Karoline Wiesner | University of Bristol (original) (raw)
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Papers by Karoline Wiesner
Humanities and Social Science Communications, 2020
Political scientists have conventionally assumed that achieving democracy is a one-way ratchet. O... more Political scientists have conventionally assumed that achieving democracy is a one-way ratchet. Only very recently has the question of "democratic backsliding" attracted any research attention. We argue that democratic instability is best understood with tools from complexity science. The explanatory power of complexity science arises from several features of complex systems. Their relevance in the context of democracy is discussed. Several policy recommendations are offered to help (re)stabilize current systems of representative democracy.
Emergence, Complexity and Computation, 2015
Encyclopedia of Complexity and Systems Science, 2009
Computational Complexity, 2012
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2010
Nature intrinsically computes. It has been suggested that the entire universe is a computer, in p... more Nature intrinsically computes. It has been suggested that the entire universe is a computer, in particular, a quantum computer. To corroborate this idea we require tools to quantify the information processing. Here we review a theoretical framework for quantifying information processing in a quantum dynamical system. So-called intrinsic quantum computation combines tools from dynamical systems theory, information theory, quantum mechanics, and computation theory. We will review how far the framework has been developed and what some of the main open questions are. On the basis of this framework we discuss upper and lower bounds for intrinsic information storage in a quantum dynamical system.
… Summaries of Uppsala Dissertations from the …, 2003
Stochastic finite-state generators are compressed descriptions of infinite time series. Alternati... more Stochastic finite-state generators are compressed descriptions of infinite time series. Alternatively, compressed descriptions are given by quantum finitestate generators [K. Wiesner and J. P. Crutchfield, Physica D 237, 1173 (2008)].
07/20/2015-07/24/2015, 2015
We present a new method for inferring hidden Markov models from noisy time sequences without the ... more We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al., and generates so called causal state models, equivalent to hidden Markov models. This method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing v...
PLoS ONE, 2012
We present a new method for inferring hidden Markov models from noisy time sequences without the ... more We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al. and generates so called causal state models, equivalent in structure to hidden Markov models. The new method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing values for the transition rates in good agreement with previous results and with results obtained using existing maximum likelihood based methods. The method differs markedly from previous Markov-model reconstructions in being able to uncover truly hidden states.
Chemical Physics, 2003
We present multicoincidence spectra of nitrogen, formic acid and methyl methacrylate. We demonstr... more We present multicoincidence spectra of nitrogen, formic acid and methyl methacrylate. We demonstrate how to probe the local symmetry of molecular orbitals from molecules core excited with linearly polarized synchrotron radiation. The intensity distribution of the photoelectron photo-ion photo-ion coincidence (PEPIPICO) spectrum reflects the selectivity and localization of core excitation by polarized light. By simulating the spectra the angular dependence of the fragmentation is determined.
Stochastic finite-state generators are compressed descriptions of infinite time series. Alternati... more Stochastic finite-state generators are compressed descriptions of infinite time series. Alternatively, compressed descriptions are given by quantum finitestate generators [K. Wiesner and J. P. Crutchfield, Physica D 237, 1173 (2008)].
Eprint Arxiv Quant Ph 0608206, Aug 27, 2006
We introduce stochastic and quantum finite-state transducers as computation-theoretic models of c... more We introduce stochastic and quantum finite-state transducers as computation-theoretic models of classical stochastic and quantum finitary processes. Formal process languages, representing the distribution over a process's behaviors, are recognized and generated by suitable specializations. We characterize and compare deterministic and nondeterministic versions, summarizing their relative computational power in a hierarchy of finitary process languages. Quantum finite-state transducers and generators are a first step toward a computation-theoretic analysis of individual, repeatedly measured quantum dynamical systems. They are explored via several physical systems, including an iterated beam splitter, an atom in a magnetic field, and atoms in an ion trap--a special case of which implements the Deutsch quantum algorithm. We show that these systems' behaviors, and so their information processing capacity, depends sensitively on the measurement protocol.
