Building models, whether to explain or to predict observed data, is an exercise of describing how the values of observed variables depend on those of others. Black box models only describe relationships between observed variables, and they are evaluated by their ability to accurately describe the values of observed variables in new situations not previously available to the model–such as the output response to a new set of inputs, for example. Black box models describe the observed behavior of the underlying system, but they may not correctly describe the way in which the system computes this behavior. White box models, on the other hand, describe the observed behavior and also incorporate hidden, intermediate variables that are used to describe the specific computation the underlying system uses to generate its observed behavior. In this sense, we say the white box model captures the structure of the system, in addition to its observed behavior. Since a given white box model may be accurately described by an infinite variety of black box models, all computing the same observed behavior but using different structures to do so, we say that any of these black box models is an abstraction of the white box model. This thesis constructs foundational pieces of a unifying theory of linear mathematical abstractions that are central to scientific modeling. It offers a precise description of the spectrums of grey box models linking any white and black box representation. There are various motivations for having this rich variety of representations of a given system. One key motivation is that of consilience, that is, to deepen our understanding of the modeling process by connecting various well developed theories under the umbrella of a broader theory. This work offers a precise relationship between Mason’s signal flow graphs [34] and Willem’s behavioral systems theory [66], in addition to linking the classical transfer function theory used by Nyquist [44], Bode [3], and Weiner [65] to the state space theory preferred by Kalman [27]. Another motivation comes from the application of identification or learning a model of the system from data. Learning problems trade off the number of a priori assumptions that one must make about a system, as well as the richness of available data, with the complexity of a model that one is able to confidently learn from measured observations. This work offers insight into these tradeoffs by characterizing them precisely over entire spectrums of grey box models of increasing complexity. A third motivation comes from the application of vulnerability analysis, which is the study of sensitivities of system behavior to structural perturbations in a grey-box model describing the attack surface, or representation of the system as visible to a potential attacker. The main results of this work, and its specific contributions, are as follows: 1. We define new graph-theoretic constructs and use them to create a unified framework for structural abstractions, 2. We demonstrate that there will always exist a complete, structure preserving, acyclic abstraction for every single-input, fully-connected system, 3. We define structural controllability of an abstraction of a system and argue why our definition is good, and 4. We show how complete abstractions preserve structural controllability. These results were accepted for publication in two papers at the 2020 International Federation of Automatic Control World Congress, each submitted to a different special invited session. These papers comprise Chapters 2 and 3, respectively, of the thesis presented here, and they are expected to appear in print July 2020.

abstract: Fractal analyses examine variability in a time series to look for temporal structure or pattern that reveals the underlying processes of a complex system. Although fractal property has been found in many signals in biological systems, how it relates to behavioral performance and what it implies about the complex system under scrutiny are still open questions. In this series of experiments, fractal property, movement kinematics, and behavioral performance were measured on participants performing a reciprocal tapping task. In Experiment 1, the results indicated that the alpha value from detrended fluctuation analysis (DFA) reflected deteriorating performance when visual feedback delay was introduced into the reciprocal tapping task. This finding suggests that this fractal index is sensitive to performance level in a movement task. In Experiment 2, the sensitivity of DFA alpha to the coupling strength between sub-processes within a system was examined by manipulation of task space visibility. The results showed that DFA alpha was not influenced by disruption of subsystems coupling strength. In Experiment 3, the sensitivity of DFA alpha to the level of adaptivity in a system under constraints was examined. Manipulation of the level of adaptivity was not successful, leading to inconclusive results to this question. / Dissertation/Thesis / Masters Thesis Psychology 2019

