This study examines behavioural differences in risk aversion (variance risk), prudence (downside risk), and temperance (tail risk) between public and private sector employees, employing higher-order risk preference theory. Using two monetary-incentivized behavioural tasks (Multiple Price List and Deck-Schlesinger Lottery) with 1000 participants in Japan, we compare risk behaviours across sectors. Results demonstrate that public employees exhibit significantly higher temperance after accounting for individual-level covariates, indicating stronger aversion to extreme outcomes. Sectoral differences in risk aversion and prudence become insignificant once public service motivation is controlled. These findings highlight the importance of distinguishing multiple risk dimensions in sectoral comparisons.

This article aims to uncover the previously overlooked perspectives of key proponents of Japan's nuclear fuel cycle program, exploring the reasons behind the current stagnation and rigidity of nuclear policies through a series of elite interviews. Our findings indicate that the interviewees are primarily divided into two groups: those who do not recognize a political rigidity in the current policy and those who do, with our analysis showing that this division is linked to fundamental differences in how the two groups view past and current nuclear policies. Both groups alluded to relationships with local communities as a strong factor behind political rigidity, while the two groups diverged on the issues of adequate consideration of technological/political alternatives and transparency of the decision-making process. Notably, the second group evaluated the concept of "belief in the public interest" as a key factor, a perspective not previously identified in existing studies. Furthermore, several interviewees from both groups suggested that factors contributing to rigidity also exist among anti-nuclear groups, indicating that the so-called "nuclear village” functions in relation to broader sociopolitical contexts. These findings underscore that actors involved are not unequivocal, but rather have a diverse range of views. Policy-making processes should be better informed of such diversity, and even encourage these differences by leveraging them to build a richer expertise on techno-political alternatives and policy planning.

Economic incentives are expected to play an important role in increasing the acceptability of high-level radioactive waste (HLW) repositories; however, their effectiveness is debated and there is a lack of sufficient evidence concerning the specific factors that affect their efficacy. This study identified two factors that may be particularly pertinent to HLW management issues, namely the probability of construction and the initial level of acceptance. We tested their moderating effects on the ability of perceived economic benefits to promote acceptability, based on the framework of the repugnant transaction theory. Analysis of original survey data for hypothetical HLW management scenarios in Japan revealed that the subjective probability of construction significantly diminished the positive impact of economic incentives on acceptability. In contrast, whether participants lived in candidate areas, i.e., areas with a high objective construction probability, did not alter the effects of these incentives. Furthermore, quantile regression analysis revealed that economic incentives were less effective in influencing those with lower initial levels of acceptance. In the context of site selection policies, these findings caution against an over-reliance on monetary incentives to tackle issues of unfairness and point to the urgency of developing a more sophisticated understanding of these dynamics. Our results show that well-structured, responsive public engagement processes and transparent governance schemes are crucial for long-term acceptability, particularly when construction becomes more imminent.

The escalating frequency and complexity of natural disasters highlight the urgent need for deeper insights into how individuals and communities perceive and respond to risk information. Yet, conventional research methods—such as surveys, laboratory experiments, and field observations—often struggle with limited sample sizes, external validity concerns, and difficulties in controlling for confounding variables. These constraints hinder our ability to develop comprehensive models that capture the dynamic, context-sensitive nature of disaster decision-making. To address these challenges, we present a novel multi-stage simulation framework that integrates Large Language Model (LLM)-driven social–cognitive agents with well-established theoretical perspectives from psychology, sociology, and decision science. This framework enables the simulation of three critical phases—information perception, cognitive processing, and decision-making—providing a granular analysis of how demographic attributes, situational factors, and social influences interact to shape behavior under uncertain and evolving disaster conditions. A case study focusing on pre-disaster preventive measures demonstrates its effectiveness. By aligning agent demographics with real-world survey data across 5864 simulated scenarios, we reveal nuanced behavioral patterns closely mirroring human responses, underscoring the potential to overcome longstanding methodological limitations and offer improved ecological validity and flexibility to explore diverse disaster environments and policy interventions. While acknowledging the current constraints, such as the need for enhanced emotional modeling and multimodal inputs, our framework lays a foundation for more nuanced, empirically grounded analyses of risk perception and response patterns. By seamlessly blending theory, advanced LLM capabilities, and empirical alignment strategies, this research not only advances the state of computational social simulation but also provides valuable guidance for developing more context-sensitive and targeted disaster management strategies.

