Exploring Different Assumptions about Outcome-Related Risk Perceptions in Discrete Choice Experiments

Open access via Springer compact agreement Acknowledgements The authors would like to thank Jianbo Hu from Guizhou University of Finance and Economics who helped facilitate the data collection for this paper. The authors would also like to thank Nathalie Picard, Andre’ de Palma, Thomas Gall, Michael Vlassopoulos and participants of the 2019 international conference of choice modeling and annual Economics workshop at the University of Southampton for their valuable feedback and suggestions on earlier drafts of the paper. All errors are our own.Peer reviewedPublisher PD

Behavioural Finance: Beginnings and Applications

The essay traces the beginning of behavioural finance by examining the development of expected utility model. Expected utility model is based on the assumptions of time consistent preferences of utility. However, experimental results in psychology regarding choice under risk and uncertainty shows well-defined deviations from the predictions of expected utility model. It was found that there were systematic biases and heuristics that economic decision makers use to make choices. In the next section, the essay describes some of these heuristics and how they modify the assumptions of utility model. Applications of behavioural understanding in finance is briefly discussed to show the widespread prevalence of behavioural heuristics in and beyond finance. The essay concludes by arguing that accommodating the behavioural variable is necessary to make neoclassical model more relevant to the real world

AutoML for Advanced Monitoring in Digital Manufacturing and Industry 4.0

The emergence of Industry 4.0 and the associated rise of sensing technologies in industrial equipment has made the adoption of Machine Learning (ML) solutions crucial in enhancing and making more efficient enterprise production processes. However, the high demand for ML models often clashes with the small number of professionals capable of handling such projects. For this reason, Automatic Machine Learning (AutoML) tools are considered high-value solutions, thanks to their capability to provide a suitable model for the provided data without the need for the intervention by a ML expert. Indeed, AutoML libraries are designed to be used in an easy way also for people with limited or no experience with ML. In this work, three important tasks involved in manufacturing, that are Anomaly Detection, Visual Anomaly Detection, and Remaining Useful Life Estimation, are considered. After analysing the most critical aspects of each task and the state of the art, possible solutions are proposed for the development of specialised AutoML modules. Additionally, given the increasing emphasis on the interpretability of MLmodels, part of the analysis performed aims at identifying explainability tools, which are particularly important for an AutoML library. In fact, they provide useful motivations for the model predictions, increasing also the user confidence in AutoML tools.The emergence of Industry 4.0 and the associated rise of sensing technologies in industrial equipment has made the adoption of Machine Learning (ML) solutions crucial in enhancing and making more efficient enterprise production processes. However, the high demand for ML models often clashes with the small number of professionals capable of handling such projects. For this reason, Automatic Machine Learning (AutoML) tools are considered high-value solutions, thanks to their capability to provide a suitable model for the provided data without the need for the intervention by a ML expert. Indeed, AutoML libraries are designed to be used in an easy way also for people with limited or no experience with ML. In this work, three important tasks involved in manufacturing, that are Anomaly Detection, Visual Anomaly Detection, and Remaining Useful Life Estimation, are considered. After analysing the most critical aspects of each task and the state of the art, possible solutions are proposed for the development of specialised AutoML modules. Additionally, given the increasing emphasis on the interpretability of MLmodels, part of the analysis performed aims at identifying explainability tools, which are particularly important for an AutoML library. In fact, they provide useful motivations for the model predictions, increasing also the user confidence in AutoML tools

The interaction between task goals and the representation of choice options in decision-making

Most decision-making studies will focus on the value and uncertainty of each choice option but do not focus on the importance of the representation of the choice option itself. This thesis presents the effects that task goals have on creating the appropriate cognitive representation to achieve those goals and how these representations are dependent on the informational input within a given task. The overall hypothesis for this work is that cognitive representations reveal a trade-off between accommodating task goals and the format of the information sampled from the environment of the task. For example, ordering books in a stand in alphabetical order, to facilitate the task of retrieving a relevant one when necessary, reveals a material implication of such cognitive representations. As in many situations, the internal representations constructed by the agent embody the remaining degrees of freedom that map the input to successful task completion. The first two chapters in this work present how uncertain beliefs about ourselves and our preferences are either integrated or compared to fixed information about other agent’s beliefs. The third chapter presents the direct manipulation of representations of choice options by changing both the stimuli and controlling for the decision strategy used by the decision-makers. The fourth chapter presents how choice options themselves are represented in the human brain. The findings related to 1) the adaptation of personal preferences and beliefs to the (fixed) preferences and beliefs of other agents, 2) observed reduction in decision strategy compliance contingent on stimulus format, and 3) the task-contingent results for similarities between brain states of choice options, support the general trade-off hypothesis. The conclusion that can be drawn is that the study of choice option representations is underdetermined unless both informational input and task goals are accounted for