Hasil untuk "Political institutions and public administration - Asia (Asian studies only)"

Jieying Chen, Karen de Jong, Andreas Poole et al.

As large language models (LLMs) become deeply embedded in digital platforms and decision-making systems, concerns about their political biases have grown. While substantial work has examined social biases such as gender and race, systematic studies of political bias remain limited, despite their direct societal impact. This paper introduces a general methodology for constructing political bias benchmarks by aligning model-generated voting predictions with verified parliamentary voting records. We instantiate this methodology in three national case studies: PoliBiasNL (2,701 Dutch parliamentary motions and votes from 15 political parties), PoliBiasNO (10,584 motions and votes from 9 Norwegian parties), and PoliBiasES (2,480 motions and votes from 10 Spanish parties). Across these benchmarks, we assess ideological tendencies and political entity bias in LLM behavior. As part of our evaluation framework, we also propose a method to visualize the ideology of LLMs and political parties in a shared two-dimensional CHES (Chapel Hill Expert Survey) space by linking their voting-based positions to the CHES dimensions, enabling direct and interpretable comparisons between models and real-world political actors. Our experiments reveal fine-grained ideological distinctions: state-of-the-art LLMs consistently display left-leaning or centrist tendencies, alongside clear negative biases toward right-conservative parties. These findings highlight the value of transparent, cross-national evaluation grounded in real parliamentary behavior for understanding and auditing political bias in modern LLMs.

Amir Asiaee, Kaveh Aryan

Boosting algorithms enjoy strong theoretical guarantees: when weak learners maintain positive edge, AdaBoost achieves geometric decrease of exponential loss. We study how to incorporate group fairness constraints into boosting while preserving analyzable training dynamics. Our approach, FairBoost, projects the ensemble-induced exponential-weights distribution onto a convex set of distributions satisfying fairness constraints (as a reweighting surrogate), then trains weak learners on this fair distribution. The key theoretical insight is that projecting the training distribution reduces the effective edge of weak learners by a quantity controlled by the KL-divergence of the projection. We prove an exponential-loss bound where the convergence rate depends on weak learner edge minus a "fairness cost" term $δ_t = \sqrt{\mathrm{KL}(w^t \| q^t)/2}$. This directly quantifies the accuracy-fairness tradeoff in boosting dynamics. Experiments on standard benchmarks validate the theoretical predictions and demonstrate competitive fairness-accuracy tradeoffs with stable training curves.

