Jan Lambooy retired in October 2002. When Jan was asked how he wanted to celebrate this occasion, he was adamant that no great festivities should take place. Characteristically, Jan wanted just a scientific conference so he “could learn something from it” and, as he insisted, no great festivities. So that is what we did and a conference was organised in Amsterdam on 25 October 2002, hosted by the Faculty of Economics and Econometrics of the University of Amsterdam. Friends of Jan’s from academia in the Netherlands and abroad participated and thus paid homage to Jan, both as a scientist and as a person. We are now very proud to present this festschrift, firstly as the palpable result of this conference and secondly as a token of sincere respect and great affection for Jan. Edited volumes run the danger of being a hotchpotch of contributions on a wide variety of topics. Here, we have explicitly focused on a central theme in contemporary economic geography and regional science, namely the relationship between learning, innovation and clustering. Internationally renowned scientists made both theoretical and empirical contributions to this volume. We think this book constitutes a broad palette of contemporary thinking and research on the relationship between spatial concentration and innovation and hope it will play a significant role in future debates on this issue.

This title was first published in 2000: This text presents a study of collective learning, networking and high-technology regions in Europe. It first provides an overview of the subject area, then goes on to discuss topics such as the role of inter-SME networking and collective learning processes in European high-technology milieux.

This book studies the determinants of cluster survival by analyzing their adaptability to change in the economic environment. Linking theoretic knowledge with empirical observations, a simulation model (based in the N/K method) is developed, which explains when and why the cluster's architecture assists or hampers adaptability. It is found that architectures with intermediate degrees of division of labor and more collective governance forms foster adaptability.

This book discusses liberal arts education and liberal arts colleges in the context of East Asia, specifically focusing on Japan, China and S. Korea where it has become an emerging issue in higher education in recent years. It first explores the development, concepts and challenges of liberal arts education and liberal arts colleges in East Asia. It then delineates the implications of the best practices of selected liberal arts colleges inside and outside East Asia, and offers policy and pedagogical guidelines for the future of liberal arts colleges and programs in East Asia and beyond.

The Handbook of Group Counseling and Psychotherapy is a comprehensive reference guide for group practitioners and researchers alike. Each chapter reviews the literature and current research as well as offers suggestions for practice in the psycho educational arena, counseling, and therapy groups. The handbook encourages the notion that the field is improved through increased collaboration between researchers and practitioners. Through a review of cutting-edge research and practice, the handbook includes: 48 chapters by renowned experts in group work The history and theory of group work Topics across the lifespan An entire section on multicultural issues A variety of clinical problems and settings Appendices include the Association for Specialists in Group Work Training Standards, Best Practice Standards, and Principles for Diversity-Competent Group Workers The Handbook of Group Counseling and Psychotherapy, the most comprehensive reference devoted to this rapidly growing field, is essential for graduate students, academics, researchers, professionals, and librarians serving the group therapy community.

"This book offers the latest research within the field of HAIS, surveying the broad topics and collecting case studies, future directions, and cutting edge analyses, investigating biologically inspired algorithms such as ant colony optimization and particle swarm optimization"--

This book presents the latest research advances in complex network structure analytics based on computational intelligence (CI) approaches, particularly evolutionary optimization. Most if not all network issues are actually optimization problems, which are mostly NP-hard and challenge conventional optimization techniques. To effectively and efficiently solve these hard optimization problems, CI based network structure analytics offer significant advantages over conventional network analytics techniques. Meanwhile, using CI techniques may facilitate smart decision making by providing multiple options to choose from, while conventional methods can only offer a decision maker a single suggestion. In addition, CI based network structure analytics can greatly facilitate network modeling and analysis. And employing CI techniques to resolve network issues is likely to inspire other fields of study such as recommender systems, system biology, etc., which will in turn expand CI’s scope and applications. As a comprehensive text, the book covers a range of key topics, including network community discovery, evolutionary optimization, network structure balance analytics, network robustness analytics, community-based personalized recommendation, influence maximization, and biological network alignment. Offering a rich blend of theory and practice, the book is suitable for students, researchers and practitioners interested in network analytics and computational intelligence, both as a textbook and as a reference work.

Offering a wide range of programming examples implemented in MATLAB, Computational Intelligence Paradigms: Theory and Applications Using MATLAB presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and pr

Applied Univariate, Bivariate, and Multivariate Statistics Using Python A practical, “how-to” reference for anyone performing essential statistical analyses and data management tasks in Python Applied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied. Most of the datasets used in the book are small enough to be easily entered into Python manually, though they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, making the book perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python. Readers will also benefit from the inclusion of: A review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors An introduction to Python, exploring how to communicate with Python A treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data management An introduction to topics such as ANOVA, MANOVA and discriminant analysis, regression, principal components analysis, factor analysis, cluster analysis, among others, exploring the nature of what these techniques can vs. cannot do on a methodological level Perfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python.