A fuzzy cognitive map (FCM) is made up of factors and direct impacts. In graph theory, a bipolar weighted digraph is used to model an FCM; its vertices represent the factors, and the arcs represent the direct impacts. Each direct impact is either positive or negative, and is assigned a weight; in the model considered in this thesis, each weight is interpreted as the probability of the impact. A directed walk from factor F to factor F' is interpreted as an indirect impact of F on F'. The probabilistic transitive closure (PTC) of an FCM (or bipolar weighted digraph) is a bipolar weighted digraph with the same set of factors, but with arcs corresponding to the indirect impacts in the given FCM. Fuzzy cognitive maps can be used to represent structured knowledge in diverse fields, which include science, engineering, and the social sciences. In [P. Niesink, K. Poulin, M. Sajna, Computing transitive closure of bipolar weighted digraphs, Discrete Appl. Math. 161 (2013), 217-243], it was shown that the transitive closure provides valuable new information for its corresponding FCM. In particular, it gives the total impact of each factor on each other factor, which includes both direct and indirect impacts. Furthermore, several algorithms were developed to compute the transitive closure of an FCM. Unfortunately, computing the PTC of an FCM is computationally hard and the implemented algorithms are not successful for large FCMs. Hence, the Reduction-Recovery Algorithm was proposed to make other (direct) algorithms more efficient. However, this algorithm has never been implemented before. In this thesis, we code the Reduction-Recovery Algorithm and compare its running time with the existing software. Also, we propose a new enhancement on the existing PTC algorithms, which we call the Separation-Reduction Algorithm. In particular, we state and prove a new theorem that describes how to reduce the input digraph to smaller components by using a separating vertex. In the application part of the thesis, we show how the PTC of an FCM can be used to compare different standpoints on the issue of work-integrated learning.

Yes / The prevalence of big data is starting to spread across the public and private sectors however, an impediment to its widespread adoption orientates around a lack of appropriate big data analytics (BDA) and resulting skills to exploit the full potential of big data availability. In this paper, we propose a novel BDA to contribute towards this void, using a fuzzy cognitive map (FCM) approach that will enhance decision-making thus prioritising IT service procurement in the public sector. This is achieved through the development of decision models that capture the strengths of both data analytics and the established intuitive qualitative approach. By taking advantages of both data analytics and FCM, the proposed approach captures the strength of data-driven decision-making and intuitive model-driven decision modelling. This approach is then validated through a decision-making case regarding IT service procurement in public sector, which is the fundamental step of IT infrastructure supply for publics in a regional government in the Russia federation. The analysis result for the given decision-making problem is then evaluated by decision makers and e-government expertise to confirm the applicability of the proposed BDA. In doing so, demonstrating the value of this approach in contributing towards robust public decision-making regarding IT service procurement. / EU FP7 project Policy Compass (Project No. 612133)

Yes / This paper provides a management perspective of organisational factors that contributes to the reduction of food waste through the application of design science principles to explore causal relationships between food distribution (organisational) and consumption (societal) factors. Qualitative data were collected with an organisational perspective from commercial food consumers along with large-scale food importers, distributors, and retailers. Cause-effect models are built and “what-if” simulations are conducted through the development and application of a Fuzzy Cognitive Map (FCM) approaches to elucidate dynamic interrelationships. The simulation models developed provide a practical insight into existing and emergent food losses scenarios, suggesting the need for big data sets to allow for generalizable findings to be extrapolated from a more detailed quantitative exercise. This research offers itself as evidence to support policy makers in the development of policies that facilitate interventions to reduce food losses. It also contributes to the literature through sustaining, impacting and potentially improving levels of food security, underpinned by empirically constructed policy models that identify potential behavioural changes. It is the extension of these simulation models set against a backdrop of a proposed big data framework for food security, where this study sets avenues for future research for others to design and construct big data research in food supply chains. This research has therefore sought to provide policymakers with a means to evaluate new and existing policies, whilst also offering a practical basis through which food chains can be made more resilient through the consideration of management practices and policy decisions.

Agriculture 5.0, marked by advanced technology and intensified human-machine collaboration, addresses significant challenges in traditional farming, such as labor shortages, declining productivity, climate change impacts, and gender disparities. This study assesses the effectiveness of probabilistic machine learning methods, with a specific focus on Bayesian networks (BN), collaborative filtering (CF), and fuzzy cognitive map (FCM) techniques, in enhancing cybersecurity risk analysis and management in Agriculture 5.0. It also explores unique cybersecurity threats within Agriculture 5.0. Using a systematic literature review (SLR), and leveraging historical data, case studies, experimental datasets, probabilistic machine learning algorithms, experiments, expert insights, and data analysis tools, the study evaluates the effectiveness of these techniques in improving cybersecurity risk analysis in Agriculture 5.0. BN, CF, and FCM were found effective in enhancing the cybersecurity of Agriculture 5.0. This research enhances our understanding of how probabilistic machine learning can bolster cybersecurity within Agriculture 5.0. The study's insights will be valuable to industry stakeholders, policymakers, and cybersecurity professionals, aiding the protection of agriculture's digital transformation amid increasing technological complexity and cyber threats, and setting the stage for future investigations into Agriculture 5.0 security.