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Abstract This paper introduces a novel approach for identifying dynamic triadic transformation processes, applied to five networks: three undirected and two directed. Our method significantly enhances the prediction accuracy of network ties. While balance theory offers insights into evolving patterns of triadic structures, its effects on overall network dynamics remain underexplored. Existing research often neglects the interaction between micro-level balancing mechanisms and overall network behavior. To bridge this gap, we develop a method for detecting dynamic triadic structures in signed networks, categorizing triangle transformations over two consecutive periods into formation and breakage. We analyze the impact of these structures on temporal network evolution by incorporating them into exponential random graph models across five networks of varying size, density, and directionality. To address the complexity of multi-layer networks derived from signed networks, we modify the temporal exponential random graph model framework. Our method significantly improves out-of-sample prediction accuracy for network ties, with additional predictive power from incorporating negative network information. These findings highlight the importance of considering the triadic transformation processes of balance triangles in studying temporal networks, validated across diverse datasets, warranting further research.

Taiwan’s long-term development in digital government has set a strong foundation for its transformation to one that is empowered by artificial intelligence (AI). This article introduces five applications of AI that the Taiwan government uses for different tasks and how government agencies implement them and overcome challenges. A demand-oriented AI enables civil servants to upgrade skills, and improve government’s functions, services and value added. This article provides practical insights on how human–AI collaboration can enhance government capabilities and create a positive feedback loop to the bureaucratic system.

Research on anti-corruption efforts often considers sunlight (transparency) to be the best disinfectant, yet prior studies have no consensus on this proposition. We argue that although transparency has a spotlight effect, its efficacy is determined by an actor's assessment of the effectiveness of various accountability channels. This study proposes a 2x2 typology to illustrate different anti-corruption mechanisms between transparency and accountability. To study the interplay among transparency and three kinds of accountability on bureaucrat's perceptions of individual and collective corruption, a survey of Chinese public servants is used to understand the efficacy of anti-corruption mechanisms in a regime challenged by rapid growth and rising corruption. Results show that transparency facilitates to deterrence of individual and collective tolerance of corruption, but this is moderated by accountability mechanisms. High administrative accountability increases the effects of transparency on both individual and collective corruption, whereas high social and legal accountability only increase the deterrence effects of transparency on collective corruption. [ABSTRACT FROM AUTHOR]

Machine intelligence, used extensively throughout modern bureaucracies, is quickly evolving, giving rise to machine agents that accomplish tasks typically reserved for human beings. This shift affects task completion, human/machine coproduction, and the control of the bureaucracy itself. Using Max Weber's ideal type bureaucracy as a guiding construct, we argue that machine agents may offer technical capacity for task completion beyond that of humans. Furthermore, the technical strengths of machine intelligence, including (1) speed, (2) dispassion, (3) predictability, and (4) rational rule-based functioning, mirror those found within Weber's ideal type. Through this lens, the evolution of both bureaucratic structures and the decision-making agents within them presents at least three important challenges for modern scholars: (1) deciding the scope of tasks machine agents should complete, (2) adapting the bureaucracy to work with machine agents, and (3) retaining the role of humans and human control. [ABSTRACT FROM AUTHOR]

To advance our understanding of the institutional-level influence of intellectual property policies on innovation, this article investigates a set of hypotheses questioning the links between state-level non-competition agreements, inventor mobility, and patent commercialization. It uses US inventor survey data covering a sample of 1,900 triadic patents. Results provide some evidence in support of the prevalent, yet under-researched, proposition that mobile inventors perform better than non-mobile ones in terms of commercialization activity. This article also finds that the severity of judicial enforcement of non-competition agreements has hampered the positive contribution of inventor mobility to invention commercialization and concludes that legal infrastructure is a crucial factor in innovation and invention success stories. Findings, therefore, have considerable policy implications with respect to the role of the judiciary in the enforcement of non-competition agreements. [ABSTRACT FROM AUTHOR]