Fuzzy-set qualitative comparative analysis (fsQCA) has gained widespread popularity in social science research. However, incorporating the concept of growth—common in business and economic studies—into fsQCA remains unintuitive and methodologically challenging. This study proposes a new approach to systematically integrate growth into fsQCA applications. The method addresses three key challenges: the naming of antecedents and outcomes, the calibration of variables, and the interpretation of results. An empirical analysis demonstrates that the proposed approach offers a more generalized and robust framework for handling both positive and negative growth values. By applying the method, antecedents and outcomes are appropriately named and calibrated, and the resulting solutions are more interpretable and self-explanatory. Ultimately, the method enhances the clarity and accuracy of fsQCA outcomes, reducing the risk of misinterpretation.
History of scholarship and learning. The humanities, Social sciences (General)
Kentaro Ueda, François Portet, Hirohiko Suwa
et al.
While LLMs excel at general tasks, they struggle in specialized domains like finance, requiring diverse skills in domain knowledge, mathematical reasoning, and multilingual processing. Merging domain-specific Continual Pre-training (CPT) "experts" offers a practical alternative to costly and unstable multi-skill training. However, unlike established Supervised Fine-Tuning (SFT) model-based merging, CPT model merging remains largely unexplored. We address this gap by creating financial LLMs from experts in finance, math, and Japanese. We propose a three-stage evaluation focusing on knowledge recovery, complementarity, and emergence, and assess three merging methods (Task Arithmetic, TIES, and DARE-TIES) on a comprehensive financial benchmark curated from 18 tasks across 8 established datasets. Results show that merging an expert with its base model recovers general knowledge lost during CPT, while merging experts improves performance and can yield emergent cross-domain skills. Among the methods, Task Arithmetic performs strongly but is hyperparameter-sensitive, whereas TIES is more robust. Our findings also suggest that while model similarity correlates with merging success, emergent skills depend on more complex factors. This work presents the first foundational analysis of CPT model merging, establishing a principled framework and providing clear guidance for building multi-skill LLMs from existing assets.
With the rise of smart contracts, decentralized autonomous organizations (DAOs) have emerged in public good auctions, allowing "small" bidders to gather together and enlarge their influence in high-valued auctions. However, models and mechanisms in the existing research literature do not guarantee non-excludability, which is a main property of public goods. As such, some members of the winning DAO may be explicitly prevented from accessing the public good. This side effect leads to regrouping of small bidders within the DAO to have a larger say in the final outcome. In particular, we provide a polynomial-time algorithm to compute the best regrouping of bidders that maximizes the total bidding power of a DAO. We also prove that such a regrouping is less-excludable, better aligning the needs of the entire DAO and the nature of public goods. Next, notice that members of a DAO in public good auctions often have a positive externality among themselves. Thus we introduce a collective factor into the members' utility functions. We further extend the mechanism's allocation for each member to allow for partial access to the public good. Under the new model, we propose a mechanism that is incentive compatible in generic games and achieves higher social welfare as well as less-excludable allocations.
Moch Andry Wikra Wardhana Mamonto, Mohammad Arif, Fitriani Ahlan Sjarif
This study examines the regulatory deficiencies in Indonesia’s political finance system, particularly the absence of laws governing party income sources. Although existing rules address campaign spending and financial reporting, they lack limits on donations and fail to restrict high-risk, foreign-linked, or opaque contributions. Using a normative legal method supported by comparative, statutory, and conceptual analysis, the research draws on experiences from Thailand and the Philippines to identify best practices for reform. It argues that source-based regulation is essential to uphold constitutional equality and electoral integrity. The proposed reform agenda rests on three pillars: introducing statutory ceilings on donations, prohibiting high-risk and anonymous contributions, and institutionalising public financing tied to democratic performance. The findings show that Indonesia’s weak regulatory framework fosters elite capture, erodes internal party democracy, and diminishes public trust. By integrating these reforms, Indonesia can close legal gaps, strengthen constitutional democracy, and contribute to global discourse on political finance reform.
Law in general. Comparative and uniform law. Jurisprudence
Abstract This study investigates the application of large language models in analyzing sentiment features within the exchange rate markets. Traditional natural language processing methods, such as LDA and BERT, are effective in extracting topics from text; however, they fail to assess the relative importance of these topics in relation to target exchange rates. To bridge this gap, this paper employs ChatGPT to extract topics from texts and evaluate their importance scores, further enhancing exchange rate forecasting by integrating topic importance into the sentiment analysis framework. Through empirical analysis, the superiority of ChatGPT over LDA and BERT in both topic extraction and importance assessment is demonstrated. Furthermore, this study utilizes the topic importance scores generated by ChatGPT to develop a novel interval-valued sentiment index (TIS index). This index not only accounts for the relative importance of various events influencing exchange rate fluctuations but also captures the dynamic evolution of market sentiment within an interval. Empirical results highlight that the TIS Index significantly enhances the forecasting accuracy of interval models such as TARI and IMLP for exchange rates. These findings further demonstrate the advantages of ChatGPT in sentiment analysis within the foreign exchange market. These findings offer new insights into the application of ChatGPT in financial text research.