This study examines the evolving role of emergency management-related nonprofit organizations (EMNPOs) in China's rapidly transforming socioeconomic and disaster governance contexts. Drawing on a novel database of 13,588 EMNPOs registered from 1949 to 2022-a period encompassing profound regulatory reforms-we harness computational methods to clarify their developmental trajectory. By employing BERT-based language model to capture nuanced organizational semantics and by implementing K-means clustering, we identify distinct functional categories spanning disaster relief, risk reduction, and capacity building. Integrating spatial statistical techniques reveals considerable functional heterogeneity and pronounced regional disparities. While EMNPOs cluster densely in the economically dynamic eastern coastal areas-regions often aligned with strong market forces and robust institutional frameworks-they remain relatively sparse across northeastern, central, and western provinces. Our findings underscore the primacy of socioeconomic determinants-particularly strong economic development, government investment, and infrastructure assets-in shaping EMNPOs' spatial distribution. In contrast, disaster variables exert limited influence, indicating that the conditions enabling EMNPO growth stem primarily from urbanization processes and fiscal resource allocation. These insights emphasize the embedded mobilization of EMNPOs within the government-market-society nexus, wherein diversified resource channels, policy support, and strategic alignments catalyze organizational adaptation and resilience. By disentangling functional complexity and regional asymmetries, this research refines our understanding of EMNPO configurations in China's evolving emergency governance landscape. The analysis provides evidence-based guidance to enhance institutional environments, foster crosssectoral synergies, and strengthen public engagement in disaster risk reduction and recovery. More broadly, it informs a global discourse on how NPOs can ultimately promote greater resilience, equity, and accountability in disaster-prone contexts.

In an era marked by increasing geopolitical conflicts, the accurate and swift assessment of urban damage in war-affected areas is crucial for implementing effective emergency responses and sustainable recovery planning. Traditional methodologies often fall short in capturing the unpredictable dynamics of warfare and the complex structures of urban systems. This paper introduces a novel visual-graph machine learning framework, ViT2G, that synergizes the feature representation capabilities of Vision Transformers (ViT) with the structural processing strengths of Graph Neural Networks (GNNs). By integrating high-resolution satellite imagery with urban topology through a hypergraph-based data structure and designing three hypergraph-based machine learning models: Hypergraph Convolutional Network (HGCN), Hypergraph Attention Network (HGAT), and Hypergraph Transformer (HGT), we redefine the challenge of assessing war-induced urban destruction as a graph machine learning task. The experiments demonstrate that for the task of patch-wise damage status classification, this framework requires only a pair of pre- and post-war images to perform fine-grained categorization of the damaged areas. In the binary classification task, the Hypergraph Attention Network (HGAT) model achieved a best accuracy of 94.4%, while in the multi-class classification task, it achieved a best accuracy of 73.4%, which significantly outperform traditional computer vision models.

AGENTiGraph is a user-friendly, agent-driven system that enables intuitive interaction and management of domain-specific data through the manipulation of knowledge graphs in natural language. It gives non-technical users a complete, visual solution to incrementally build and refine their knowledge bases, allowing multi-round dialogues and dynamic updates without specialized query languages. The flexible design of AGENTiGraph, including intent classification, task planning, and automatic knowledge integration, ensures seamless reasoning between diverse tasks. Evaluated on a 3,500-query benchmark within an educational scenario, the system outperforms strong zero-shot baselines (achieving 95.12% classification accuracy, 90.45% execution success), indicating potential scalability to compliance-critical or multi-step queries in legal and medical domains, e.g., incorporating new statutes or research on the fly. Our open-source demo offers a powerful new paradigm for multi-turn enterprise knowledge management that bridges LLMs and structured graphs.

Dominant accounts of interactive storytelling, long wedded to seamless immersion, struggle to explain the appeal of titles like Honkai: Star Rail that flaunt fourth-wall ruptures yet deepen attachment. We contend that this paradox signals a design philosophy best described as Reflexive World-Building. Crucially, this reflexivity is not a theatrical aside but a modality of realism: by openly staging its own constructedness, the game remaps lived coordinates—work–time discipline, risk governance, platformized affect—into playable form. In this sense the “wall” is less a surface to be smashed than a seam through which social experience continually threads, including pressures that animate contemporary Chinese youth cultures. Across character design, interface paratexts, and core mechanics, Honkai: Star Rail deploys meta-narrative and self-reference not as narrative failure but as a deliberate rhetoric of recognition—an Invitation to Conspiracy that converts the player from a passive immersant into a knowing co-conspirator. Immersion is thereby relocated: from the mimetic demand to “believe” a world to the relational experience of being seen by it. Our analysis systematizes this shift, articulating how reflexive cues can operate in concert to sustain a durable ludic contract grounded in shared literacy rather than fragile illusion. The resulting framework moves beyond the immersion paradigm while retaining its affective aims, offering practical insight for crafting narratively complex experiences that speak to media-savvy publics without forfeiting realism’s bite.

The integration of artificial intelligence into development research methodologies offers unprecedented opportunities to address persistent challenges in participatory research, particularly in linguistically diverse regions like South Asia. Drawing on empirical implementation in Sri Lanka's Sinhala-speaking communities, this study presents a methodological framework designed to transform participatory development research in the multilingual context of Sri Lanka's flood-prone Nilwala River Basin. Moving beyond conventional translation and data collection tools, the proposed framework leverages a multi-agent system architecture to redefine how data collection, analysis, and community engagement are conducted in linguistically and culturally complex research settings. This structured, agent-based approach facilitates participatory research that is both scalable and adaptive, ensuring that community perspectives remain central to research outcomes. Field experiences underscore the immense potential of LLM-based systems in addressing long-standing issues in development research across resource-limited regions, delivering both quantitative efficiencies and qualitative improvements in inclusivity. At a broader methodological level, this research advocates for AI-driven participatory research tools that prioritize ethical considerations, cultural sensitivity, and operational efficiency. It highlights strategic pathways for deploying AI systems to reinforce community agency and equitable knowledge generation, offering insights that could inform broader research agendas across the Global South.

