Learning Under Imperfections By Networked Agents
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Learning Under Imperfections by Networked Agents
Distributed learning deals with the problem of optimizing aggregate cost functions by networked agents from streaming data. This scenario arises in many contexts including distributed estimation, machine learning, resource allocation, and in the modeling of flocking and swarming behavior by biological networks. Among several available solutions such as consensus and incremental strategies, the class of diffusion strategies has proven to be particularly attractive because these techniques are scalable, robust, fully-distributed, and endow networks with real-time adaptation and learning abilities. One key challenge in real applications is that networked agents generally face many types of asynchronous imperfections, such as random link failures, random data arrival times, noisy links, random topology changes, agents turning on and off randomly, and even drifting objectives. This dissertation provides a detailed analysis of the stability and performance of asynchronous diffusion strategies for solving distributed optimization and adaptation problems over networks in the presence of such imperfections. Conditions are developed to ensure the stability of the mean-square and mean-fourth-order moments of the network error vectors; closed-form expressions are derived to reveal how the network parameters influence the learning behavior; and the performance of the asynchronous networks is then compared against centralized solutions and synchronous networks. One notable conclusion is that the mean-square performance of asynchronous networks degrades only in the order of & mu, which is a small step-size parameter, while the convergence rate remains largely unaltered. A second notable conclusion is that even under the influence of asynchronous events, all agents in the adaptive network can still reach an O(& musuper1+ & gamma\super)$ near-agreement with some constant & gamma> 0, while approaching the desired solution within O(& mu) accuracy. These theoretical results provide a solid justification for the remarkable resilience of cooperative networks in the face of random imperfections at multiple levels: agents, links, data arrivals, and topology. The dissertation also examines a second challenging form of uncertainty arising from agents in a network pursuing different objectives or observing data arising from different unknown models. In these cases, indiscriminate cooperation will lead to undesired results. A useful adaptive clustering and learning strategy is developed in order to allow agents to learn which neighbors should be trusted and which other neighbors should be ignored. The resulting procedure enables agents to identify their grouping and to attain improved learning performance.
Network Models in Economics and Finance
Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.
Network-Oriented Modeling
This book presents a new approach that can be applied to complex, integrated individual and social human processes. It provides an alternative means of addressing complexity, better suited for its purpose than and effectively complementing traditional strategies involving isolation and separation assumptions. Network-oriented modeling allows high-level cognitive, affective and social models in the form of (cyclic) graphs to be constructed, which can be automatically transformed into executable simulation models. The modeling format used makes it easy to take into account theories and findings about complex cognitive and social processes, which often involve dynamics based on interrelating cycles. Accordingly, it makes it possible to address complex phenomena such as the integration of emotions within cognitive processes of all kinds, of internal simulations of the mental processes of others, and of social phenomena such as shared understandings and collective actions. A variety of sample models – including those for ownership of actions, fear and dreaming, the integration of emotions in joint decision-making based on empathic understanding, and evolving social networks – illustrate the potential of the approach. Dedicated software is available to support building models in a conceptual or graphical manner, transforming them into an executable format and performing simulation experiments. The majority of the material presented has been used and positively evaluated by undergraduate and graduate students and researchers in the cognitive, social and AI domains. Given its detailed coverage, the book is ideally suited as an introduction for graduate and undergraduate students in many different multidisciplinary fields involving cognitive, affective, social, biological, and neuroscience domains.