The connection between public spending and the ambitions of urban elites is a common topic in the historiography of the late Middle Ages. However, it is still unclear how city finances and private capital interacted before the use of sophisticated financial systems of the late 13th to early 14th centuries. The case study of Siena provides an analysis of many different archival sources that date back to the first half of the 13th century. Data show a cycle starting with deficit spending by the city to support the war effort. The deficit was financed by the municipality with large-scale borrowing from wealthy citizens, later repaid with revenues from direct taxes. For the lenders, this was a very low-risk investment that yielded medium-low returns. However, loans to the city were more a political tool to secure a position of power, rather than just an economic opportunity.
Economic history and conditions, Economics as a science
Miguel Fuentes, Brett Mullins, Ryan McKenna
et al.
Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of these methods is their inability to incorporate public data. Initializing a data generating model by pre-training on public data has shown to improve the quality of synthetic data, but this technique is not applicable when model structure is not determined a priori. We develop the mechanism jam-pgm, which expands the adaptive measurements framework to jointly select between measuring public data and private data. This technique allows for public data to be included in a graphical-model-based mechanism. We show that jam-pgm is able to outperform both publicly assisted and non publicly assisted synthetic data generation mechanisms even when the public data distribution is biased.
Jonathan Bright, Florence E. Enock, Saba Esnaashari
et al.
Generative AI has the potential to transform how public services are delivered by enhancing productivity and reducing time spent on bureaucracy. Furthermore, unlike other types of artificial intelligence, it is a technology that has quickly become widely available for bottom-up adoption: essentially anyone can decide to make use of it in their day to day work. But to what extent is generative AI already in use in the public sector? Our survey of 938 public service professionals within the UK (covering education, health, social work and emergency services) seeks to answer this question. We find that use of generative AI systems is already widespread: 45% of respondents were aware of generative AI usage within their area of work, while 22% actively use a generative AI system. Public sector professionals were positive about both current use of the technology and its potential to enhance their efficiency and reduce bureaucratic workload in the future. For example, those working in the NHS thought that time spent on bureaucracy could drop from 50% to 30% if generative AI was properly exploited, an equivalent of one day per week (an enormous potential impact). Our survey also found a high amount of trust (61%) around generative AI outputs, and a low fear of replacement (16%). While respondents were optimistic overall, areas of concern included feeling like the UK is missing out on opportunities to use AI to improve public services (76%), and only a minority of respondents (32%) felt like there was clear guidance on generative AI usage in their workplaces. In other words, it is clear that generative AI is already transforming the public sector, but uptake is happening in a disorganised fashion without clear guidelines. The UK's public sector urgently needs to develop more systematic methods for taking advantage of the technology.
This paper introduces an explicit algorithm for computing perfect public equilibrium (PPE) payoffs in repeated games with imperfect public monitoring, public randomization, and discounting. The method adapts the established framework by Abreu, Pearce, and Stacchetti (1990) into a practical tool that balances theoretical accuracy with computational efficiency. The algorithm simplifies the complex task of identifying PPE payoff sets for any given discount factor δ. A stand-alone implementation of the algorithm can be accessed at: https://github.com/jasmina-karabegovic/IRGames.git.
This study delves into the influence of socioeconomic status on the financial socialization practices of African American women in the United States. Financial socialization, defined as the process by which individuals acquire financial knowledge, skills, and behaviors, is impacted by various factors, including family dynamics, cultural values, and socioeconomic conditions. However, there is a lack of research specifically focusing on how socioeconomic status affects the financial socialization practices among African American women, who have historically encountered unique economic challenges and systemic inequalities. Employing a qualitative approach, this research integrates the analysis of national survey data with qualitative interviews conducted with African American women from diverse socioeconomic backgrounds. The study explores how socioeconomic status impacts the ways these women educate their children about money management, savings, credit use, and overall financial responsibility. The findings highlight significant variations in financial socialization practices based on socioeconomic status, indicating that women from higher socioeconomic backgrounds tend to utilize more structured and proactive financial education strategies, while those from lower socioeconomic backgrounds often encounter barriers such as limited access to financial resources and education. Furthermore, the study reveals how African American women across all socioeconomic status levels employ culturally specific strategies to overcome systemic barriers and cultivate financial resilience in their families. The results underscore the importance of considering socioeconomic status in developing tailored financial literacy programs and policies that address the distinct needs and challenges faced by African American women. By offering a nuanced understanding of how socioeconomic status influences financial socialization, this research contributes to broader discussions on economic empowerment and financial inclusion within African American communities in the United States.