Through laboratory experiments, this study explores how belief polarization occurs. This refers to the phenomenon in which individuals' beliefs become more extreme and opposed even with access to the same information. Specifically, we test whether people's perceptions of the payoff structure increase polarization. The results show that polarization is amplified when the participants need to calculate their final earnings independently. However, when people know the payoffs of all possible consequences, its magnitude decreases dramatically. The comprehension level is also improved by understanding the payoff structure.

The integration of artificial intelligence into development research methodologies presents unprecedented opportunities for addressing persistent challenges in participatory research, particularly in linguistically diverse regions like South Asia. Drawing from an empirical implementation in Sri Lanka's Sinhala-speaking communities, this paper presents an empirically grounded methodological framework designed to transform participatory development research, situated in the challenging multilingual context of Sri Lanka's flood-prone Nilwala River Basin. Moving beyond conventional translation and data collection tools, this framework deploys a multi-agent system architecture that redefines how data collection, analysis, and community engagement are conducted in linguistically and culturally diverse research settings. This structured agent-based approach enables participatory research that is both scalable and responsive, ensuring that community perspectives remain integral to research outcomes. Field experiences reveal the immense potential of LLM-based systems in addressing long-standing issues in development research across resource-limited regions, offering both quantitative efficiencies and qualitative improvements in inclusivity. At a broader methodological level, this research agenda advocates for AI-driven participatory research tools that maintain ethical considerations, cultural respect, and operational efficiency, highlighting strategic pathways for deploying AI systems that reinforce community agency and equitable knowledge generation, potentially informing broader research agendas across the Global South.

This study investigated the association between public service motivation (PSM) and the not-in-my-backyard (NIMBY) reaction by the public that often blocks important projects. A conjoint experiment was conducted using the scenario of hypothetical plans for the construction of a high-level radioactive waste disposal site, one of the most noxious NIMBY facilities. The results showed that overall PSM (the full scale) had no association with the public's willingness to accept such facilities. Still, we observed significant positive correlations for the PSM dimensions of attraction to public service and self-sacrifice, and a significant negative correlation with the PSM dimension of compassion. As expected, some dimensions of PSM strengthened (moderated) the positive effect of policy attributes on acceptance. Contrary to our prediction that PSM attenuates the relationship between distance and acceptance, overall PSM, commitment to public values, and compassion amplified the relationship. Together, these findings suggest the need to promote research considering the multifaceted nature of PSM in NIMBY problems.

Improvement in the built environment is an essential component of transit-oriented development; however, the economic effects of such investments have not yet been fully understood because their investigation requires a suitable empirical case. Using the decentralized development of station plazas, this study identified the conditions under which plaza development raises the economic value of the surrounding areas. We empirically analyzed the impact of station plaza development on the land prices of 3870 properties in the surrounding areas at 181 of the 1556 stations in the Tokyo Metropolitan Area from 2000 to 2010, where station plazas were developed for the revitalization of station areas in a mature urban economy. Mahalanobis distance matching was applied to obtain the treatment effect, and the heterogeneity in the effects on the land price of properties by landuse pattern and geographical location was analyzed. The results showed that the properties benefited from station plaza development depending on the accessibility to and from the nearest station and the Tokyo Station (the central station) and their land-use patterns. The study found that there could be a redistributive effect in that the plaza development had positive effects on land price changes around the stations and negative effects in places far from the stations.

This study explores how “belief polarization” changes by revealing others’ actions through laboratory experiments. Belief polarization refers to the phenomenon in which individuals’ beliefs become more extreme and divergent, even with access to the same public information as others, when they possess private information. Revealing others’ actions can prevent belief polarization, since people can infer others’ private information through their actions. The key results of the study are as follows. First, when we reveal others’ actions only once, polarization still occurs and increases over rounds. On the other hand, polarization is not occurred by revealing others’ private information. Second, when others’ actions are revealed in all experiment rounds, polarization does not occur. However, if subjects think others have insufficient information, polarization persists—even when others’ actions are revealed in all rounds.

This study investigates the impact of high-speed rail (HSR) on regional innovation through an empirical analysis that uses municipal-level panel data covering all 1,741 municipalities in Japan, and spanning the period from 1976 to 2016. The study assumes that the number of patent applications serves as a proxy for regional innovation and follows a negative binomial distribution. A difference-in-differences specification with multiple timeframes is employed for the empirical analysis. The results reveal that HSR development has had a significant positive effect on regional innovation, supporting the companion innovation hypothesis. Robustness checks, such as through combining propensity score matching and the instrumental variable methods with difference-in-differences analysis, are also conducted. The results suggest that the effect of HSR on regional innovation in small towns and villages is larger than that in large cities. This implies that inter-regional communication opportunities for employees could play a key role in fostering innovation in small rural areas.