Heterogeneity in consumer preferences for organic and genetically modified food products in Ghana

Consumers are increasingly becoming very concerned about food safety, with many giving preference to organic food products over conventional food products, which make use of agrochemicals with potential implications for health. Furthermore, to make the food choice decisions even more complex, genetically modified (GM) foods have been introduced in an attempt to meet global food demand. Consumers therefore must make decisions regarding organic and GM foods. This paper investigates consumer heterogeneity for organic and GM tomatoes in Ghana using advanced discrete choice modelling techniques. The data for empirical application come from a choice experimental study conducted among 200 consumers in Ghana. Our econometric modelling revealed that the sampled consumers preferred organic tomatoes that are produced locally and certified by the Food and Drugs Authority. However, we find a likelihood that women and older consumers may have preferences for GM tomatoes with environmental and health benefits. Policy implications are drawn from the findings of the study.

Generative Modelling and Machine Learning Methods for Travel Behaviour Analysis

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.

Generative Modelling and Machine Learning Methods for Travel Behaviour Analysis

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.