Mathematical models are an essential component of quantitative science. They generate predictions... more Mathematical models are an essential component of quantitative science. They generate predictions about the future, based on information available in the present. In the spirit of simpler is better; should two models make identical predictions, the one that requires less input is preferred. Yet, for almost all stochastic processes, even the provably optimal classical models waste information. The amount of input information they demand exceeds the amount of predictive information they output. Here we show how to systematically construct quantum models that break this classical bound, and that the system of minimal entropy that simulates such processes must necessarily feature quantum dynamics. This indicates that many observed phenomena could be significantly simpler than classically possible should quantum effects be involved.
We introduce ways to measure information storage in quantum systems, using a recently introduced ... more We introduce ways to measure information storage in quantum systems, using a recently introduced computation-theoretic model that accounts for measurement effects. The first, the quantum excess entropy, quantifies the shared information between a quantum process's past and its future. The second, the quantum transient information, determines the difficulty with which an observer comes to know the internal state of a quantum process through measurements. We contrast these with von Neumann entropy and quantum entropy rate and provide a closed-form expression for the latter for the class of deterministic quantum processes.
European Journal for Philosophy of Science, 2012
ABSTRACT Complex systems research is becoming ever more important in both the natural and social ... more ABSTRACT Complex systems research is becoming ever more important in both the natural and social sciences. It is commonly implied that there is such a thing as a complex system across the disciplines. However, there is no concise definition of a complex system, let alone a definition that all disciplines agree on. We review various attempts to characterize a complex system, and consider a core set of features that are widely associated with complex systems by scientists in the field. We argue that some of these features are neither necessary nor sufficient for complexity, and that some of them are too vague or confused to be of any analytical use. In order to bring mathematical rigour to the issue we then review some standard measures of complexity from the scientific literature, and offer a taxonomy for them, before arguing that the one that best captures the qualitative notion of complexity is that of the statistical complexity. Finally, we offer our own list of necessary conditions as a characterization of complexity. These conditions are qualitative and may not be jointly sufficient for complexity. We close with some suggestions for future work.
Humanities and Social Science Communications, 2020
Political scientists have conventionally assumed that achieving democracy is a one-way ratchet. O... more Political scientists have conventionally assumed that achieving democracy is a one-way ratchet. Only very recently has the question of "democratic backsliding" attracted any research attention. We argue that democratic instability is best understood with tools from complexity science. The explanatory power of complexity science arises from several features of complex systems. Their relevance in the context of democracy is discussed. Several policy recommendations are offered to help (re)stabilize current systems of representative democracy.
Emergence, Complexity and Computation, 2015
Encyclopedia of Complexity and Systems Science, 2009
Computational Complexity, 2012
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2010
Nature intrinsically computes. It has been suggested that the entire universe is a computer, in p... more Nature intrinsically computes. It has been suggested that the entire universe is a computer, in particular, a quantum computer. To corroborate this idea we require tools to quantify the information processing. Here we review a theoretical framework for quantifying information processing in a quantum dynamical system. So-called intrinsic quantum computation combines tools from dynamical systems theory, information theory, quantum mechanics, and computation theory. We will review how far the framework has been developed and what some of the main open questions are. On the basis of this framework we discuss upper and lower bounds for intrinsic information storage in a quantum dynamical system.
… Summaries of Uppsala Dissertations from the …, 2003
Stochastic finite-state generators are compressed descriptions of infinite time series. Alternati... more Stochastic finite-state generators are compressed descriptions of infinite time series. Alternatively, compressed descriptions are given by quantum finitestate generators [K. Wiesner and J. P. Crutchfield, Physica D 237, 1173 (2008)].
07/20/2015-07/24/2015, 2015
We present a new method for inferring hidden Markov models from noisy time sequences without the ... more We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al., and generates so called causal state models, equivalent to hidden Markov models. This method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing v...