Abbas Sarvestani, Mohammadreza Mohammadi

Research Problem and Background Contemporary environmental crises have precipitated unprecedented primary and secondary threats to humanity, placing these issues at the core of global politics. The devastating impact of these threats on global health, economy, security, and geopolitics—manifesting in intensified unemployment, widening economic inequalities, and associated domestic political challenges such as the rise of populism, authoritarian regimes, the undermining of free trade, regional conflicts, forced migration, violence, organized crime, and the risks of terrorism and fundamentalism—has fundamentally transformed the traditional, military-centric, and zero-sum understanding of peace and security. This evolution has given rise to a new, comprehensive concept of environmental security that encompasses not only military and political dimensions but also economic, social, and environmental components. Given the absence of a supreme international political authority to resolve these issues and the limitations of nation-states in responding to such transnational challenges, the responsibility for ensuring environmental security is increasingly shifting towards a vast, transnational, multilateral, and multi-level network of global governance. This article is situated within the scholarly debates on environmental security and global governance. While traditional security studies focused on state-centric military threats, the post-Cold War era expanded the security agenda to include environmental dimensions. Concurrently, the literature on global governance emerged to describe the complex systems of rule-making and implementation involving both state and non-state actors at multiple levels. This research bridges these two discourses, examining how global governance structures are being shaped to address environmental security challenges and, crucially, what role nation-states retain within this evolving framework. Research Objective: This study aims to elucidate the meaning and conceptualization of environmental security and to analyze the phenomenon of global governance in this domain. Its primary objective is to critically examine the position and role of nation-states within this novel architecture of global politics and international relations, specifically concerning environmental security. Research Methodology This qualitative research employs an analytical-descriptive methodology, grounded in a critical review of existing literature. It synthesizes theories from international relations, security studies, and global environmental politics. The analysis involves conceptual investigation to define core terms like environmental security and global governance. Furthermore, it examines empirical evidence, including case studies of international environmental agreements (e.g., the Paris Agreement), statistical data trends on environmental degradation and cooperation, and comparative analysis of different countries' experiences with ecological crises. This multi-faceted approach allows for a comprehensive assessment of the dynamics between state sovereignty and transnational governance mechanisms. Findings and Discussion: The analysis reveals that global governance, whether viewed positively as a novel mechanism to tackle globalization's problems or critically as a political project of global dominance, has instigated three fundamental shifts in world politics concerning environmental security. First, it describes global policies that are no longer the exclusive domain of national governments but are shaped by a network of transnational and sub-national non-state actors. Second, global governance policies are formulated through new forms of cooperation within networks involving both states and non-institutions, extending beyond traditional state-negotiated binding legal instruments. Finally, global governance has created diverse networks and clusters of rule-making and implementation, where authority is distributed both horizontally among various actors and vertically across transnational, national, and sub-national levels. Regarding the future of global governance for environmental security, several scenarios emerge from the findings. Some theorists, citing the borderless nature of environmental problems and declining trust in the capacity of nation-states, posit that transnational actors will increasingly collaborate to fill the void, potentially leading to a global governance system that gradually supplants state-centric governance. The examined data, including the role of agreements like the Paris Accord in mitigating greenhouse gas emissions, supports the significant positive impact that international cooperation and global governance can have on enhancing environmental security. Conversely, the findings also highlight a countervailing trend. The rise of nationalism and geopolitical tensions threatens to erode the space that fostered transnational governance in recent decades. This shift towards nationalist and authoritarian domestic politics constrains the ability of sub-state and non-state actors to operate across borders, suggesting a potential overall contraction in civil society space and non-governmental activism within global governance for environmental security. Conclusion This article argues that the ultimate trajectory likely lies between these two extremes. It predicts a continued coexistence of multiple levels of global governance for environmental security. As expanding environmental threats like resource scarcity and natural disasters elevate ecological issues to high politics and increase their weight in domestic political discourse and electoral agendas internationally, the accumulation of environmental crises will likely pressure the global governance regime. This pressure may drive a shift from soft, normative laws towards harder, more binding and restrictive international environmental regulations, imposing greater constraints on nation-states. Therefore, while the state's monopoly in governing environmental security is being challenged and transformed, it is not rendered obsolete. Instead, states are compelled to navigate and negotiate their role within an increasingly complex, multi-actor global governance landscape aimed at securing a fragile planetary environment.

Tanise Ceron, Dmitry Nikolaev, Dominik Stammbach et al.

Large language models (LLMs) are known to generate politically biased text. Yet, it remains unclear how such biases arise, making it difficult to design effective mitigation strategies. We hypothesize that these biases are rooted in the composition of training data. Taking a data-centric perspective, we formulate research questions on (1) political leaning present in data, (2) data imbalance, (3) cross-dataset similarity, and (4) data-model alignment. We then examine how exposure to political content relates to models' stances on policy issues. We analyze the political content of pre- and post-training datasets of open-source LLMs, combining large-scale sampling, political-leaning classification, and stance detection. We find that training data is systematically skewed toward left-leaning content, with pre-training corpora containing substantially more politically engaged material than post-training data. We further observe a strong correlation between political stances in training data and model behavior, and show that pre-training datasets exhibit similar political distributions despite different curation strategies. In addition, we find that political biases are already present in base models and persist across post-training stages. These findings highlight the central role of data composition in shaping model behavior and motivate the need for greater data transparency.

Dmitry Dudukalov, Evgeny Prokopenko

This paper discusses the challenges encountered in building a ranking model for aggregator site products, using the example of ranking microfinance institutions (MFIs) based on post-click conversion. We suggest which features of MFIs should be considered, and using an algorithm based on Markov chains, we demonstrate the ``usefulness'' of these features on real data. The ideas developed in this work can be applied to aggregator websites in microinsurance, especially when personal data is unavailable. Since we did not find similar datasets in the public domain, we are publishing our dataset with a detailed description of its attributes.

Emi Yoshikawa, Franziska Roesner

Building on recent work studying content in the online advertising ecosystem, including our own prior study of political ads on the web during the 2020 U.S. elections, we analyze political ad content appearing on websites leading up to and during the 2024 U.S. elections. Crawling a set of 745 news and media websites several times from three different U.S. locations (Atlanta, Seattle, and Los Angeles), we collect a dataset of over 15000 ads, including (at least) 315 political ads, and we analyze it quantitatively and qualitatively. Among our findings: a prevalence of clickbait political news ads, echoing prior work; a seemingly new emphasis (compared to 2020) on voting safety and eligibility ads, particularly in Atlanta; and non-election related political ads around the Israel-Palestine conflict, particularly in Seattle. We join prior work in calling for more oversight and transparency of political-related ads on the web. Our dataset is available at https://ad-archive.cs.washington.edu.

