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Lending Leniency: The Relationship Between High-status Affiliations and Consumer Acceptance of Products in Contested Markets
Full text linkMarkets are often sites of ongoing contestation regarding the acceptability of various product features and production practices. While prior research has explored how producers resolve moral controversies, less attention has been paid to how they convince consumers of their productsβ moral acceptability when consensus remains elusive. This study addresses this gap by examining a prominent tactic: producersβ strategic affiliations with high-status moral advisors. We theorize that such affiliations reassure consumers, making them more likely to accept reduced financial returns for products bearing a strong stamp of moral approval. We test and find support for this argument using data on 1,540 Shariah-compliant bonds, or sukuk, where there has been ongoing debate over what product features are allowed according to Islam. We find that sukuk endorsed by high-status Shariah scholars (sheikhs) have significantly lower coupon rates, indicating consumersβ willingness to accept reduced financial returns in exchange for moral reassurance. Additionally, the impact of high-status endorsements weakens as sukuk adhere more closely to strict moral interpretations, highlighting a compensatory relationship between status signals and substantive product features. Supplementary analyses reveal that issuers are more likely to seek endorsements from high-status moral advisors when their products are complex or opaque. Overall, this research helps to build a more comprehensive picture of the tactics producers use to overcome the challenges of contested moral markets
The Dynamics of Theory and Value Performativity at Business Schools: Performative Valorization Work for Finance Education
No full textTheories taught in business schools are performative in the sense that they shape future practitionersβ identities and practices, often leading them to prioritize selfβinterests and profit maximization to the detriment of social and environmental welfare. Moving beyond the mere recognition of theory performativity, researchers have called for βpositive performativityββthat is, interventions that leverage theory to enact more desirable realities. Such interventions, however, are driven by values. Recognizing that values themselves are performed, this paper theorizes the dynamic relationships between theory performativity and value performativity. We conceptualize βperformative valorization workβ as a set of microprocesses coenabling the performativity of theory through values
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the performativity of values through theory. We integrate these microprocesses into a positive performativity model that shows how performative valorization work contributes to theory and values de/realization at business schools. We illustrate our model with a case of realignment of a finance programme with ecological values at a business schoolβa shift that produced performative effects on the ecosystems of researchers, students and practitioners. By theorizing how theory and value performativity interact, we advance the analysis of positive performativity and clarify how theory and values interact in pedagogical transformations
From trust to augmentation: A comprehensive survey on synergistic integration of decentralized and generative intelligence
No full textThe integration of artificial intelligence (AI) and decentralization is reshaping application and system design across industry, government, and academia. Existing surveys typically examine blockchain, Web3, or generative models independently, which obscures the cross-layer dependencies that govern verifiability, privacy, coordination, and governance in decentralized systems. This survey bridges that gap by introducing a unified trust-to-augmentation framework that organizes the convergence into four interdependent layers: trust-based execution, privacy-preserving interoperable middleware, collaborative learning mesh, and generative augmentation. Unlike prior work that addresses these domains in isolation or in limited binary pairings, the survey explains how advances in one layer alter the requirements and surfaces of the others and identifies research gaps that arise from the integration of decentralized and generative AI. We map representative systems to the four layers and consolidate a taxonomy of enabling techniques, evaluation metrics, and layer-specific comparison tables to support consistent positioning of novel contributions. The synthesis clarifies how the convergence mitigates key limitations of centralized AI, including opacity and single points of failure. It enables automated governance, intelligent consensus, and adaptive user interfaces that preserve fault tolerance and data sovereignty. The analysis also highlights deployment challenges, including scalability bottlenecks, privacy protection under transparent ledgers, cross-chain interoperability, model interpretability, and incentive alignment. The survey identifies barriers to widespread adoption and provides strategic guidance for researchers, practitioners, and policymakers through analysis of real-world applications and deployment methodologies
