Space-Time Sampling for Network Observability

Designing sparse sampling strategies is one of the important components in having resilient estimation and control in networked systems as they make network design problems more cost-effective due to their reduced sampling requirements and less fragile to where and when samples are collected. It is shown that under what conditions taking coarse samples from a network will contain the same amount of information as a more finer set of samples. Our goal is to estimate initial condition of linear time-invariant networks using a set of noisy measurements. The observability condition is reformulated as the frame condition, where one can easily trace location and time stamps of each sample. We compare estimation quality of various sampling strategies using estimation measures, which depend on spectrum of the corresponding frame operators. Using properties of the minimal polynomial of the state matrix, deterministic and randomized methods are suggested to construct observability frames. Intrinsic tradeoffs assert that collecting samples from fewer subsystems dictates taking more samples (in average) per subsystem. Three scalable algorithms are developed to generate sparse space-time sampling strategies with explicit error bounds.Comment: Submitted to IEEE TAC (Revised Version

Transition Management: toward a prescriptive model for multi-level governance systems

Over the last decades, we have witnessed a shift from the centralized government-based nation-state, towards liberalized, market based and decentralized decision-making structures. Due to societal developments the power of central government to make policies and implement these has decreased, leading to increasingly diffuse policy-making structures and processes stratified across sub-national, national and supra-national levels of government (Hooghe and Marks 2001). Generally referred to with the term ‘governance’ (Kooiman 1993), the current practice of government in making policy is in interaction with a diversity of societal actors. At the European level, this development has led to multi-level, participatory decision-making structures in which for example regions are dealing directly with EU-offices, NGO’s and businesses are involved in the development of policies and top-down decisions are limited to the politically most controversial issues. But governance has also become common practice at the global as well as on a regional scale, where influence of non-governmental organizations (NGO’s), business and science slowly becomes part of policy-making

Modernizing Russia: Round III. Russia and the other BRIC countries: forging ahead, catching up or falling behind?

The term 'BRIC countries' - Brazil, Russia, India, and China - traces its roots to investment banking, Goldman Sachs coined the term in 2001. The idea of large emerging economies catching up with, and challenging, the West has captured social scientists and policy-makers alike. However, the sheer size and different historical legacies dictate that there are enormous differences between the BRIC economies. Russiaÿs situation is in three ways unique among the BRIC countries. First, Russia was an industrialized nation long before the others, secondly, it experienced unprecedented economic decline in the 1990s and by 2008 Russia barely reached the GDP level of 1989; thirdly, unlike Brazil, China, India and in fact most of the developing world, Russia is not a member of the World Trade Organization (WTO). These unique features beg the following questions that this document seeks to (at least tentatively) answer: first, what is the structural legacy of the decline in the 1990s in terms of technological and industrial capabilities in Russia; and second, what can and should Russia learn from the WTO experience of the rest of the BRIC economies until today. We argue, in brief, that while the decline of the 1990s is relatively well-known and documented on the macro-level (GDP) and more controversially in some of its micro-level and sociological impacts, there seems to be little awareness of the magnitude of devastation that took place during this period within Russiaÿs industry. Along with a massive increase in income from natural resources, a partial disintegration of the R&D system, and a greatly diminished policy capacity, the structural changes of the 1990s continue to pose grave challenges to Russiaÿs economic policy making. In fact, in many areas Russiaÿs technological and industrial capabilities have simply been lost.