Michael S. Harre

The integration of agential artificial intelligence into socioeconomic systems requires us to reexamine the evolutionary processes that describe changes in our economic institutions. This article synthesizes three frameworks: multi-level selection theory, Aoki's view of firms as computational processes, and Ostrom's design principles for robust institutions. We develop a framework where selection operates concurrently across organizational levels, firms implement distributed inference via game-theoretic architectures, and Ostrom-style rules evolve as alignment mechanisms that address AI-related risks. This synthesis yields a multi-level Price equation expressed over nested games, providing quantitative metrics for how selection and governance co-determine economic outcomes. We examine connections to Acemoglu's work on inclusive institutions, analyze how institutional structures shape AI deployment, and demonstrate the framework's explanatory power via case studies. We conclude by proposing a set of design principles that operationalize alignment between humans and AI across institutional layers, enabling scalable, adaptive, and inclusive governance of agential AI systems. We conclude with practical policy recommendations and further research to extend these principles into real-world implementation.

Gumaro Rendon, Rutuja Kshirsagar, Quoc Hoan Tran

In this work, we present a quantum algorithm designed to solve the differential equation used in the pricing of Asian options, in the framework of the Black-Scholes model. Our approach modifies an existing quantum pre-conditioning method (different from classical methods) for the problem of Asian option pricing such that we remove the dependence on the original condition number of discretized differential equation (system of linear equations). This was possible with new fast-forwardable discretizations of the first and second derivatives with respect to the underlying asset value ratio (value over average). We determine that these discretizations handle well kinks in the initial/terminal conditions. We also introduce a new circuit construction for the discretized time-derivative operator with Dirichlet boundary conditions which avoids the oracle workspace needed for the general sparse matrix implementation. Here, we also devised a new method probability integral estimation from which we extract the solution, achieving $\tilde{O}({\rm polylog}\left(1/ε)\right)$, which is an exponential improvement over other quantum methods when it comes to solution information extraction from the solution state.

Huijia Teh

The paper aims to enrich the discourse on global governance through the SDGs in higher education systems by focusing on the sustainability engagement of two prominent sustainability-led universities in Malaysia and the rationales and factors that drive or impede their engagement. Literature predominantly focuses on the normative and practical aspects of universities’ capacity to contribute to the SDGs through their three missions of education, research, and outreach, but there is a lack of discussion on rationales for universities to orient toward the global agenda. Through qualitative case studies of two universities in Malaysia, Universiti Sains Malaysia (USM) and Sunway University (SU), selected for initiating their sustainability engagement before the adoption of the SDGs, the study uses complexity theory to analyze the motivations, drivers, and barriers for their actions, from the perceptions of university management and faculty members. The study found that although the rationales and factors varied in significance over time, the diversification of actors and bottom-up processes, situated with positive feedback from internal and external stakeholders, contributed to the continuity of sustainability-focused governance and operations within both universities. This suggests that universities in non-Anglophonic and non-Western contexts interested in elevating their sustainability engagement should create interventions at multiple levels to generate momentum toward sustainability engagement among their stakeholders.

Hendra Nugroho Saputro

Kemajuan teknologi di Era Industri 4.0 dan Era Society 5.0 memberikan dampak signifikan dalam berbagai aspek kehidupan/aktivitas masyarakat dunia khususnya terkait dengan pemanfaatan teknologi digital. Setiap organisasi, baik sektor publik maupun sektor privat, berlomba untuk menyediakan teknologi dan inovasi yang saling terintegrasi. Hal ini dilakukan guna meningkatkan nilai tambah/keunggulan dalam rangka peningkatan produktivitas dan pencapaian tujuan organisasi serta meningkatkan kualitas hidup secara keseluruhan.

Larisa B. Mandzhikova

Introduction. The “Regulation on the Administration of the Kalmyk People” dated April 23, 1847 established the Horde branch of the Astrakhan Chamber of State Properties, settled the issues of administrating the Kalmyk people, including special rights of Noyons, Zaisangs and Lamas. In the course of the activity of the Horde department, the documents containing the information on the change of the legal status of Zaisangs, their place in the state structure of the Kalmyk steppe during the period of administrative reform were created. The purpose of the study is to study the four archival documents of the I—6 “Horde Branch of the Astrakhan Chamber of State Properties” fund containing information about zaisangs, to analyze the specific composition of the documents, their functional purpose and to source potential in studying the integration of the Kalmyk society into the all-Russian state system in the second half of the 19th century. Results. The study of the composition and content of the archival documents created during the period of the Horde branch activity of the Astrakhan Chamber of State Properties made it possible to identify and introduce into scientific circulation the new types of documents introduced into the clerical process of the Kalmyk people’s governing body, to trace the order of approval in the rank of so called generic Zaisangs as well as Kalmyk commoners who achieved success in the period after the adoption of the Regulations of 1847.

