1,720,952 research outputs found
Behaviour and Stability of Interconnected Systems: From Biological Applications to Opinion Dynamics
No full textAn interconnected system is composed of multiple well-defined self-contained subsystems that interact among them and that together create collective behaviours. We can find many examples of interconnected systems in real life. Ranging from biological systems, such as the growth and interaction of populations in diverse and spatially distributed environments, to electric grids connecting power-generating sources, buildings and infrastructures in a country. When studying interconnected systems, a fundamental and natural question is how the properties and characteristics of the individual subsystems and the way they are connected relate to the collective behaviour of the complete system. That is the driving question of the present dissertation. Given that interconnected systems can be found in a wide variety of contexts, their representation and specific research interests can be equally varied. Because of this, it is impossible to answer the aforementioned question uniquely for all interconnected systems, and specific cases must be considered. In this dissertation, we consider two types of interconnected systems: a general class of uncertain multiple-input-multipleoutput (MIMO) systems, and agent-based opinion formation models. The investigation of uncertain MIMO interconnected systems is focused on providing topology-independent conditions for robust stability. The primary motivation for this approach is that, in real systems, it is costly or even impossible to have complete and accurate information on the network topology and subsystem parameters and dynamics. However, it is of critical interest to guarantee the system’s stability. Therefore we need stability conditions that require only partial information about the network and the subsystems to ensure the system’s stability. By studying these systems both in the time and frequency domain, we are able to provide conditions thatmeet these requirements. As for agent-based opinion formation models, we assume that each individual (or agent) in a population has an opinion about a statement. By exchanging opinions among themselves, the agents update their own internal opinion, resulting in a collective dynamic of opinion evolution. When studying these systems, the interests shifts from stability conditions, to a characterisation of the relation between the agents’ individual traits and qualitative properties of the opinion distribution in the population. Several techniques and approaches to analyse opinion formation models are proposed and applied to multiple models, one of which is new to this dissertation. The collective study of the previously mentioned interconnected systems requires the use of multiple and diverse analysis techniques and approaches, from analytical methods based on the Nyquist criterion, Bauer-Fike theorem, and Lyapunov functions to qualitative and numerical analysis techniques like histograms and binomial proportion confidence intervals. It is our hope that some of the presented results, methods, or ideas may advance the knowledge frontier in this scientific field, sparkle new research directions, and either directly or indirectly prove some value to society
Topology-Independent Robust Stability Conditions for Uncertain MIMO Networks
No full textWe give a sufficient and a necessary condition for the topology-independent robust stability of networked systems formed by uncertain MIMO systems. Both conditions involve constants associated with the nominal node dynamics and arc interconnection matrices, the uncertainty bounds, and the maximum connectivity degree of the network; they are scalable (they can be checked locally), independent of the network topology and even of the number of nodes and arcs, and hold for networks of heterogeneous MIMO systems and interconnection matrices, with heterogeneous uncertainties. The dual cases of 1-norm and ∞-norm bounds are considered. In both cases, if the systems at the nodes are diagonal, we get a necessary and sufficient condition. We apply our results to the topology-independent robust stability analysis of a case-study from cancer biology.Accepted Author ManuscriptTeam Tamas Keviczk
Optimal duration and planning of switching treatments taking drug toxicity into account: a convex optimisation approach
No full textWe consider a multi-compartment evolutionary model representing growth, mutation and migration of cancer cells, as well as the effect of drugs, and we design optimal switching targeted cancer therapies where a single drug, or suitable drug combination, is given at each time so as to minimise not only the overall tumor size over a finite horizon, but also drug-provoked side effects. The strong diagonally- dominant structure of the model allows to solve the problem via convex optimisation. We provide an algorithm that yields optimality throughout the whole treatment duration by solving the convex optimisation problem with different horizons, and show how dwell time can be enforced via heuristics. Also the optimal treatment duration can be computed via convex optimisation. The proposed approaches are applied to a model of ALK-rearranged lung carcinoma.Accepted Author ManuscriptTeam Tamas Keviczk
Probabilistic analysis of agent-based opinion formation models
