Suchergebnisse

Der Autor beschäftigt sich mit Machtprozessen in Politiknetzwerken, indem er eine systematische Untersuchung des Zusammenhanges zwischen Macht, Interessen und der 'Issue-Position' der beteiligten Akteure hinsichtlich politischer Entscheidungen vornimmt. Er definiert 'Macht' als Bestimmungsmoment von 'kollektiven Ergebnissen' in sozialen Systemen und fragt danach, inwieweit das einzelne Ergebnis durch Tausch, Verhandlung oder Konflikt erzielt wurde. Er stellt eine Modellierung des politischen Entscheidungsansatzes anhand des 'Zweistufenmodells', des 'Konfliktmodells' und des 'Tauschmodells' am Fallbeispiel einer Unternehmenssanierung in den Niederlanden vor. Er vergleicht die Modelle hinsichtlich ihrer Operationalisierung von 'Macht' und Prognosekraft in Bezug auf die getroffenen politischen Entscheidungen der Akteure. Das Ergebnis zeigt, daß durch die Integration von unterschiedlichen Modellen eine realistische Konzeptualisierung von Machtprozessen erreicht werden kann. (ICI)

Two dynamic models of collecuve decision maklng are Introduced and lllustrated wlth a simple example. A more extensive presentation and appllcauon concerning the European Community can be found In Bueno de Mesqulta and Stokman (19941. The two dynamlc models reflect two alternative views of collective decision making and politics. The flrst, represented In Ule expected utility model, conceives of collective decision making as conflict resolution, a non-cooperative game, fundamentally different from exchange relaUons In economics. The second. represented in the exchange model of Stokman and Van Oosten (19941, does not see fundamental differences between economic exchanges and political decision making. Dynamic Models. Collective Decision Making. Conflict Resolution, Economlc Exchange Relations.

This book answers the question of whether we can apply evolutionary theories to our understanding of the development of social structures. Social networks have increasingly become the focus of many social scientists as a way of analyzing these social structures. While many powerful network analytic tools have been developed and applied to a wide range of empirical phenomena, understanding the evolution of social organization still requires theories and analyses of social network evolutionary processes. Researchers from a variety of disciplines have combined their efforts in what is an indicati

Introduction to: The proceedings of a conference that was held on the analysis of repeated cross sections at the University of Nijmegen on June 15±16, 2000. This two-days meeting was attended by 75 participants from seven different countries (AUS, B, F, GER, NL, USA, UK) and from a wide array of academic fields and disciplines: statistics, econom(etr)ics, social science methodology, medicine/epidemiology, demography, political science, psychology, sociology, criminology.

This article evaluates two competing dynamic policy-network models and one static network model by applying them to local politics in Amsterdam. In the dynamic models an influence relation results from the acceptance of an influence request. The first model, Control Maximization, represents the view that politics are primarily power driven, and the second, Policy Maximization, policy driven. In the static model (the Two-Stage), network relations are empirically investigated as in other policy-network models and used as a benchmark for evaluating the dynamic models. Policy Maximization is shown to be the most accurate predictor of decision outcomes, better even than the static model, and to generate richer networks. However, both dynamic models generate networks that are too hierarchical.

This article evaluates two competing dynamic policy-network models and one static network model by applying them to local politics in Amsterdam. In the dynamic models an influence relation results from the acceptance of an influence request. The first model, Control Maximization, represents the view that politics are primarily power driven, and the second, Policy Maximization, policy driven. Zn the static model (the Two-Stage), network relations are empirically investigated as in other policy-network models and used as a benchmark for evaluating the dynamic models. Policy Maximization is shown to be the most accurate predictor of decision outcomes, better even than the static model, and to generate richer networks. However, both dynamic models generate networks that are too hierarchical.