PLoS ONE, 2012
We present a new method for inferring hidden Markov models from noisy time sequences without the ... more We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al. and generates so called causal state models, equivalent in structure to hidden Markov models. The new method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing values for the transition rates in good agreement with previous results and with results obtained using existing maximum likelihood based methods. The method differs markedly from previous Markov-model reconstructions in being able to uncover truly hidden states.
Chemical Physics, 2003
We present multicoincidence spectra of nitrogen, formic acid and methyl methacrylate. We demonstr... more We present multicoincidence spectra of nitrogen, formic acid and methyl methacrylate. We demonstrate how to probe the local symmetry of molecular orbitals from molecules core excited with linearly polarized synchrotron radiation. The intensity distribution of the photoelectron photo-ion photo-ion coincidence (PEPIPICO) spectrum reflects the selectivity and localization of core excitation by polarized light. By simulating the spectra the angular dependence of the fragmentation is determined.
Stochastic finite-state generators are compressed descriptions of infinite time series. Alternati... more Stochastic finite-state generators are compressed descriptions of infinite time series. Alternatively, compressed descriptions are given by quantum finitestate generators [K. Wiesner and J. P. Crutchfield, Physica D 237, 1173 (2008)].
Eprint Arxiv Quant Ph 0608206, Aug 27, 2006
We introduce stochastic and quantum finite-state transducers as computation-theoretic models of c... more We introduce stochastic and quantum finite-state transducers as computation-theoretic models of classical stochastic and quantum finitary processes. Formal process languages, representing the distribution over a process's behaviors, are recognized and generated by suitable specializations. We characterize and compare deterministic and nondeterministic versions, summarizing their relative computational power in a hierarchy of finitary process languages. Quantum finite-state transducers and generators are a first step toward a computation-theoretic analysis of individual, repeatedly measured quantum dynamical systems. They are explored via several physical systems, including an iterated beam splitter, an atom in a magnetic field, and atoms in an ion trap--a special case of which implements the Deutsch quantum algorithm. We show that these systems' behaviors, and so their information processing capacity, depends sensitively on the measurement protocol.
Mathematical models are an essential component of quantitative science. They generate predictions... more Mathematical models are an essential component of quantitative science. They generate predictions about the future, based on information available in the present. In the spirit of simpler is better; should two models make identical predictions, the one that requires less input is preferred. Yet, for almost all stochastic processes, even the provably optimal classical models waste information. The amount of input information they demand exceeds the amount of predictive information they output. Here we show how to systematically construct quantum models that break this classical bound, and that the system of minimal entropy that simulates such processes must necessarily feature quantum dynamics. This indicates that many observed phenomena could be significantly simpler than classically possible should quantum effects be involved.
We introduce ways to measure information storage in quantum systems, using a recently introduced ... more We introduce ways to measure information storage in quantum systems, using a recently introduced computation-theoretic model that accounts for measurement effects. The first, the quantum excess entropy, quantifies the shared information between a quantum process's past and its future. The second, the quantum transient information, determines the difficulty with which an observer comes to know the internal state of a quantum process through measurements. We contrast these with von Neumann entropy and quantum entropy rate and provide a closed-form expression for the latter for the class of deterministic quantum processes.
European Journal for Philosophy of Science, 2012
ABSTRACT Complex systems research is becoming ever more important in both the natural and social ... more ABSTRACT Complex systems research is becoming ever more important in both the natural and social sciences. It is commonly implied that there is such a thing as a complex system across the disciplines. However, there is no concise definition of a complex system, let alone a definition that all disciplines agree on. We review various attempts to characterize a complex system, and consider a core set of features that are widely associated with complex systems by scientists in the field. We argue that some of these features are neither necessary nor sufficient for complexity, and that some of them are too vague or confused to be of any analytical use. In order to bring mathematical rigour to the issue we then review some standard measures of complexity from the scientific literature, and offer a taxonomy for them, before arguing that the one that best captures the qualitative notion of complexity is that of the statistical complexity. Finally, we offer our own list of necessary conditions as a characterization of complexity. These conditions are qualitative and may not be jointly sufficient for complexity. We close with some suggestions for future work.