Applications of Belief Functions in Business Decisions: A Review

This is the author's final draft. The publisher's official version is available from: .In this paper, we review recent applications of Dempster-Shafer theory (DST) of belief functions to auditing and business decision-making. We show how DST can better map uncertainties in the application domains than Bayesian theory of probabilities. We review the applications in auditing around three practical problems that challenge the effective application of DST, namely, hierarchical evidence, versatile evidence, and statistical evidence. We review the applications in other business decisions in two loose categories: judgment under ambiguity and business model combination. Finally, we show how the theory of linear belief functions, a new extension of DST, can provide an alternative solution to a wide range of business problems

Knowledge representation and integration for portfolio evaluation using linear belief functions

In this paper, we propose a linear belief function approach to evaluating portfolio performance. By drawing on the notion of linear belief functions, we propose an elementary approach to knowledge representation for expert systems using linear belief functions. We show how to use basic matrices to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, distributional assumptions, linear relations, and empirical asset pricing models. We then appeal to Dempster’s rule of combination to integrate the knowledge for assessing the overall belief of portfolio performance, and updating the belief by incorporating additional information. We use an example of three gold stocks to illustrate the approach

Enriching remote labs with computer vision and drones

165 p.With the technological advance, new learning technologies are being developed in order to contribute to better learning experience. In particular, remote labs constitute an interesting and a practical way that can motivate nowadays students to learn. The studen can at anytime, and from anywhere, access the remote lab and do his lab-work. Despite many advantages, remote tecnologies in education create a distance between the student and the teacher. Without the presence of a teacher, students can have difficulties, if no appropriate interventions can be taken to help them. In this thesis, we aim to enrich an existing remote electronic lab made for engineering students called "LaboREM" (for remote Laboratory) in two ways: first we enable the student to send high level commands to a mini-drone available in the remote lab facility. The objective is to examine the front panels of electronic measurement instruments, by the camera embedded on the drone. Furthermore, we allow remote student-teacher communication using the drone, in case there is a teacher present in the remote lab facility. Finally, the drone has to go back home when the mission is over to land on a platform for automatic recharge of the batteries. Second, we propose an automatic system that estimates the affective state of the student (frustrated/confused/flow) in order to take appropriate interventions to ensure good learning outcomes. For example, if the studen is having major difficulties we can try to give him hints or to reduce the difficulty level of the lab experiment. We propose to do this by using visual cues (head pose estimation and facil expression analysis). Many evidences on the state of the student can be acquired, however these evidences are incomplete, sometims inaccurate, and do not cover all the aspects of the state of the student alone. This is why we propose to fuse evidences using the theory of Dempster-Shafer that allows the fusion of incomplete evidence

Enriching remote labs with computer vision and drones

165 p.With the technological advance, new learning technologies are being developed in order to contribute to better learning experience. In particular, remote labs constitute an interesting and a practical way that can motivate nowadays students to learn. The studen can at anytime, and from anywhere, access the remote lab and do his lab-work. Despite many advantages, remote tecnologies in education create a distance between the student and the teacher. Without the presence of a teacher, students can have difficulties, if no appropriate interventions can be taken to help them. In this thesis, we aim to enrich an existing remote electronic lab made for engineering students called "LaboREM" (for remote Laboratory) in two ways: first we enable the student to send high level commands to a mini-drone available in the remote lab facility. The objective is to examine the front panels of electronic measurement instruments, by the camera embedded on the drone. Furthermore, we allow remote student-teacher communication using the drone, in case there is a teacher present in the remote lab facility. Finally, the drone has to go back home when the mission is over to land on a platform for automatic recharge of the batteries. Second, we propose an automatic system that estimates the affective state of the student (frustrated/confused/flow) in order to take appropriate interventions to ensure good learning outcomes. For example, if the studen is having major difficulties we can try to give him hints or to reduce the difficulty level of the lab experiment. We propose to do this by using visual cues (head pose estimation and facil expression analysis). Many evidences on the state of the student can be acquired, however these evidences are incomplete, sometims inaccurate, and do not cover all the aspects of the state of the student alone. This is why we propose to fuse evidences using the theory of Dempster-Shafer that allows the fusion of incomplete evidence

