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The Chinese government has long enjoyed a higher level of popular trust in its central authority than in its local governments, which means that the Chinese public's trust in government is hierarchical. While existing research has highlighted hierarchical trust's role in bolstering the Chinese regime's rule, the formation mechanism for such trust has not been adequately explored empirically. In this paper, we use data from the China General Social Survey (2010) to explore the formation mechanism of hierarchical government trust and find that economic development, adherence to traditional values, and high frequency of Internet usage all contribute to the decrease of hierarchical government trust. These findings challenge conventional views that cultural traditions and Internet use help sustain hierarchical government trust and show that propaganda is the only variable that sustains the pattern of hierarchical government trust. We further challenge existing literature that views hierarchical government trust as a natural outcome of China's hierarchical administrative structure and empirically prove that such trust is in fact intentionally constructed by the central government through propaganda campaigns and an institutional design aimed at strengthening the central government's authority and at guiding people to divert dissent to local governments. Our findings make an important contribution to the dialogue and highlight a new area of authoritarian durability. (Pac Aff/GIGA)

It is known that many networks modeling real-life complex systems are small-word (large local clustering and small diameter) and scale-free (power law of the degree distribution), and very often they are also hierarchical. Although most of the models are based on stochastic methods, some deterministic constructions have been recently proposed, because this allows a better computation of their properties. Here a new deterministic family of hierarchical networks is presented, which generalizes most of the previous proposals, such as the so-called binomial tree. The obtained graphs can be seen as graphs on alphabets (where vertices are labeled with words of a given alphabet, and the edges are defined by a specific rule relating different words). This allows us the characterization of their main distance-related parameters, such as the radius and diameter. Moreover, as a by-product, an efficient shortest-path local algorithm is proposed. ; This research has been partially supported by AGAUR from the Catalan Government under project 2017SGR1087 and by MICINN from the Spanish Government under project PGC2018- 095471-B-I00. The research of the first author has also been supported by MICINN from the Spanish Government under project MTM2017-83271-R. The research of the first author has also received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No 734922.

This thesis is devoted to the development of new hierarchical latent variable models for Dimensionality Reduction with a specific focus on the construction of Composite Indicators (CIs). Since our society is producing a huge quantity of data, the construction of model-based CIs represents an interesting and still open methodological challenge. This dissertation is motivated by the necessity to provide model-based CIs which are built according to a statistical approach avoiding subjective choices (e.g., normative weights), therefore, the new insights and proposals hope to represent a contribution to the current literature. This thesis provides an introduction to CIs, a brief review of the most used methods in Multidimensional Data Analysis framework, a discussion about measurement models and methodological proposals to model latent concepts. Factor Analysis and its hierarchical extensions have been introduced in order to set the starting point of the analysis. A first proposal represents a new latent factor model that could be used for building CIs, it aims to investigate the hierarchical structure of the data in order to define two levels of CIs. The model, named Hierarchical Disjoint Non-Negative Factor Analysis is composed of two novelties: a model which is the two level hierarchical extension of FA and its disjoint extension with non-negative loadings. The latter model is enriched by considerations about the CIs used for tracking coherent policy conclusions. A set of features, properties and rules useful to build "good" CIs have been presented and explained. The last proposal in the thesis represents a new model for positive data correlation matrices which aims to detect reliable concepts and to build the hierarchy from them to the most general one. The proposed models are illustrated both via simulation studies and real data applications, to analyze their performances and abilities. In particular, the main application in this thesis regards the construction of a hierarchically aggregated index for the multidimensional phenomenon Waste Management in European Union. Waste Management is becoming even more important for its impact on human-being's lives, and many data have been produced about it, therefore the construction of a CI able to reduce its dimensionality and to highlight the main dimensions of it has a extraordinary usefulness in order to provide support to EU countries' action and policies.

Enhanced participation has been prescribed as the way forward for improving democratic decision making while generating positive attributes like trust. Yet we do not know the extent to which rules affect the outcome of decision making. This article investigates how different group decision rules affect group trust by testing three ideal types of decision rules (i.e., a Unilateral rule, a Representative rule and a 'Non‐rule') in a laboratory experiment. The article shows significant differences between the three decision rules on trust after deliberation. Interestingly, however, it finds that the Representative rule yields more trust than the Non‐rule and also significantly more trust than the Unilateral rule, when analysing the results at group level. These findings challenge the theoretical understanding by, for example, deliberative normative theorists that more inclusive, consensual and non‐hierarchical decision‐making procedures enhance trust vis‐à‐vis other more hierarchical decision‐making procedures.

