam Khomeini in order to answer the question of how the components of the social contract are formulated in the political thought of the Imam Khomeini. The aim of the study is to clarify what kind of connection can be established between the thought of Imam Khomeini and the social contract theory. The findings of the study show that although at the functional level, concepts such as law, majority vote, and accountability in the thought of the Imam Khomeini play a role similar to the concepts of the social contract in the political organization of modern societies, at the basic and ultimate levels, it presents a new formulation of the social contract that is defined in conjunction with the divine covenant.

This study explores the role of cultural diplomacy in Al-Farabi’s political philosophy, specifically the strategic function of orators and poets in his “virtuous city.” By analyzing Farabi’s theory of the imaginative faculty (al-quwwah al-mutakhayyilah), the research argues that these groups are central to governance, serving as intermediaries who translate intellectual truths into sensory and persuasive forms. The study posits that Farabi’s framework offers a pre-modern model of cultural diplomacy—not as mere artistic exchange, but as an epistemological process for communicating rational truth across communities, thereby integrating aesthetics into the structure of ideal governance.

Credit card fraud remains a significant threat to financial institutions and the integrity of digital payment systems, posing challenges for both operational risk management and regulatory oversight. This paper presents a novel hybrid machine learning framework for credit card fraud detection that combines Principal Component Analysis (PCA) for feature extraction with a Support Vector Machine (SVM) based feature selection mechanism. The aim is to reduce dimensionality while retaining the most informative features, thereby improving detection performance on highly imbalanced transaction datasets. The approach is evaluated on a large credit card transactions dataset, where PCA is first used to transform the input variables into principal components capturing the majority of variance, and an SVM with recursive feature elimination is then employed to identify and retain the most relevant components. Experimental results demonstrate that the proposed PCA-SVM pipeline significantly outperforms baseline models lacking this hybrid feature engineering: for example, it achieves a higher fraud recall (detection rate) by several percentage points while maintaining high precision, leading to improved F1-scores and overall accuracy. These findings indicate that the hybrid method effectively mitigates class imbalance issues and eliminates redundant features, yielding a more compact and robust fraud detection model. By enhancing the identification of rare fraudulent transactions without excessive false alarms, our study contributes to central bank objectives in fraud risk management. The proposed framework can strengthen the resilience of digital payment infrastructures and support payment system oversight, ultimately helping to safeguard financial stability and public trust in electronic payment channels.

Blockchain technology, due to its prominent features such as information transparency, high security, data immutability, and the ability to eliminate intermediaries, has the potential to transform the banking industry. By creating a distributed ledger, this technology enables secure, fast transactions without the need for central authorities, which can reduce operational costs and improve the efficiency of banking systems. This research was conducted to identify and determine the significance of critical success factors of blockchain technology in the banking industry, aiming to help banks effectively leverage this emerging technology. First, critical success factors were identified through a comprehensive literature review, and then they were screened using the fuzzy Delphi technique. Subsequently, the importance of each factor was calculated using the Bayesian best-worst method, a multi-criteria group decision-making approach. The findings indicated that high scalability and fast transaction processing speed, security and standardization of blockchain technology, and interoperability with existing systems are among the most critical success factors. Finally, based on the results and with the goal of improving the efficiency and security of banking systems, practical and research recommendations were provided.

This paper investigates the breakdown of a money-demand relationship derived from the Quantity Theory of Money for Iran during episodes of large fluctuations in the real value of oil exports. The joint rise in the inverse velocity of money and the real value of oil exports in the 1970s and 2000s suggests that conventional specifications fail to capture the oil-driven component of money demand. This issue is crucial for monetary policy, because the central bank must distinguish whether increases in desired real money balances stem from supply-side expansions in the transaction base or from shifts in money demand in order to calibrate an appropriate policy response. We hypothesise that real GDP is an inadequate proxy for the supply of goods and services in the Quantity Theory of Money framework, because swings in natural-resource rents from oil are largely excluded from real GDP growth even though, in practice, oil rents finance additional absorption and effective supply. To address this, we examine the impact of both the real price and the volume of oil on the inverse velocity of money using linear ARDL and nonlinear NARDL models. Using these models, we find a long-run level relationship between inverse velocity, the real oil price, and oil production, with positive long-run effects and a significant error-correction term. Asymmetry is concentrated in the short run for oil prices: positive shocks raise inverse velocity more than negative shocks reduce it. For oil production, increases lift inverse velocity while declines are muted, consistent with fiscal smoothing, monetization, and offsetting macroeconomic adjustments under sanctions. Policy should therefore be explicitly oil-contingent, separating oil-driven shifts in money demand from policy-induced balance-sheet expansion, and relying more on quantity instruments combined with fiscal and foreign-exchange sterilization during oil booms.