State Space

We show the existence ofaverage cost (AC-) optimal policy for an inventory system withuncountable state space; in fact, the AC-optimal cost and an AC-optimal stationary policy areexplicitly computed. In order to do this, we use a variant of thevanishing discount factor approach, which have been intensively studied in recent years but the available results not cover the inventory problem we are interested in.

The contributions to this book explore the spatiality of state power in historical and contemporary capitalism. The observation that states are spatial entities may seem self-evident to many readers. Modern states are demarcated from each other by territorial boundaries that they patrol and regulate for military, police, economic, political, demographic, and many other reasons. Likewise, modern states are internally divided into diverse territorial jurisdictions and administrative subdivisions. The most cursory glance at a contemporary world political map seems to confirm the self-evident character of these spatial properties of the modern state. For such maps depict a plethora of distinct state territories, large and small, separated from one another by a global grid of boundaries, and generally demarcated as colour-coded 'blocks' of space on a flat surface. Thus, each individual state is represented as a kind of container that separates an 'inside' of domestic political interactions from an 'outside' of international or interstate relations (Walker 1993). This container-metaphor also underpins conventional depictions of intra-national political spaces. Thus, each regional or local jurisdiction is viewed as a self-enclosed political territory within a nested hierarchy of geographical arenas contained within each other like so many Russian dolls. Our introduction identifies and comments on an emergent research agenda that is concerned with the production and transformation of state space. This agenda is particularly focused on the restructuring of territorially demarcated forms of state power and the recent decentring of nationally-scaled forms of state activity. It also highlights the differential effects of newly emergent political and state spaces on the structural and strategic capacities of the state, the mobilization of social forces and the dynamics and effectivity of political struggles. Taken together, as we explore below, these complementary research agendas are systematically challenging the entrenched geographical assumptions within mainstream approaches to state space.

Recent scholarship has highlighted the central role of India's ‘new middle class’ in gentrifying and ‘cleaning up’ its cities. According to this literature, this class experienced a political awakening in the 1990s and began mobilizing to reclaim urban space from the poor. Using the example of Delhi's Bhagidari scheme, a governance experiment launched in 2000, I argue that urban middle-class power did not emerge from internal changes within this class itself (as is commonly argued), but was rather produced by the machinations of the local state. In particular, I show how Bhagidari has realigned the channels by which citizens can access the state on the basis of property ownership. In so doing, it has undermined the electoral process dominated by the poor, and privileged property owners' demands for a ‘world-class’ urban future. By examining the ‘new state spaces’ it constructs, I show how Bhagidari has effectively gentrified the channels of political participation, respatializing the state by breaking the informal ties binding the unpropertied poor to the local state and thereby removing the obstacles to large-scale slum demolitions. In making this argument, the article introduces a unique approach to mapping state space that aims to reveal the relationship between state form and political participation.

By asserting the realities of settler coloniality in the evolution of southwestern Mindanao as a frontier space, this paper addresses a phenomenon consistently disavowed in mainstream historiographical accounts of the region. It raises three other broad concerns. First, that while resources, territory, and population are factors that figure centrally in the political economy of settler colonialism, they may equally be said to be bound up with the practices of state sovereignty in general, involving ‘white’ settler colonial relations as well as ‘indigenous’ ones competing for spatial hegemony at the frontier. Second, that these processes are embedded in historical narratives of the frontier and its inhabitants that echo, in similar or divergent ways, well into the present. Finally, that settler colonial studies would benefit from a stronger engagement with the political geographic literature and with analyses of the production of (post)colonial state space.

Why and how was the territorialized state form disseminated through colonial expansion? To begin to answer this question, this study proposes a relational account of the production of territorialized state space, drawing on empirical evidence from two understudied cases of colonial expansion in the early 20th century: Spain in Morocco and Italy in Libya. Drawing on colonial and local archival sources, I demonstrate how colonial territoriality resulted from a violent clash between an aspiring colonial power and a reactive, rural counter-state building movement, led by the Amir Abd al-Krim in the Rif Mountains of northern Morocco and the Sanusi leader, Omar al-Mokhtar, in Cyrenaica in eastern Libya. Territorialization was not imposed from the outside by a European colonial power. Rather, it was produced relationally through violent interactions between the colonial state and a local autonomous political entity. This analysis contributes to the still-nascent study of colonial state space and to contemporary policy debates about political order in North Africa and the Middle East by emphasizing the importance of local political mobilization, the complexity of interactions catalyzed across local and translocal scales by colonial expansion, and the high levels of physical violence endemic to the production of territorialized state space.


Jonathan Wyrtzen , (2017), Colonial War and the Production of Territorialized State Space in North Africa, in Søren Rud , Søren Ivarsson (ed.) Rethinking the Colonial State (Political Power and Social Theory, Volume 33) Emerald Publishing Limited, pp.151 - 173

We propose a method for learning dialogue management policies from a fixed dataset. The method is designed for use with" Information State Update"(ISU)-based dialogue systems, which represent the state of a dialogue as a large set of features, resulting in a very large state space and a very large policy space. To address the problem that any fixed dataset will only provide information about small portions of these state and policy spaces, we propose a hybrid model which combines reinforcement learning (RL) with ...

Criminal activity acts like a tax on the entire economy: it discourages domestic and foreign direct investments, it reduces firms' competitiveness, and reallocates resources creating uncertainty and inefficiency. Although the impact of economic variables on crime has been widely investigated, there is not much concern about crime also affecting the overall economic performance. This work aims to bridge this gap by presenting an empirical analysis of the macroeconomic consequences of criminal activity. Italy is the case study for the time span 1979–2002. Dealing with a state space framework, a time varying parameter approach is employed to measure the impact of criminality on real Gross Domestic Product along time, and to measure the asymmetric impact in recession and expansion periods. The analysis is completed evaluating the effects of crime fluctuations in the long period by an impulse response analysis.