Abstract Crowdfunding provides a novel and potential way for innovative but risky new ventures to fund their new product development (NPD) projects. To help potential investors evaluate the projects and enhance the credibility of disclosure, founders are struggling with how to phrase the project description. The rapidly growing cleantech crowdfunding projects provide an ideal context to study this issue. We collected information on cleantech crowdfunding projects and matched non-cleantech crowdfunding projects from Kickstarter. The sample period extends from January 2013 to October 2018. Using signaling research as a theoretical lens and a dictionary-based, computerized text mining method, we found that founders of high-quality cleantech crowdfunding projects could create a reliable signal of quality by providing a project description with a less ambiguous tone and thus boost the success of crowdfunding. Moreover, the signaling effectiveness of a less ambiguous tone is more pronounced in cleantech crowdfunding than in matched non-cleantech crowdfunding, suggesting that the marginal benefit of using a less ambiguous tone is larger when the industry information environment is noisier. Further evidence shows that the signaling effectiveness of a less ambiguous tone in cleantech crowdfunding could be strengthened by backers’ endorsements. Our findings imply that tone ambiguity in project descriptions is related to founders’ information-concealing behavior. Potential investors could search ambiguous words in project descriptions and just allocate their limited attention into projects with a low percentage of ambiguous words to avoid information overload. Founders of high-quality projects could boost crowdfunding success by using a less ambiguous tone to describe their projects. The marginal effect is larger when there is greater uncertainty about project prospects.
TB is an airborne public health threat, so the reponse to TB has been defined mainly through the lens of vertical, public-sector national TB programs (NTPs). However, TB exists within a broader health systems and health financing context. Here, we examine the intersection between the particular needs of TB programs and the broader health financing landscape. This includes the strategies needed to finance both the clinical and public health aspects of the TB response. In high-burden countries, the resource mobilization and strategic purchasing approaches described here will be critical if we are to maximize the reach and impact of the TB response.
Ananya Joshi, Kathryn Mazaitis, Roni Rosenfeld
et al.
Irregularities in public health data streams (like COVID-19 Cases) hamper data-driven decision-making for public health stakeholders. A real-time, computer-generated list of the most important, outlying data points from thousands of daily-updated public health data streams could assist an expert reviewer in identifying these irregularities. However, existing outlier detection frameworks perform poorly on this task because they do not account for the data volume or for the statistical properties of public health streams. Accordingly, we developed FlaSH (Flagging Streams in public Health), a practical outlier detection framework for public health data users that uses simple, scalable models to capture these statistical properties explicitly. In an experiment where human experts evaluate FlaSH and existing methods (including deep learning approaches), FlaSH scales to the data volume of this task, matches or exceeds these other methods in mean accuracy, and identifies the outlier points that users empirically rate as more helpful. Based on these results, FlaSH has been deployed on data streams used by public health stakeholders.
We complete the characterization of the computational complexity of equilibrium in public goods games on graphs. In this model, each vertex represents an agent deciding whether to produce a public good, with utility defined by a "best-response pattern" determining the best response to any number of productive neighbors. We prove that the equilibrium problem is NP-complete for every finite non-monotone best-response pattern. This answers the open problem of [Gilboa and Nisan, 2022], and completes the answer to a question raised by [Papadimitriou and Peng, 2021], for all finite best-response patterns.
Celebrities can significantly influence the public towards any desired outcome. In a bid to tackle an infectious disease, a leader (government) exploits such influence towards motivating a fraction of public to get vaccinated, sufficient enough to ensure eradication. The leader also aims to minimize the vaccinated fraction of public (that ensures eradication) and use minimal incentives to motivate the influencers; it also controls vaccine-supply-rates. Towards this, we consider a three-layered Stackelberg game, with the leader at the top. A set of influencers at the middle layer are involved in a stochastic vaccination game driven by incentives. The public at the bottom layer is involved in an evolutionary game with respect to vaccine responses. We prove the disease can always be eradicated once the public is sufficiently sensitive towards the vaccination choices of the influencers -- with a minimal fraction of public vaccinated. This minimal fraction depends only on the disease characteristics and not on other aspects. Interestingly, there are many configurations to achieve eradication, each configuration is specified by a dynamic vaccine-supply-rate and a number -- this number represents the count of the influencers that needs to be vaccinated to achieve the desired influence. Incentive schemes are optimal when this number equals all or just one; the former curbs free-riding among influencers while the latter minimizes the dependency on influencers.