Molecular dynamics simulation study on thermophysical properties of carbon nanotube-enhanced lithium fluoride as a high-temperature phase change material
Full text linkLithium fluoride is combined with single-walled carbon nanotubes to enhance its performance as a phase change material for using the latent heat thermal energy storage approach in concentrated solar power systems. Molecular dynamics simulation using Large-scale Atomic/Molecular Massively Parallel Simulator is employed to evaluate thermophysical properties, including density, melting point, enthalpy, specific heat capacity, thermal conductivity, diffusion coefficients, and viscosity, across both solid and liquid phases. The addition of single-walled carbon nanotube increases the density by 3.11β6.35% in the system containing 704 carbon atoms and 5.47β10.26% in the system containing 1024 carbon atoms, while enhancing the thermal conductivity by 2.76β29.42% and 17.06β33.53% in the respective systems, thereby improving volumetric energy storage and heat transfer. A reduction in melting temperature and a minor enhancement in specific heat capacity, up to 2.6% at higher carbon concentration, are also observed. Diffusion coefficients are reduced by up to 33% and viscosity by up to 35% at higher SWCNT concentrations, demonstrating the materialβs suitability for stationary thermal energy storage systems. Figure of merit analysis indicates that the composite phase change material with 1024 carbon atoms exhibits the best overall performance. These findings highlight the potential of single-walled carbon nanotube-enhanced lithium fluoride as a composite phase change material for thermal energy storage applications, validating the effectiveness of molecular dynamics simulations for high-temperature composite phase change material optimization in concentrated solar power systems
Graph neural networks for precise bug localization through structural program analysis
Full text linkBug localization (BL) is known as one of the major steps in the program repair process, which generally seeks to find a set of commands causing a program to crash or fail. At the present time, locating bugs and their sources quickly seems to be impossible as the complexity of modern software development and scaling is soaring. Accordingly, there is a huge demand for BL techniques with minimal human intervention. A graph representing source code typically encodes valuable information about both the syntactic and semantic structures of programs. Many software bugs are associated with these structures, making graphs particularly suitable for bug localization (BL). Therefore, the key contributions of this work involve labeling graph nodes, classifying these nodes, and addressing imbalanced classifications within the graph data structure to effectively locate bugs in code. A graph-based bug classifier is initially introduced in the method proposed in this paper. For this purpose, the program source codes are mapped to a graph representation. Since the graph nodes do not have labels, the Gumtree algorithm is then exploited to label them by comparing the buggy graphs and the corresponding bug-free ones. Afterward, a trained, supervised node classifier, developed based on a graph neural network (GNN), is applied to classify the nodes into buggy or bug-free ones. Given the imbalance in the data, accuracy, precision, recall, and F1-score metrics are used for evaluation. Experimental results on identical datasets show that the proposed method outperforms other related approaches. The proposed approach effectively localizes a broader spectrum of bug types, such as undefined properties, functional bugs, variable naming errors, and variable misuse issues
Durables and Lemons: Private Information and the Market for Cars
No full textPrivate information on car quality means the sale price reflects the average quality of cars sold, which can be lower than the average quality in the population. This difference is the lemons penalty imposed on holders of high-quality cars. We estimate the evolution of the lemons penalty through an equilibrium model of car ownership with private information using Danish linked registry data on car ownership, income, and wealth. We examine the aggregate implications and distributional consequences of these penalties. In the first year of ownership, we estimate that the lemons penalty is 12% of the price. The penalty declines sharply with the length of ownership. It reduces the self-insurance value of cars and leads to a large reduction in transaction volumes and the rate of car turnover. The market does not collapse: income shocks induce households to sell their cars, even if they are of good quality, and this helps mitigate the lemons problem. The size of the lemons penalty declines when income uncertainty in the economy increases and when the supply of credit decreases
Unequal in the spotlight: Gender differences in how serving on prominent firms affects directorsβ new board appointments