Document analysis at DFKI. - Part 1: Image analysis and text recognition

Document analysis is responsible for an essential progress in office automation. This paper is part of an overview about the combined research efforts in document analysis at the DFKI. Common to all document analysis projects is the global goal of providing a high level electronic representation of documents in terms of iconic, structural, textual, and semantic information. These symbolic document descriptions enable an "intelligent\u27; access to a document database. Currently there are three ongoing document analysis projects at DFKI: INCA, OMEGA, and PASCAL2000/PASCAL+. Though the projects pursue different goals in different application domains, they all share the same problems which have to be resolved with similar techniques. For that reason the activities in these projects are bundled to avoid redundant work. At DFKI we have divided the problem of document analysis into two main tasks, text recognition and text analysis, which themselves are divided into a set of subtasks. In a series of three research reports the work of the document analysis and office automation department at DFKI is presented. The first report discusses the problem of text recognition, the second that of text analysis. In a third report we describe our concept for a specialized document analysis knowledge representation language. The report in hand describes the activities dealing with the text recognition task. Text recognition covers the phase starting with capturing a document image up to identifying the written words. This comprises the following subtasks: preprocessing the pictorial information, segmenting into blocks, lines, words, and characters, classifying characters, and identifying the input words. For each subtask several competing solution algorithms, called specialists or knowledge sources, may exist. To efficiently control and organize these specialists an intelligent situation-based planning component is necessary, which is also described in this report. It should be mentioned that the planning component is also responsible to control the overall document analysis system instead of the text recognition phase onl

BORROWING FROM YOUR NEIGHBORS: THREE STATISTICAL TECHNIQUES FROM NONTRADITIONAL SOURCES

From Generalised Fiducial Inference to Causal Inference, the past few years have seen a rising tide of new statistical paradigms calling into question our previous approaches of learning from data. This thesis will follow in this movement and demonstrate how these newer paradigms allow us to perform analyses that would be difficult to perform using conventional approaches. In the first chapter, we show how Dempster-Shafer and Fidu- cial Inference can be used as an alternative approach to the conventional Neyman-Pearson hypothesis testing paradigm through the inclusion of an “unknown” class into the testing procedure. This not only allows for tests with in-built robustness estimates, but allows for a natural analysis of the effects of adversarial attacks on hypothesis tests. In the second chap- ter, we demonstrate how interpretable causal inference combine with differential equation modeling gives users a powerful new approach to answering causal questions about patients exhibiting epileptiform activity. Finally, we combine the Empirical Mode Decomposition, which pioneered a signal decomposition that makes far fewer assumptions than traditional Fourier or Wavelet decompositions, with statistical techniques to allow for more accurate signal identification and cleaning.Doctor of Philosoph

Contributions to modeling with set-valued data: benefitting from undecided respondents

This dissertation develops a methodological framework and approaches to benefit from undecided survey participants, particularly undecided voters in pre-election polls. As choices can be seen as processes that - in stages - exclude alternatives until arriving at one final element, we argue that in pre-election polls undecided participants can most suitably be represented by the set of their viable options. This consideration set sampling, in contrast to the conventional neglection of the undecided, could reduce nonresponse and collects new and valuable information. We embed the resulting set-valued data in the framework of random sets, which allows for two different interpretations, and develop modeling methods for either one. The first interpretation is called ontic and views the set of options as an entity of its own that most accurately represents the position at the time of the poll, thus as a precise representation of something naturally imprecise. With this, new ways of structural analysis emerge as individuals pondering between particular parties can now be examined. We show how the underlying categorical data structure can be preserved in this formalization process for specific models and how popular methods for categorical data analysis can be broadly transferred. As the set contains the eventual choice, under the second interpretation, the set is seen as a coarse version of an underlying truth, which is called the epistemic view. This imprecise information of something actually precise can then be used to improve predictions or election forecasting. We developed several approaches and a framework of a factorized likelihood to utilize the set-valued information for forecasting. Amongst others, we developed methods addressing the complex uncertainty induced by the undecided, weighting the justifiability of assumptions with the conciseness of the results. To evaluate and apply our approaches, we conducted a pre-election poll for the German federal election of 2021 in cooperation with the polling institute Civey, for the first time regarding undecided voters in a set-valued manner. This provides us with the unique opportunity to demonstrate the advantages of the new approaches based on a state-of-the-art survey