Alexander Spangher, Emilio Ferrara, Ben Welsh
et al.
Journalists must find stories in huge amounts of textual data (e.g. leaks, bills, press releases) as part of their jobs: determining when and why text becomes news can help us understand coverage patterns and help us build assistive tools. Yet, this is challenging because very few labelled links exist, language use between corpora is very different, and text may be covered for a variety of reasons. In this work we focus on news coverage of local public policy in the San Francisco Bay Area by the San Francisco Chronicle. First, we gather news articles, public policy documents and meeting recordings and link them using probabilistic relational modeling, which we show is a low-annotation linking methodology that outperforms other retrieval-based baselines. Second, we define a new task: newsworthiness prediction, to predict if a policy item will get covered. We show that different aspects of public policy discussion yield different newsworthiness signals. Finally we perform human evaluation with expert journalists and show our systems identify policies they consider newsworthy with 68% F1 and our coverage recommendations are helpful with an 84% win-rate.
The current wave of digital transformation has spurred digitisation reforms and has led to prodigious development of AI & NLP systems, with several of them entering the public domain. There is a perception that these systems have a non trivial impact on society but there is a dearth of literature in critical AI exploring what kinds of systems exist and how do they operate. This paper constructs a broad taxonomy of NLP systems which impact or are impacted by the ``public'' and provides a concrete analyses via various instrumental and normative lenses on the socio-technical nature of these systems. This paper categorises thirty examples of these systems into seven families, namely; finance, customer service, policy making, education, healthcare, law, and security, based on their public use cases. It then critically analyses these applications, first the priors and assumptions they are based on, then their mechanisms, possible methods of data collection, the models and error functions used, etc. This paper further delves into exploring the socio-economic and political contexts in which these families of systems are generally used and their potential impact on the same, and the function creep of these systems. It provides commentary on the potential long-term downstream impact of these systems on communities which use them. Aside from providing a birds eye view of what exists our in depth analysis provides insights on what is lacking in the current discourse on NLP in particular and critical AI in general, proposes additions to the current framework of analysis, provides recommendations future research direction, and highlights the need to importance of exploring the social in this socio-technical system.
MinRank is an NP-complete problem in linear algebra whose characteristics make it attractive to build post-quantum cryptographic primitives. Several MinRank-based digital signature schemes have been proposed. In particular, two of them, MIRA and MiRitH, have been submitted to the NIST Post-Quantum Cryptography Standardization Process. In this paper, we propose a key-generation algorithm for MinRank-based schemes that reduces the size of the public key to about 50% of the size of the public key generated by the previous best (in terms of public-key size) algorithm. Precisely, the size of the public key generated by our algorithm sits in the range of 328-676 bits for security levels of 128-256 bits. We also prove that our algorithm is as secure as the previous ones.
The agricultural sector of the economy has a great potential to influence various aspects of sustainable development, such as economic stability, social equality, and ecological balance. However, one of the critical problems in achieving the Sustainable Development Goals is the limitation of financial resources, which significantly narrows the opportunities of the agricultural sector and holds back its post-war recovery based on the green economy. The article's purpose is to typologize the theoretical and methodological foundations of the sustainable ontogenesis of the agricultural sector of the Ukrainian economy, taking into account the mutual influences and interdependencies between development and the financial and regulatory context of achieving the agro-oriented Sustainable Development Goals. The article analyses tasks and indicators and presents the empirical basis for achieving the Sustainable Development Goals in the context of overcoming hunger and developing agriculture adapted to the realities of Ukraine. The problems of the agricultural sector of the economy, which the government tried to solve during the 2000-2020s, were identified, and the interdependence between these problems and the agro-oriented Sustainable Development Goals was established. The financial context of programs supporting the sustainable development of the agrarian sector of the economy in the pre-war period was determined, and their content, advantages and disadvantages were systematically characterized. The fact of systemic lagging of niche-sectoral financial support programs for agricultural commodity producers from reforms in the public finance management system and its derivatives has been revealed. The study results indicate that Ukraine's agricultural sector is currently at the crossroads between stability and sustainable development, with the former being predominant. In this regard, it is necessary to ensure the transition from state support to state aid (for specific branches of the sector), from state support for crop production to state support for livestock production, and from infrastructure financing to the development of the Institute of Rural Construction.