Full text linkDoes gaining a foothold in the upper echelons of the corporate landscape carry different implications for women and men? We address this question by examining gender differences in how serving on the boards of prominent firms leads to new board appointments. While prominent affiliations are widely recognized as advantageous, research has yet to ask whether these benefits vary by gender. Using data on the population of directors in the FTSE-100 between 2010 and 2017, we find that women are, on average, more likely than men to obtain additional board appointmentsβconsistent with the expectation that diversity pressures stimulate demand for incumbent women relative to men. However, serving on more prominent boards within the FTSE-100 increases menβs likelihood of new appointments while decreasing it for women. Thus, womenβs advantage diminishes, and eventually reverses, as firm prominence increases. Our systematic evaluation of potential demand- and supply-side explanations for this pattern finds limited support for either. We propose instead that womenβs experiences of greater scrutiny and informal demands on more prominent boards may shape their willingness to pursue additional appointments. We highlight the dual role of prominent affiliations as sources of both opportunity and constraint, with implications for individual careers and organizational diversity
Mutation-bias learning: an evolutionary game dynamics approach to convergence analysis in multi-agent reinforcement learning
Full text linkWe present two variants of a multi-agent reinforcement learning algorithm based on evolutionary game theoretic considerations. The intentional simplicity of one variant enables us to rigorously prove results on its relationship to a system of ordinary differential equations of replicator-mutator dynamics type, allowing us to present proofs on the algorithmβs convergence conditions in various settings via its ODE counterpart, including in stable and zero-sum games. The more complicated variant enables comparisons to Q-learning based algorithms. We further compare both variants experimentally to WoLF-PHC and frequency-adjusted Q-learning on a range of settings, illustrating cases of increasing dimensionality where our variants preserve convergence in contrast to more complicated algorithms. The availability of analytic results provides a degree of transferability of results as compared to a focus on specific game classes or a purely empirical case studies, illustrating the general utility of a dynamical systems perspective on multi-agent reinforcement learning when addressing questions of convergence and reliable generalisation
Machine Learning-Driven Capacity Design and Embodied Carbon Reduction Optimization in Composite Reduced Web Section (RWS) Connections
Full text linkA gap in current predictive modelling approaches limits the ability to accurately assess the mechanical, durability performance and sustainability metrics of Reduced Web Section (RWS) connections. This paper addresses this gap by developing an ensemble machine learning (ML) framework combined with multi-objective optimisation, enabling the efficient prediction of seven key mechanical and ductility properties alongside total embodied carbon (EC) reduction. Three ensemble ML modelsβExtra Trees Regressor (ETR), Gradient Tree Boosting (GTBR), and Extreme Gradient Boosting (XGBoost)βwere evaluated, with XGBoost demonstrating superior generalization across most outputs. Additionally, Shapley Additive Explanations (SHAP) analysis was conducted to identify the most influential design parameters, improving model interpretability. The multi-objective optimisation performed using NSGA-II, generated Pareto-optimal solutions, highlighting trade-offs between structural performance and sustainability considerations. The findings reveal that cross-sectional properties, material stiffness, and connection type significantly impact RWS performance, and optimising these parameters can lead to improved ductility, moment capacity, and reduced environmental impact. To enhance practical applicability, a user-friendly interface was developed and deployed via Hugging Face, allowing users to test the results, make predictions and retrieve optimal design parameters based on the nearest Pareto-optimal solutions. The results of this paper demonstrate that ensemble ML methods, coupled with optimisation and explainability tools, provide a robust framework for advancing RWS connection design, ensuring both seismic resilience and sustainability in structural engineering
Do active Chinese equity fund managers produce positive alpha? A comprehensive performance evaluation
Full text linkThis paper evaluates the performance of mutual funds in China with the bootstrap-based false discovery rate (FDR) method based on a battery of factor models. We find robust evidence of a significantly higher proportion of skilled funds in China (19.25 percent) than is found for developed countries in the existing literature. We also examine the heterogeneity across sub-samples of different fund styles and find positive alphas for 27 percent of growth funds, 14.56 percent for balance-oriented funds and 11.6 percent for value-oriented funds. We complement the FDR accuracy assessment literature by validating the applicability of the FDR method through elaborate simulations
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