<div>The past few years have seen an increasing focus on fairness and the long-term impact of algorithmic decision making in the context of Machine learning, Artificial Intelligence and other disciplines. In this thesis, we model hiring processes in enterprises and organizations using dynamic mechanism design. Using a stochastic block model to simulate the workings of a hiring process, we study fairness and long-term evolution in the system. </div><div> </div><div> We first present multiple results on a deterministic variant of our model including convergence and an accurate approximate solution describing the state of the deterministic variant after any time period has elapsed. Using the differential equation method, it can be shown that this deterministic variant is in turn an accurate approximation of the evolution of our stochastic block model with high probability.</div><div> </div><div> Finally, we derive upper and lower bounds on the expected state at each time step, and further show that in the limiting case of the long-term, these upper and lower bounds themselves converge to the state evolution of the deterministic system. These results offer conclusions on the long-term behavior of our model, thereby allowing reasoning on how fairness in organizations could be achieved. We conclude that without sufficient, systematic incentives, under-represented groups will wane out from organizations over time.</div>

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The proposed aim of this thesis, inspired by the human brain, is to improve on the performance of a perceptual processing algorithm, referred to as a “perceptor”. This is done by trying to bridge the gap between neuroscience and engineering. To this end, we build on localized perception-action cycle in cognitive neuroscience by categorizing it under the umbrella of perceptual attention, which lends itself to increase gradually the contrast between relevant information and irrelevant information. Stated in another way, irrelevant information is filtered away while relevant information about the environment is enhanced from one cycle to the next. Accordingly, we propose to improve on the performance of a perceptor by modifying it to operate under the influence of perceptual attention. For this purpose, we first start with a single-layered perceptor and investigate the impact of perceptual attention on its performance through two computer experiments: The first experiment uses simulated (real-valued) data that are generated to purposely make the problem challenging. The second experiment uses real-life radar data that are complex-valued, hence the proposal to introduce Wirtinger calculus into derivation of our proposed method. We then take one step further and extend our proposed method to the case where a perceptor is hierarchical. In this context, every constitutive component of a hierarchical perceptor is modified to operate under the influence of perceptual attention. Then, another experiment is carried out to demonstrate the positive impact of perceptual attention on the performance of that hierarchical perceptor, just described. / Dissertation / Doctor of Philosophy (PhD)

Theories predicting performance of human control of complex dynamic systems must assess how decision makers capture and utilise knowledge for achieving and maintaining control. Traditional problem solving theories and corresponding measures such as Ravens matrices have been applied to predict performance in complex dynamic systems. While they assume stable properties of decision makers to predict control performance in decision-making tasks these tests have shown to provide only a limited degree of prediction in human control of complex dynamic systems. This paper reviews theoretical developments from recent empirical studies and tests the theoretical predictions of a model of dynamic decision-making using a complex dynamic microworld – Moro. The requirements for control of the microworld is analysed in study one. Theoretical predictions from the reviewed theory and results from study one are tested in study two. In study three additional hypotheses are derived by including meta cognitive dynamics to explain anomalies found in study two. A total of 21 Hypotheses are tested. Results indicate that for predicting human control of complex dynamic opaque systems a number of meta cognitive processes play an important role in determining outcome. Specifically, results show that we cannot expect a lower risk of failure in complex dynamic opaque systems from people with high problem solving capabilities when these also express higher goals. Further research should seek to explore the relative contribution of task characteristics to determine conditions under which these meta cognitive processes of decision makers take a dominant role over problem-solving capabilities – enabling improved decision-maker selection and support. / <p>Rapportkod: LiU-Tek-Lic-2002:4. I den tryckta versionen är bilagorna bifogade som CD-skiva. Den elektroniska versionen innehåller den kompletta avhandlingen.</p>

An agent based model (ABM) is a computational model with ``agents'' that interact with each other in an ``environment.'' This paper analyzes a particular ABM simulating individual ions in cardiac tissue, with the goal of modelling the strength and consistency of the electrical signals needed for a healthy heartbeat. We build several frameworks based on work by M. A. Yereniuk and S. D. Olson to demonstrate robustness of the original model. We conclude a moderate level of robustness using those frameworks, through a combination of proofs and empirical evidence.

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No description available.