Research has indicated limited effects of formal governance measures on securities fraud prevention in emerging markets due to the weak rule of law. We propose that hierarchical inconsistency, misaligned rank ordering in formal organizational and informal social hierarchies of the corporate elite, can provide a novel monitoring mechanism to reduce securities fraud. Leaders at the top of the two inconsistent hierarchies can feel distressed and motivated to engage in contestation and challenge each other's authority, thus providing checks and balances and preventing groupthink. This monitoring effect is likely to be stronger when either of the two heads has dominant and unequivocal superiority in their respective hierarchy, making them particularly distressed by the hierarchical inconsistency and prone to contest. We test our argument in the context of publicly listed family-controlled firms in China, where business and family hierarchies may confer superiority to different individuals. Our study contributes to the corporate securities fraud literature by understanding how formal organizational structures and informal social relationships interact and jointly influence governance effectiveness in emerging markets.Funding: This work was supported by the Strategic Management Society [2015 SRF Dissertation Research Grant], Rudolf and Valeria Maag research funds, and INSEAD alumni funds.

Coordinated human behaviour takes place within a diverse range of social organisational structures, which can be thought of as power structures with "managers" who influence "subordinates". A change in policy in one part of the organisation can cause cascades throughout the structure, which may or may not be desirable. As organisations change in size, complexity and structure, the system dynamics also change. Here, we consider majority rule dynamics on organisations modelled as hierarchical directed graphs, where the directed edges indicate influence. We utilise a topological measure called the trophic incoherence parameter, q, which effectively gauges the stratification of power structure in an organisation. We show that this measure bounds regimes of behaviour. There is fast consensus at low q (e.g. tyranny), slow consensus at mid q (e.g. democracy), and no consensus at high q (e.g. anarchy). These regimes are investigated analytically, numerically and empirically with diverse case studies in the Roman Army, US Government, and a healthcare organisation. Our work demonstrates the usefulness of the trophic incoherence parameter when considering models of social influence dynamics, with widespread consequences in the design and analysis of organisations.

Coordinated human behaviour takes place within a diverse range of social organisational structures, which can be thought of as power structures with "managers" who influence "subordinates". A change in policy in one part of the organisation can cause cascades throughout the structure, which may or may not be desirable. As organisations change in size, complexity and structure, the system dynamics also change. Here, we consider majority rule dynamics on organisations modelled as hierarchical directed graphs, where the directed edges indicate influence. We utilise a topological measure called the trophic incoherence parameter, q, which effectively gauges the stratification of power structure in an organisation. We show that this measure bounds regimes of behaviour. There is fast consensus at low q (e.g. tyranny), slow consensus at mid q (e.g. democracy), and no consensus at high q (e.g. anarchy). These regimes are investigated analytically, numerically and empirically with diverse case studies in the Roman Army, US Government, and a healthcare organisation. Our work demonstrates the usefulness of the trophic incoherence parameter when considering models of social influence dynamics, with widespread consequences in the design and analysis of organisations

AbstractPrevious work has shown that infants as young as 8 months of age can use certain features of the environment, such as the shape or color of visual stimuli, as cues to organize simple inputs into hierarchical rule structures, a robust form of reinforcement learning that supports generalization of prior learning to new contexts. However, especially in cluttered naturalistic environments, there are an abundance of potential cues that can be used to structure learning into hierarchical rule structures. It is unclear how infants determine what features constitute a higher‐order context to organize inputs into hierarchical rule structures. Here, we examine whether 9‐month‐old infants are biased to use social stimuli, relative to non‐social stimuli, as a higher‐order context to organize learning of simple visuospatial inputs into hierarchical rule sets. Infants were presented with four face/color‐target location pairings, which could be learned most simply as individual associations. Alternatively, infants could use the faces or colorful backgrounds as a higher‐order context to organize the inputs into simpler color‐location or face‐location rules, respectively. Infants were then given a generalization test designed to probe how they learned the initial pairings. The results indicated that infants appeared to use the faces as a higher‐order context to organize simpler color‐location rules, which then supported generalization of learning to new face contexts. These findings provide new evidence that infants are biased to organize reinforcement learning around social stimuli.