Clément Cornet, Héloïse Aumaître, Romaric Besançon et al.

Die studies are fundamental to quantifying ancient monetary production, providing insights into the relationship between coinage, politics, and history. The process requires tedious manual work, which limits the size of the corpora that can be studied. Few works have attempted to automate this task, and none have been properly released and evaluated from a computer vision perspective. We propose a fully automatic approach that introduces several innovations compared to previous methods. We rely on fast and robust local descriptors matching that is set automatically. Second, the core of our proposal is a clustering-based approach that uses an intrinsic metric (that does not need the ground truth labels) to determine its critical hyper-parameters. We validate the approach on two corpora of Greek coins, propose an automatic implementation and evaluation of previous baselines, and show that our approach significantly outperforms them.

Goshi Aoki

The advancement of generative AI, particularly large language models (LLMs), has a significant impact on politics and democracy, offering potential across various domains, including policymaking, political communication, analysis, and governance. This paper surveys the recent and potential applications of LLMs in politics, examining both their promises and the associated challenges. This paper examines the ways in which LLMs are being employed in legislative processes, political communication, and political analysis. Moreover, we investigate the potential of LLMs in diplomatic and national security contexts, economic and social modeling, and legal applications. While LLMs offer opportunities to enhance efficiency, inclusivity, and decision-making in political processes, they also present challenges related to bias, transparency, and accountability. The paper underscores the necessity for responsible development, ethical considerations, and governance frameworks to ensure that the integration of LLMs into politics aligns with democratic values and promotes a more just and equitable society.

Federico Albanese, Esteban Feuerstein, Pablo Balenzuela

Individuals engaging on social media often tend to establish online communities where interactions predominantly occur among like-minded peers. While considerable efforts have been devoted to studying and delineating these communities, there has been limited attention directed towards individuals who diverge from these patterns. In this study, we examine the community structure of re-post networks within the context of a polarized political environment at two different times. We specifically identify individuals who consistently switch between opposing communities and analyze the key features that distinguish them. Our investigation focuses on two crucial aspects of these users: the topological properties of their interactions and the political bias in the content of their posts. Our analysis is based on a dataset comprising 2 million tweets related to US President Donald Trump, coupled with data from over 100 000 individual user accounts spanning the 2020 US presidential election year. Our findings indicate that individuals who switch communities exhibit disparities compared to those who remain within the same communities, both in terms of the topological aspects of their interaction patterns (pagerank, degree, betweenness centrality.) and in the sentiment bias of their content towards Donald Trump.

Yujin Potter, Shiyang Lai, Junsol Kim et al.

How could LLMs influence our democracy? We investigate LLMs' political leanings and the potential influence of LLMs on voters by conducting multiple experiments in a U.S. presidential election context. Through a voting simulation, we first demonstrate 18 open- and closed-weight LLMs' political preference for a Democratic nominee over a Republican nominee. We show how this leaning towards the Democratic nominee becomes more pronounced in instruction-tuned models compared to their base versions by analyzing their responses to candidate-policy related questions. We further explore the potential impact of LLMs on voter choice by conducting an experiment with 935 U.S. registered voters. During the experiments, participants interacted with LLMs (Claude-3, Llama-3, and GPT-4) over five exchanges. The experiment results show a shift in voter choices towards the Democratic nominee following LLM interaction, widening the voting margin from 0.7% to 4.6%, even though LLMs were not asked to persuade users to support the Democratic nominee during the discourse. This effect is larger than many previous studies on the persuasiveness of political campaigns, which have shown minimal effects in presidential elections. Many users also expressed a desire for further political interaction with LLMs. Which aspects of LLM interactions drove these shifts in voter choice requires further study. Lastly, we explore how a safety method can make LLMs more politically neutral, while raising the question of whether such neutrality is truly the path forward.