Demand for learning, design and decision making is higher than ever before. Autonomous vehicles need to learn how to ride safely by recognizing pedestrians, traffic signs, and other cars. Companies and consumers need to identify possible changes in the environment and adapt their strategies relatively fast to stay competitive. The complexity of biological systems necessitates incorporation of the biological knowledge with mathematical models to find effective treatments for many chronic fatal diseases. This dissertation addresses some of the critical issues concerning estimation, identification and learning of complex dynamical systems observed through noisy data.
Nonlinear state-space models are a popular class of time series models with numerous ap- plications in fields such as cyber-physical systems, economics, biology and more. However, the applicability of the existing techniques for inference of large systems or systems with big data sets, two common scenarios in many real-world applications, becomes impossible. We have developed a multi-fidelity Bayesian optimization algorithm for the inference of general nonlinear state-space models (MFBO-SSM), which enables simultaneous sequential selection of parameters and approximators. The accuracy and speed of the algorithm are demonstrated by numerical experiments using synthetic gene expression data from a gene regulatory network model and real data from the VIX stock price index.
Along with estimation and identification, control of dynamical systems has been on the center of attention is many years. A Markov Decision Processes (MDPs) is a rich framework for modeling the dynamical systems in varieties of fields. The optimal control of MDP with known dynamics and finite state and action spaces is achievable using the Dynamic programming (DP) framework. However, in complex applications, there is often uncertainty about the system dynamics. In addition, many practical problems have large or continuous state and action spaces which hinders the simple application of DP. Reinforcement learning is a powerful technique widely used for adaptive control of MDPs with unknown dynamics. Existing RL techniques developed for MDPs with unknown dynamics rely on data that is acquired via interaction with the system or via simulation. While this is feasible in areas such as robotics or speech recognition, in other applications such as biology, manufacturing, cyber-physical systems, and marketing, there is either a lack of reliable simulators or inaccessibility to the real system due to practical limitations, including cost, ethical, and physical considerations. We have developed Bayesian decision making frame- work for control of MDPs with unknown dynamics and large, possibly continuous, state, action, and parameter spaces in data-poor environments. The effectiveness of the proposed framework is demonstrated using a simple dynamical system model with continuous state and action spaces, as well as a more complex model for a metastatic melanoma gene regulatory network observed through noisy synthetic gene expression data.
Finally, we have studied an instance of partially-observed dynamical systems with Boolean state variable, called partially-observed Boolean dynamical systems (POBDS). This signal model has applications in many areas such as genomics/metagenomics, brain networking signals, fault propagation in sensor networks, communication and more. We have developed sets of optimal tools for this signal model, which most are the first exact solutions for the entire class of nonlinear non- Gaussian state-space models. These include optimal minimum mean-square error (MMSE) state estimator for known POBDS, which is called the Boolean Kalman Smoother (BKS). For POBDS with a significant uncertainty in the modeling process, we developed the maximum-likelihood and the optimal Bayesian adaptive filters for simultaneous estimation of the state and parameters, capable of tackling discrete, continuous or mixture of discrete/continuous parameters. In addition, tools for control and learning for this signal model have been introduced. The performance and applicability of all methods have been shown through important problems in genomics domain.

Abstract: The missile interceptors integrated guidance and control technology for achieving the hit-to-kill accuracy against targets performing evasive maneuvers including spiraling motion is developed on the basis of high (second) order sliding mode control (HOSM). The integration of guidance and flight control systems is achieved in a two-loop guidance and flight control system designed in the combined state space of

Recently, there has been considerable interest in the use of Model Checking for Systems Biology. Unfortunately, the state space of stochastic biological models is often too large for classical Model Checking techniques. For these models, a statistical approach to Model Checking has been shown to be an effective alternative. Extending our earlier work, we present the first algorithm for performing statistical Model Checking using Bayesian Sequential Hypothesis Testing. We show that our Bayesian approach outperforms current statistical Model Checking techniques, which rely on tests from Classical (aka Frequentist) statistics, by requiring fewer system simulations. Another advantage of our approach is the ability to incorporate prior Biological knowledge about the model being verified. We demonstrate our algorithm on a variety of models from the Systems Biology literature and show that it enables faster verification than state-of-the-art techniques, even when no prior knowledge is available.

Modern system approaches will be used to realize a 5 th order dynamic differential equation as an analogue circuit using resistors, capacitors, and operational amplifiers. The resulting circuit will be tested using Matlab® and those results will be verified using Multisim. The state space approach will be used to convert the 5 th order differential equation to five first order differential equations. Using the Multisim simulator, various input types were tested across the input terminals of the analog computer and the results were recorded. Index Terms-Analog computer, state space, operational amplifier, resistor, capacitor, modern system approach.

A general linear controller design method for a class of hyperbolic linear partial differential equation (PDEs) systems is presented. This is achieved by using an infinite-dimensional Hilbert state-space description with infinite-dimensional (distributed) input and output. A state LQ-feedback operator is computed via the solution of a matrix Riccati differential equation in the space variable. The proposed method is applied to a fixed-bed chemical reactor control problem, where one elementary reaction takes place. An optimal controller is designed for linearized fixed-bed reactor model, it is applied to the original nonlinear model and the resulting closed-loop stability is analyzed. Numerical simulations are performed to show the performance of the designed controller.