Applying qualitative reasoning to finance

Qualitative reasoning has traditionally been applied in the domain of physical systems, where there are well established and understood laws governing the behaviour of each `component' in the system. Such application has shown that it is possible to produce models which can be used for explaining and predicting the behaviour of physical phenomena and also trouble-shooting. The principles underlying the theory ensure that the models are robust and exhibit consistent behaviour under all conditions. This research examines the validity of applying the theory in the financial domain where such laws may not exist or if they do, may not be universally applicable. In particular, it investigates how far these principles and techniques may be applied in the construction of financial analysis models. Because of the inherent differences in the nature of these two domains, it is argued that a different qualitative value system ought to be employed. The dissertation enlarges on the constraints this places on model descriptions and the effect it may have on the power and usefulness of the resulting models. It also describes the implementation of a system that investigates the implications of applying this theory by way of testing it on situations drawn from both text-books and published financial information.

The application of methods of uncertain reasoning to the biological classification of river water quality

This thesis presents an investigation into the application of methods of uncertain reasoning to the biological classification of river water quality. Existing biological methods for reporting river water quality are critically evaluated, and the adoption of a discrete biological classification scheme advocated. Reasoning methods for managing uncertainty are explained, in which the Bayesian and Dempster-Shafer calculi are cited as primary numerical schemes. Elicitation of qualitative knowledge on benthic invertebrates is described. The specificity of benthic response to changes in water quality leads to the adoption of a sensor model of data interpretation, in which a reference set of taxa provide probabilistic support for the biological classes. The significance of sensor states, including that of absence, is shown. Novel techniques of directly eliciting the required uncertainty measures are presented. Bayesian and Dempster-Shafer calculi were used to combine the evidence provided by the sensors. The performance of these automatic classifiers was compared with the expert's own discrete classification of sampled sites. Variations of sensor data weighting, combination order and belief representation were examined for their effect on classification performance. The behaviour of the calculi under evidential conflict and alternative combination rules was investigated. Small variations in evidential weight and the inclusion of evidence from sensors absent from a sample improved classification performance of Bayesian belief and support for singleton hypotheses. For simple support, inclusion of absent evidence decreased classification rate. The performance of Dempster-Shafer classification using consonant belief functions was comparable to Bayesian and singleton belief. Recommendations are made for further work in biological classification using uncertain reasoning methods, including the combination of multiple-expert opinion, the use of Bayesian networks, and the integration of classification software within a decision support system for water quality assessment.

A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

A semantic web environment for component model

Component-based development (CBD) has become an important emerging topic in the software engineering field. It promises long-sought-after benefits such as increased software reuse, reduced development time to market and, hence, reduced software production cost. Despite the huge potential, the lack of reasoning support and development environment of component modeling and verification may hinder its development. Methods and tools that can support component model analysis are highly appreciated by industry. Such a tool support should be fully automated as well as efficient. At the same time, the reasoning tool should scale up well as it may need to handle hundreds or even thousands of components that a modern software system may have. Furthermore, a distributed environment that can effectively manage and compose components is also desirable. In this paper, we present an approach to the modeling and verification of a newly proposed component model using Semantic Web languages and their reasoning tools. We use the Web Ontology Language and the Semantic Web Rule Language to precisely capture the inter-relationships and constraints among the entities in a component model. Semantic Web reasoning tools are deployed to perform automated analysis support of the component models. Moreover, we also proposed a service-oriented architecture (SOA)-based semantic web environment for CBD. The adoption of Semantic Web services and SOA make our component environment more reusable, scalable, dynamic and adaptive.

A gloss composition and context clustering based distributed word sense representation model

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

Resting state morphology predicts the effect of theta burst stimulation in false belief reasoning:ventrolateral prefrontal cortex in false belief reasoning

When required to represent a perspective that conflicts with one's own, functional magnetic resonance imaging (fMRI) suggests that the right ventrolateral prefrontal cortex (rvlPFC) supports the inhibition of that conflicting self-perspective. The present task dissociated inhibition of self-perspective from other executive control processes by contrasting belief reasoning-a cognitive state where the presence of conflicting perspectives was manipulated-with a conative desire state wherein no systematic conflict existed. Linear modeling was used to examine the effect of continuous theta burst stimulation (cTBS) to rvlPFC on participants' reaction times in belief and desire reasoning. It was anticipated that cTBS applied to rvlPFC would affect belief but not desire reasoning, by modulating activity in the Ventral Attention System (VAS). We further anticipated that this effect would be mediated by functional connectivity within this network, which was identified using resting state fMRI and an unbiased model-free approach. Simple reaction-time analysis failed to detect an effect of cTBS. However, by additionally modeling individual measures from within the stimulated network, the hypothesized effect of cTBS to belief (but, importantly, not desire) reasoning was demonstrated. Structural morphology within the stimulated region, rvlPFC, and right temporoparietal junction were demonstrated to underlie this effect. These data provide evidence that inconsistencies found with cTBS can be mediated by the composition of the functional network that is being stimulated. We suggest that the common claim that this network constitutes the VAS explains the effect of cTBS to this network on false belief reasoning. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.