Keemya V. Orlova

Introduction. Evgeny A. Stulov (1902–1940) is a remarkable yet still little-known figure who invested extensive and fruitful efforts in Mongolia and for Mongolia. He came from a large peasant family but grew to receive sound training for that time which included real school courses followed by studies at Moscow University (External Affairs Department, Faculty of Social Science). On graduation, he was added to the reserve list of the People’s Commissariat of Foreign Trade (Administrative and Economic Department) to be further employed as economist (since August 1925) at the Chief Department of Tariffs (same Commissariat) — and soon promoted to Secretary of the Committee for Customs and Tariffs (CDT). Stulov’s expertise and competence were manifested in most diverse ways: he was writing scholarly articles, delivering lectures; as Advisor to the MPR Economic Council had to deal with the troubled situation in Arkhangai Aimag; and was in charge of Mongolia’s zoning. Goals. The study describes E. Stulov’s efforts dealing with Mongolia’s zoning, provides data on his articles and lectures. Materials. The paper investigates documents housed at Russian State Archive of the Economy, examines some published sources and scholarly writings. Results. Despite E. Stulov lived a short life (38 years only), his knowledge, competences, and accomplishments made it possible for him to gain a deep and practical understanding of the country and its citizens. As Advisor to the MPR Economic Council, he promptly delved into the circumstances behind the uprising in Arkhangai Aimag — and along with other researchers was further involved in zoning endeavors. Key outcomes of his work were approved and accepted by the Mongolian government. The scope of his activity also included writing of articles and lecturing. Our analysis attests to that he was well familiar with works authored both by predecessors and contemporaries , i.e. then and there we would face a man of individuality and talent who had accumulated good knowledge of Mongolia and actually done a lot for its future.

Simon Montfort

Public support and political mobilization are two crucial factors for the adoption of ambitious climate policies in line with the international greenhouse gas reduction targets of the Paris Agreement. Despite their compound importance, they are mainly studied separately. Using a random forest machine-learning model, this article investigates the relative predictive power of key established explanations for public support and mobilization for climate policies. Predictive models may shape future research priorities and contribute to theoretical advancement by showing which predictors are the most and least important. The analysis is based on a pre-election conjoint survey experiment on the Swiss CO2 Act in 2021. Results indicate that beliefs (such as the perceived effectiveness of policies) and policy design preferences (such as for subsidies or tax-related policies) are the most important predictors while other established explanations, such as socio-demographics, issue salience (the relative importance of issues) or political variables (such as the party affiliation) have relatively weak predictive power. Thus, beliefs are an essential factor to consider in addition to explanations that emphasize issue salience and preferences driven by voters' cost-benefit considerations.

Kurniawan Kurniawan

There are few serious problems faced by BPJS Kesehatan such as inaccuracy in member’s data management, complicated system, mistreatment toward BPJS patients, and patients’ rejection in several hospitals for various reasons. These problems could be analyzed by scrutinizing the employee work satisfaction and performance which are affected by work environment, organization culture, and employee workload. This study aimed to determine the effect of the work environment, organizational culture and workload on job satisfaction and employee performance of BPJS Kesehatan Muara Bungo branch in 2020. This study was conducted using a quantitative statistical approach and collecting research data using questionnaire. The population in this study were all employees of BPJS Kesehatan Muara Bungo Branch Office, totaling 105 people and all of them were assigned as research respondents. The dependent variable was employee performance and job satisfaction, while the use of the independent variable consisted of the work environment, organizational culture, and workload. Structural Equation Model (SEM) was applied to analyze data for answering the research hypothesis. The findings in this study proved that the work environment, workload, and organizational culture affected on job satisfaction. Workload and organizational culture variables affected employee performance while the work environment variable did not affect performance. The test results of job satisfaction variables were known to have an influence on employee performance variables.

Tunazzina Islam, Shamik Roy, Dan Goldwasser

Social media platforms are currently the main channel for political messaging, allowing politicians to target specific demographics and adapt based on their reactions. However, making this communication transparent is challenging, as the messaging is tightly coupled with its intended audience and often echoed by multiple stakeholders interested in advancing specific policies. Our goal in this paper is to take a first step towards understanding these highly decentralized settings. We propose a weakly supervised approach to identify the stance and issue of political ads on Facebook and analyze how political campaigns use some kind of demographic targeting by location, gender, or age. Furthermore, we analyze the temporal dynamics of the political ads on election polls.

Rubal Kanozia, Ritu Arya, Satwinder Singh et al.

This research article attempts to understand the current situation of fake news on social media in India. The study focused on four characteristics of fake news based on four research questions: subject matter, presentation elements of fake news, debunking tool(s) or technique(s) used, and the social media site on which the fake news story was shared. A systematic sampling method was used to select a sample of 90 debunked fake news stories from two Indian fact-checking websites, Alt News and Factly, from December 2019 to February 2020. A content analysis of the four characteristics of fake news stories was carefully analyzed, classified, coded, and presented. The results show that most of the fake news stories were related to politics in India. The majority of the fake news was shared via a video with text in which narrative was changed to mislead users. For the largest number of debunked fake news stories, information from official or primary sources, such as reports, data, statements, announcements, or updates were used to debunk false claims.