No full textWhen agent-based models are developed to capture opinion formation in large-scale populations, the opinion update equations often need to embed several complex psychological traits. The resulting models are more realistic, but also challenging to assess analytically, and hence numerical analysis techniques have an increasing importance in their study. Here, we propose the Qualitative Outcome Likelihood (QOL) analysis, a novel probabilistic analysis technique aimed to unravel behavioural patterns and properties of agent-based opinion formation models, and to characterise possible outcomes when only limited information is available. The QOL analysis reveals which qualitative categories of opinion distributions a model can produce, brings to light their relation to model features such as initial conditions, agent parameters and underlying digraph, and allows us to compare the behaviour of different opinion formation models. We exemplify the proposed technique by applying it to four opinion formation models: the classical Friedkin-Johnsen model and Bounded Confidence model, as well as the recently proposed Backfire Effect and Biased Assimilation model and Classification-based model.Team Tamas Keviczk
Classification-Based Opinion Formation Model Embedding Agents’ Psychological Traits
No full textWe propose an agent-based opinion formation model characterised by a two-fold novelty. First, we realistically assume that each agent cannot measure the opinion of its neighbours about a given statement with infinite resolution and accuracy, and hence it can only perceive the opinion of others as agreeing much more, or more, or comparably, or less, or much less (than itself) with that given statement. This leads to a classification-based rule for opinion update. Second, we consider three complementary agent traits suggested by significant sociological and psychological research: conformism, radicalism and stubbornness. We rely on World Values Survey data to show that the proposed model has the potential to predict the evolution of opinions in real life: the classification-based approach and complementary agent traits produce rich collective behaviours, such as polarisation, consensus, and clustering, which can yield predicted opinions similar to survey results.Team Tamas Keviczk
A framework to analyze opinion formation models
No full textComparing model predictions with real data is crucial to improve and validate a model. For opinion formation models, validation based on real data is uncommon and difficult to obtain, also due to the lack of systematic approaches for a meaningful comparison. We introduce a framework to assess opinion formation models, which can be used to determine the qualitative outcomes that an opinion formation model can produce, and compare model predictions with real data. The proposed approach relies on a histogram-based classification algorithm, and on transition tables. The algorithm classifies an opinion distribution as perfect consensus, consensus, polarization, clustering, or dissensus; these qualitative categories were identified from World Values Survey data. The transition tables capture the qualitative evolution of the opinion distribution between an initial and a final time. We compute the real transition tables based on World Values Survey data from different years, as well as the predicted transition tables produced by the French-DeGroot, Weighted-Median, Bounded Confidence, and Quantum Game models, and we compare them. Our results provide insight into the evolution of real-life opinions and highlight key directions to improve opinion formation models.Team Tamas Keviczk
Topology-independent robust stability for networks of homogeneous MIMO systems
No full textWe study dynamic networks described by a directed graph where the nodes are associated with MIMO systems with transfer-function matrix F(s), representing individual dynamic units, and the arcs are associated with MIMO systems with transfer-function matrix G(s), accounting for the dynamic interactions among the units. In the nominal case, we provide a topology-independent condition for the stability of all possible dynamic networks with a maximum connectivity degree, regardless of their size and interconnection structure. When node and arc transfer-function matrices are affected by norm-bounded homogeneous uncertainties, the robust condition for size- and topology-independent stability depends on the uncertainty magnitude. Both conditions, expressed as constraints for the Nyquist diagram of the poles of the transfer-function matrix H(s) = F(s)G(s), are scalable and can be checked locally to guarantee stability-preserving “plug-and-play” addition of new nodes and arcs.Team Tamas Keviczk
Fair and Sparse Solutions in Network-Decentralized Flow Control
No full textWe proposed network-decentralized control strategies, in which each actuator can exclusively rely on local information, without knowing the network topology and the external input, ensuring that the flow asymptotically converges to the optimal one with respect to the p -norm. For 1 < p < ∞ , the flow converges to a unique constant optimal up∗. We show that the state converges to the optimal Lagrange multiplier of the optimization problem. Then, we consider networks where the flows are affected by unknown spontaneous dynamics and the buffers need to be driven exactly to a desired set-point. We propose a network-decentralized proportional-integral controller that achieves this goal along with asymptotic flow optimality; now it is the integral variable that converges to the optimal Lagrange multiplier. The extreme cases p=1 and p=∞ are of some interest since the former encourages sparsity of the solution while the latter promotes fairness. Unfortunately, for p=1 or p=∞ these strategies become discontinuous and lead to chattering of the flow, hence no optimality is achieved. We then show how to approximately achieve the goal as the limit for p 1 or p ∞.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Tamas Keviczk
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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