Choice modelling in the age of machine learning - Discussion paper

The dissertation outlines novel analytical and experimental methods for discrete choice modelling using generative modelling and information theory. It explores the influence of information heterogeneity on large scale datasets using generative modelling. The behaviorally subjective psychometric indicators are replaced with a learning process in an artificial neural network architecture. Part of the dissertation establishes new tools and techniques to model aspects of travel demand and behavioural analysis for the emerging transport and mobility markets. Specically, we consider: (i) What are the strengths, weaknesses and role of generative learning algorithms for behaviour analysis in travel demand modelling? (ii) How to monitor and analyze the identiability and validity of the generative model using Bayesian inference methods? (iii) How to ensure that the methodology is behaviourally consistent? (iv) What is the relationship between the generative learning process and realistic representation of decision making as well as its usefulness in choice modelling? and (v) What are the limitations and assumptions that have needed to develop the generative model systems? This thesis is based on four articles introduced in Chapters 3 to 6. Chapters 3 and 4 introduces a restricted Boltzmann machine learning algorithm for travel behaviour that includes an analysis of modelling discrete choice with and without psychometric indicators. Chapter 5 provides an analysis of information heterogeneity from the perspective of a generative model and how it can extract population taste variation using a Bayesian inference based learning process. One of the most promising applications for generative modelling is for modelling the multiple discretecontinuous data. In Chapter 6, a generative modelling framework is developed to show the process and methodology of capturing higher-order correlation in the data and deriving a process of sampling that can account for the interdependencies between multiple outputs and inputs. A brief background on machine learning principles for discrete choice modelling and newly developed mathematical models and equations related to generative modelling for travel behaviour analysis are provided in the appendices.

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Generative Modelling and Machine Learning Methods for Travel Behaviour Analysis

10.32920/ryerson.14664273.v1 ◽

2021 ◽

Author(s):

Melvin Wong

Keyword(s):

Machine Learning ◽

Discrete Choice ◽

Learning Process ◽

Travel Demand ◽

Travel Behaviour ◽

Generative Model ◽

Choice Modelling ◽

Behaviour Analysis ◽

Discrete Choice Modelling ◽

Generative Modelling

The dissertation outlines novel analytical and experimental methods for discrete choice modelling using generative modelling and information theory. It explores the influence of information heterogeneity on large scale datasets using generative modelling. The behaviorally subjective psychometric indicators are replaced with a learning process in an artificial neural network architecture. Part of the dissertation establishes new tools and techniques to model aspects of travel demand and behavioural analysis for the emerging transport and mobility markets. Specically, we consider: (i) What are the strengths, weaknesses and role of generative learning algorithms for behaviour analysis in travel demand modelling? (ii) How to monitor and analyze the identiability and validity of the generative model using Bayesian inference methods? (iii) How to ensure that the methodology is behaviourally consistent? (iv) What is the relationship between the generative learning process and realistic representation of decision making as well as its usefulness in choice modelling? and (v) What are the limitations and assumptions that have needed to develop the generative model systems? This thesis is based on four articles introduced in Chapters 3 to 6. Chapters 3 and 4 introduces a restricted Boltzmann machine learning algorithm for travel behaviour that includes an analysis of modelling discrete choice with and without psychometric indicators. Chapter 5 provides an analysis of information heterogeneity from the perspective of a generative model and how it can extract population taste variation using a Bayesian inference based learning process. One of the most promising applications for generative modelling is for modelling the multiple discretecontinuous data. In Chapter 6, a generative modelling framework is developed to show the process and methodology of capturing higher-order correlation in the data and deriving a process of sampling that can account for the interdependencies between multiple outputs and inputs. A brief background on machine learning principles for discrete choice modelling and newly developed mathematical models and equations related to generative modelling for travel behaviour analysis are provided in the appendices.

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Discussion Paper: Social accountability for students in a machine learning era

Focus on Health Professional Education A Multi-Professional Journal ◽

10.11157/fohpe.v21i1.363 ◽

2020 ◽

Vol 21 (1) ◽

pp. 114

Author(s):

Logan Zane John Williams ◽

Rebecca Grainger

Keyword(s):

Machine Learning ◽

Social Accountability ◽

Discussion Paper

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Mapping platforms into a new open science model for machine learning

it - Information Technology ◽

10.1515/itit-2018-0022 ◽

2019 ◽

Vol 61 (4) ◽

pp. 197-208

Author(s):

Thomas Weißgerber ◽

Michael Granitzer

Keyword(s):

Machine Learning ◽

Process Model ◽

Data Science ◽

Common Ground ◽

Research Process ◽

Open Science ◽

Major Research ◽

Discussion Paper ◽

The Core ◽

Research Areas

Abstract Data-centric disciplines like machine learning and data science have become major research areas within computer science and beyond. However, the development of research processes and tools did not keep pace with the rapid advancement of the disciplines, resulting in several insufficiently tackled challenges to attain reproducibility, replicability, and comparability of achieved results. In this discussion paper, we review existing tools, platforms and standardization efforts for addressing these challenges. As a common ground for our analysis, we develop an open science centred process model for machine learning research, which combines openness and transparency with the core processes of machine learning and data science. Based on the features of over 40 tools, platforms and standards, we list the, in our opinion, 11 most central platforms for the research process in this paper. We conclude that most platforms cover only parts of the requirements for overcoming the identified challenges.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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Pharmacology Issues Related to Dementia Syndrome of Depression in Elderly Residents of Long-Term Care Settings

Perspectives on Neurophysiology and Neurogenic Speech and Language Disorders ◽

10.1044/nnsld25.2.61 ◽

2015 ◽

Vol 25 (2) ◽

pp. 61-69

Author(s):

Angel L. Ball ◽

Adina S. Gray

Keyword(s):

Depressive Symptoms ◽

The Elderly ◽

Environmental Communication ◽

Pharmacological Intervention ◽

Team Approach ◽

Discussion Paper ◽

Term Care ◽

Anticholinergic Medications ◽

Inappropriate Treatment ◽

Dementia Syndrome

Pharmacological intervention for depressive symptoms in institutionalized elderly is higher than the population average. Among the patients on such medications are those with a puzzling mix of symptoms, diagnosed as “dementia syndrome of depression,” formerly termed “pseudodementia”. Cognitive-communicative changes, potentially due to medications, complicate the diagnosis even further. This discussion paper reviews the history of the terminology of “pseudodementia,” and examines the pharmacology given as treatment for depressive symptoms in the elderly population that can affect cognition and communication. Clinicians can reduce the risk of misdiagnosis or inappropriate treatment by having an awareness of potential side effects, including decreased attention, memory, and reasoning capacities, particularly due to some anticholinergic medications. A team approach to care should include a cohesive effort directed at caution against over-medication, informed management of polypharmacology, enhancement of environmental/communication supports and quality of life, and recognizing the typical nature of some depressive signs in elderly institutionalized individuals.

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