Simulating Network Structure, Layering Multi-layer Network System and Developing Network Block Configuration Model to Understand and Improve Energy Conservation in Residential Buildings

The building sector is a major contributor to total energy consumption in most countries. Traditionally, researchers have focused on leveraging energy efficiency by improving building materials, in-house facilities and transmission equipment. More recently, however, there has been increased focus on research concerning demand-side energy consumption behavior. Current research suggests that energy efficient behavior of a building's occupants can be extensively enhanced through the sharing of energy consumption information among residents in a peer network. However, most of this research relies on experimental tests and does not theorize concepts related to peer network energy efficiency systematically. My dissertation addresses this research gap on two levels. First, I examined if and how the structure of peer networks can impact residents' conservation behaviors through network analysis by employing agent-based simulation techniques. Following confirmation of the impact that network structure has on user behavior, I created a layered network model to integrate information from various network layers and a block configuration model to reconstruct increasingly reliable random networks. In contrast to controlled energy efficiency experiments, real-world networks are large in size, heterogeneous in nature and regularly interact with other networks. By utilizing models developed in this dissertation, we are able to estimate the contribution of network structural coefficients to the energy consumption performance of peer networks. By comparing the layered network and block configuration model I developed with other conventional models, I prove the efficiency, accuracy and reliability of these improved models. These findings have implications for assessing network performance, creating accurate complex random networks for large-scale research, and developing strategies for network design to improve building energy efficiency. This research establishes a system to study residents' energy efficient behaviors from the perspective of peer networks and proposes some instructive models for further energy feedback system design

Hustle and Flow: A Social Network Analysis of the American Federal Judiciary

Article published in the Ohio State Law Journal

Perspectives on Law and Legal Institutions as Complex Adaptive Systems.

This dissertation employs various theoretical and methodological perspectives to consider the “evolution” of the law and “law as a complex adaptive system.” Chapter 2 addresses the strategic institutional conditions that produced Chief Justice Rehnquist’s majority opinion in Dickerson v. United States. In the wake of the Chief Justice’s ruling, legal scholars grappled to interpret this apparently anomalous decision. This process produced a litany of deeply unsatisfactory explanations for the Chief’s behavior. Chapter 2 rejects all of these existing explanations and instead outlines a game theoretic account for the Chief’s decision in this very important Miranda related case. Applying network theory, Chapter 3 considers the social topology of the American federal judiciary. Scholars have long asserted that social structure is an important feature of a variety of societal institutions. However, to date, such social considerations have not been formally integrated in positive legal theory. Using the flow of law clerks as a proxy for social and professional linkages between jurists, Chapter 3 offers a variety of visualizations and analytics useful for considering the physical properties of the judicial social network. Chapter 4 considers the ‘evolution’ of the law in the early jurisprudence of the United States Supreme Court. Relevant dynamics include but are not limited to doctrinal importation, path dependence, cross-fertilization, mutation, fitness and selection. Chapter 4 explores a subset of these dynamics in the applied context of the early United States Supreme Court (1791-1835). Justices on the early United States Supreme Court relied upon a wide variety of sources as evidence in support of their arguments. Chapter 4 offers both descriptive data regarding the magnitude of references and identifies the extent to which those references imported ideas from foreign sources. Next, it applies the tools of network science to measure the structural importance of these foreign law infused decisions. While the empirical results are relevant to the ongoing debate regarding the Supreme Court's reliance upon foreign sources, there is something far more fundamental at stake. Specifically, Chapter 4 introduces the “legal genome project” a new conceptual framework useful for understanding the “evolution” of the law.Ph.D.Public Policy & Political ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89688/1/dmartink_1.pd

Recherche de structure dans un graphe aléatoire : modèles à espace latent

.This thesis addresses the clustering of the nodes of a graph, in the framework of randommodels with latent variables. To each node i is allocated an unobserved (latent) variable Zi and the probability of nodes i and j being connected depends conditionally on Zi and Zj . Unlike Erdos-Renyi's model, connections are not independent identically distributed; the latent variables rule the connection distribution of the nodes. These models are thus heterogeneous and their structure is fully described by the latent variables and their distribution. Hence we aim at infering them from the graph, which the only observed data.In both original works of this thesis, we propose consistent inference methods with a computational cost no more than linear with respect to the number of nodes or edges, so that large graphs can be processed in a reasonable time. They both are based on a study of the distribution of the degrees, which are normalized in a convenient way for the model.The first work deals with the Stochastic Blockmodel. We show the consistency of an unsupervised classiffcation algorithm using concentration inequalities. We deduce from it a parametric estimation method, a model selection method for the number of latent classes, and a clustering test (testing whether there is one cluster or more), which are all proved to be consistent. In the second work, the latent variables are positions in the ℝd space, having a density f. The connection probability depends on the distance between the node positions. The clusters are defined as connected components of some level set of f. The goal is to estimate the number of such clusters from the observed graph only. We estimate the density at the latent positions of the nodes with their degree, which allows to establish a link between clusters and connected components of some subgraphs of the observed graph, obtained by removing low degree nodes. In particular, we thus derive an estimator of the cluster number and we also show the consistency in some sense.Cette thèse aborde le problème de la recherche d'une structure (ou clustering) dans lesnoeuds d'un graphe. Dans le cadre des modèles aléatoires à variables latentes, on attribue à chaque noeud i une variable aléatoire non observée (latente) Zi, et la probabilité de connexion des noeuds i et j dépend conditionnellement de Zi et Zj . Contrairement au modèle d'Erdos-Rényi, les connexions ne sont pas indépendantes identiquement distribuées; les variables latentes régissent la loi des connexions des noeuds. Ces modèles sont donc hétérogènes, et leur structure est décrite par les variables latentes et leur loi; ce pourquoi on s'attache à en faire l'inférence à partir du graphe, seule variable observée.La volonté commune des deux travaux originaux de cette thèse est de proposer des méthodes d'inférence de ces modèles, consistentes et de complexité algorithmique au plus linéaire en le nombre de noeuds ou d'arêtes, de sorte à pouvoir traiter de grands graphes en temps raisonnable. Ils sont aussi tous deux fondés sur une étude fine de la distribution des degrés, normalisés de façon convenable selon le modèle.Le premier travail concerne le Stochastic Blockmodel. Nous y montrons la consistence d'un algorithme de classiffcation non supervisée à l'aide d'inégalités de concentration. Nous en déduisons une méthode d'estimation des paramètres, de sélection de modèles pour le nombre de classes latentes, et un test de la présence d'une ou plusieurs classes latentes (absence ou présence de clustering), et nous montrons leur consistence.Dans le deuxième travail, les variables latentes sont des positions dans l'espace ℝd, admettant une densité f, et la probabilité de connexion dépend de la distance entre les positions des noeuds. Les clusters sont définis comme les composantes connexes de l'ensemble de niveau t > 0 fixé de f, et l'objectif est d'en estimer le nombre à partir du graphe. Nous estimons la densité en les positions latentes des noeuds grâce à leur degré, ce qui permet d'établir une correspondance entre les clusters et les composantes connexes de certains sous-graphes du graphe observé, obtenus en retirant les nœuds de faible degré. En particulier, nous en déduisons un estimateur du nombre de clusters et montrons saconsistence en un certain sen

Computational socioeconomics

Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies