Fuzzy qualitative simulation with multivariate constraints

Postprin

An immune network approach to learning qualitative models of biological pathways

ACKNOWLEDGMENT GMC is supported by the CRISP project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative. WP and GMC are also supported by the partnership fund from dot.rural, RCUK Digital Economy research.Postprin

QML-Morven : A Novel Framework for Learning Qualitative Models

Publisher PD

Using qualitative models to define sustainable management for the commons in data poor conditions

Acknowledgments This work was funded by the University of Aberdeen and Scottish Natural Heritage (SNH) and their support is gratefully acknowledged. We thank MASTS (the Marine Alliance for Science and Technology for Scotland) for their role in funding this work and B. Leyshon and F. Manson (SNH) for fruitful discussion.Peer reviewedPostprin

Non-constructive interval simulation of dynamic systems

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An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems

The authors would like to thank the support on this research by the CRISP project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative.Peer reviewedPublisher PD

Qualitative System Identification from Imperfect Data

Experience in the physical sciences suggests that the only realistic means of understanding complex systems is through the use of mathematical models. Typically, this has come to mean the identification of quantitative models expressed as differential equations. Quantitative modelling works best when the structure of the model (i.e., the form of the equations) is known; and the primary concern is one of estimating the values of the parameters in the model. For complex biological systems, the model-structure is rarely known and the modeler has to deal with both model-identification and parameter-estimation. In this paper we are concerned with providing automated assistance to the first of these problems. Specifically, we examine the identification by machine of the structural relationships between experimentally observed variables. These relationship will be expressed in the form of qualitative abstractions of a quantitative model. Such qualitative models may not only provide clues to the precise quantitative model, but also assist in understanding the essence of that model. Our position in this paper is that background knowledge incorporating system modelling principles can be used to constrain effectively the set of good qualitative models. Utilising the model-identification framework provided by Inductive Logic Programming (ILP) we present empirical support for this position using a series of increasingly complex artificial datasets. The results are obtained with qualitative and quantitative data subject to varying amounts of noise and different degrees of sparsity. The results also point to the presence of a set of qualitative states, which we term kernel subsets, that may be necessary for a qualitative model-learner to learn correct models. We demonstrate scalability of the method to biological system modelling by identification of the glycolysis metabolic pathway from data

Using social media to quantify spatial and temporal dynamics of nature-based recreational activities

Data Availability: All the data and R scripts are available at https://github.com/FrancescaMancini/Flickr-API and https://github.com/FrancescaMancini/Flickr-Statistical-Analysis. Funding: This work was supported by the University of Aberdeen, Scottish Natural Heritage through a Dominic Counsell PhD studentship, and the Marine Alliance for Science and Technology for Scotland (MASTS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation.Peer reviewedPublisher PD

A critical analysis of Donald M. MacKay’s contribution to theology and science

In this dissertation I present a critical analysis of some of the philosophical and theological ideas of Donald M. MacKay. (1922–1987). MacKay was a Scottish scientist who made significant contributions across a range of scientific fields (information theory, artificial intelligence, and neuroscience), as well as to the discipline of theology and science. His main contributions that I explore are Complementarity and Logical Indeterminism, both of which highlight the importance of standpoint for what one says about a subject. Complementarity identifies two,or more, descriptions of a common referent as each being complete and exhaustive with respect to their own language, but saying nothing about the other. MacKay described two types of complementarity. The first is nonhierarchical, an example of which is the projection of all the points of a three dimensional object onto two, or more, two dimensional planes: the points on the planes are in a complementarity relation to each other. The other is hierarchical, examples of which are the physical and informational descriptions of a communicated message (the sign and the things signified) and also the relation between the non-hierarchical descriptions and the original object. He used this, amongst other things, to suggest a relation between scientific and theological perspectives; but also, as he moved into neuroscience, to argue for a nondualist approach to the mind-body problem(which he termed “duality without dualism”). It turns out that hierarchical complementarity, as MacKay presents it, has the same basic definition as supervenience. However, I argue that hierarchical complementarity can be revised to make it a more general relation that is better suited than supervenience to issues of religion and science, particularly free will and determinism. The other of MacKay’s main contributions was to propose a solution to the problem of free will and determinism. He called this Logical Indeterminism. Here MacKay supposes, for the sake of argument, that hard determinism holds. He posits a thought experiment in which one can use a instrument, which he calls a “cerebroscope”, to view every detail of a brain and it’s behaviour. It turns out that in such a world it would be possible for an external observer to identify a complete specification of the brain state, but for the owner of the brain this specification would not exist. From this he argues that a “superscientist” could, from their standpoint, use the observation of brain state (also making use of the complementarity of brain and mind) to predict what the observed agent would do in their immediate future. However, because that observed brain state does not exist for the cognitive agent whose brain is being observed, the predicted behaviour does not have a “claim to their assent” until after they have made up their minds what they are going to do. As such the future remains open for them, and so they are free in a very real sense. In my analysis of MacKay’s argument I find that in its current form it is guilty of the modal fallacy. However, by making it more explicitly modal this issue can be overcome such that it then achieves what MacKay claimed for it. MacKay also suggested that Logical Indeterminism entails that the Godhead must be multi-personal. This was with respect to the economic Trinity. I suggest that the modal version entails that the Godhead must be ontologically multi-personal in a manner that fits with classical Christian theism