The author explores the relationship between substantive rules and formal regulations in 3 bureaucratic contexts: French, American and Thai. Relationship between superior and subordinate bureaucracy in two rural districts of Thailand. Hierarchical structures of the district-level administration. (DÜI-Sen)

The Internet of Things (IoT) marks a phase transition in the evolution of the Internet, distinguished by a massive connectivity and the interaction with the physical world. The organic evolution of IoT requires the consideration of three dimensions: scale, organization, and context. These dimensions are particularly relevant in Ultra Large Scale Systems (ULSS), of which autonomous vehicles is a prime example. Fog Computing is well positioned to support contextual awareness and communication, critical for ULSS. The design and orchestration of ULSS require fresh approaches, new organizing principles. A recent paper proposed Hierarchical Emergent Behaviors (HEB), an architecture that builds on established concepts of emergent behaviors and hierarchical decomposition and organization. HEB's local rules induce emergent behaviors, i.e., useful behaviors not explicitly programmed. In this chapter we take a first step to validate HEB concepts through the study of two basic self-driven car "primitives": exiting a platoon formation, and maneuvering in anticipation of obstacles beyond the range of on-board sensors. Fog nodes provide the critical contextual information required. ; Damian Roca work was supported by a Doctoral Scholarship provided by Fundación La Caixa. This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493) and by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P). ; Peer Reviewed ; Postprint (author's final draft)

The Internet of Things (IoT) marks a phase transition in the evolution of the Internet, distinguished by a massive connectivity and the interaction with the physical world. The organic evolution of IoT requires the consideration of three dimensions: scale, organization, and context. These dimensions are particularly relevant in Ultra Large Scale Systems (ULSS), of which autonomous vehicles is a prime example. Fog Computing is well positioned to support contextual awareness and communication, critical for ULSS. The design and orchestration of ULSS require fresh approaches, new organizing principles. A recent paper proposed Hierarchical Emergent Behaviors (HEB), an architecture that builds on established concepts of emergent behaviors and hierarchical decomposition and organization. HEB's local rules induce emergent behaviors, i.e., useful behaviors not explicitly programmed. In this chapter we take a first step to validate HEB concepts through the study of two basic self-driven car "primitives": exiting a platoon formation, and maneuvering in anticipation of obstacles beyond the range of on-board sensors. Fog nodes provide the critical contextual information required. ; Damian Roca work was supported by a Doctoral Scholarship provided by Fundación La Caixa. This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493) and by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P). ; Peer Reviewed ; Postprint (author's final draft)

"European Union Governance" published on by Oxford University Press.

A random sample attitude survey concluded a ten-year research effort on fisheries co-management that was carried out in Malawi, Mozambique and Zambia. The survey responses were analyzed using a two-level hierarchical model that allowed conclusions about village-level institutions to be based on individual-level survey responses. The present paper describes the surveys background, methodology, and conclusions. Three of these conclusions are very clear from the survey results. The first is that co-management that is more responsive to the community is more effective. The second is that co-management institutions made up mainly of fishers are more effective than ones that incorporate a broad range of stakeholders. The third is that local conservation efforts being seen as making a positive contribution to village life is unrelated to their being seen as rule enforcement mechanisms.

A random sample attitude survey concluded a ten-year research effort on fisheries co-management that was carried out in Malawi, Mozambique and Zambia. The survey responses were analyzed using a two-level hierarchical model that allowed conclusions about village-level institutions to be based on individual-level survey responses. The present paper describes the surveys background, methodology, and conclusions. Three of these conclusions are very clear from the survey results. The first is that co-management that is more responsive to the community is more effective. The second is that co-management institutions made up mainly of fishers are more effective than ones that incorporate a broad range of stakeholders. The third is that local conservation efforts being seen as making a positive contribution to village life is unrelated to their being seen as rule enforcement mechanisms.

Existing scholarship shows that a history of military rule increases the risk of democratic breakdown. However, scholars overlook the fact that military rule takes two distinct forms: collegial and personalist military rule. I argue that the two types of military rule provide different structural settings for post-authoritarian contexts. Collegial military rule hands over more cohesive and hierarchical militaries to their subsequent democracies than personalist military rule. These militaries remain organized, politicized, and powerful in emerging democracies, which increases the risk of military intervention and coups. I hypothesize that collegial military rule poses a greater threat to the survival of the ensuing democracies than personalist military rule. Empirical analysis reveals that democracies after collegial military rule are more likely to collapse than other democracies, including those emerging from personalist military rule. This shows that the previous finding on the detrimental effect of military rule is largely driven by collegial military rule.