Tshilidzi Marwala

University of Johannesburg | uj

Load-shedding is vital for managing electrical power shortages and avoiding grid collapse. However, excessive electricity demand poses an imminent threat to the overall stability of power grid system (PGS) and its ability to run safely and reliably. Load-shedding strategies can be complicated and inadequate to manage electrical power system efficie...

This chapter discusses the concept of enterprise risk management. Enterprise risk management is a holistic approach towards managing organisational risks. It differs from the old way of managing risks in silos, which has limitations regarding integrating risks and optimal resource allocation to manage and mitigate risks. The move towards enterprise...

It is proposed that the risk management field relies on information. For instance, to be in a position to identify, assess, and treat the risk, as well as monitor and report on the risk, one would need to have information about that particular risk. In this treatise, we propose three types of complexity. These include those caused by the architectu...

Enterprise risk management in the fourth industrial revolution is a hybrid approach where information is collected in a hybrid manner rather than either the bottom-up or top-down approaches. We argue that, however, whilst the hybrid approach addresses the information leaks, it will be prone to complexities, and could lead to delays. However, it sho...

The fourth industrial revolution is essentially a series of significant shifts in how economic, political, and social value is created, exchanged, and distributed. The advantage of the fourth industrial revolution is the integration of technologies such as big data analytical tools, cloud computing, and other emerging technologies into global manuf...

This chapter maps out the potential changes in enterprise risk management in the fourth industrial revolution to capture the broader information, and to analyse it to gain hindsight, insights, and foresights. Furthermore, this chapter explores the combination of skills needed in the enterprise risk management function. Given that nothing is guarant...

More than 250 emerging technologies have been identified by researchers in the fourth industrial revolution space. However, literature points out that the main fourth industrial revolution technologies revolve around 3D printing, Internet of things (IoT), Artificial intelligence (AI), Big data analytics tools, Cloud computing, Machine learning, Rob...

This book examined enterprise risk management in the fourth industrial revolution. It did this by providing a broader introduction of the fourth industrial revolution and the enterprise risk management in Chapter 1. A detailed account of what the fourth industrial revolution is and what it is not was carried out in Chapter 2. Documenting the fourth...

This chapter provides a high-level introduction to enterprise risk management in the fourth industrial revolution. It outlines the nine chapters that are covered in this book. Following the introduction in Chapter 1, the book details the fourth industrial revolution in Chapter 2. Documenting the fourth industrial revolution, Chapter 2 lays the grou...

At the onset of an infectious disease, such as the monkeypox virus (MPXV), surveillance data is crucial in keeping track of the outbreak’s progression. The surveillance data for MPXV received considerable attention after multiple European countries recorded cases. Historical data obtained from May 9, 2022, to August 10, 2022, were used to model the...

Streamflow predictions are vital for detecting flood and drought events. Such predictions are even more critical to Sub-Saharan African regions that are vulnerable to the increasing frequency and intensity of such events. These regions are sparsely gaged, with few available gaging stations that are often plagued with missing data due to various cau...

Named Entity Recognition (NER) plays a vital role in various Natural Language Processing tasks such as information retrieval, text classification, and question answering. However, NER can be challenging, especially in low-resource languages with limited annotated datasets and tools. This paper adds to the effort of addressing these challenges by in...

In this paper, a probabilistic-based evolution Markov chain algorithm is used for updating finite element models. The Bayesian approaches are well-known algorithms used for quantifying uncertainties associated with structural systems and several other engineering domains. In this approach, the unknown parameters and their associated uncertainties a...

A novel approach is presented for predicting the mean-mid stock price by utilizing high-frequency and complex limit order book (LOB) data as inputs for machine learning algorithms. Specifically, the proposed approach uses rough path theory to extract signature path features from the LOB data and compresses them for training machine learning models....

Good financial management provides economic stability and sustainability to an organization. It enables an organisation to make good use of its resources and plan effectively. South Africa’s public financial management has deteriorated over time, with only 16% of municipalities receiving a clean audit in the 2020-21 financial period as reported by...

This paper introduces the Ulimisana Optimisation Algorithm enabled Population Based Training (PBT-UOA) framework which allows for hyperparameters to be fine-tuned using a population based meta-heuristic algorithm at the same time as parameters are being optimised. Models are trained until near-convergence on the updated hyperparameters and the para...

A key drawback of the popular k-means clustering algorithm is its susceptibility to local minima. This problem is often addressed by performing repeated runs of the algorithm, and choosing the best run afterward. The approach is effective but computationally expensive: it multiplies the running time proportional to the number of repeats. We observe...

We address two key challenges of k-means clustering. In the first part of the paper, we show that: when a dataset is partitioned with an appropriate number of clusters (k), not more than 1/9 of D will exceed twice its standard deviation (2 s.d.), and not more than 4/9 of D will exceed its standard deviation (1 s.d.) (σ), where D is a vector compris...

Incorporating partial momentum refreshment into Magnetic Hamiltonian Monte Carlo (MHMC) to create Magnetic Hamiltonian Monte Carlo with partial momentum refreshment (PMHMC) has been shown to improve the sampling performance of MHMC significantly. At the same time, sampling from an integrator-dependent shadow or modified target density has been util...

Sell-side analysts’ recommendations are primarily targeted at institutional investors mandated to invest across many companies within client-mandated equity benchmarks, such as the FTSE/JSE All-Share index. Given the numerous sell-side recommendations for a single stock, making unbiased investment decisions is not often straightforward for portfoli...

The scandals in publicly listed companies have highlighted the large losses that can result from financial statement fraud and weak corporate governance. Machine learning techniques have been applied to automatically detect financial statement fraud with great success. This work presents the first application of a Bayesian inference approach to the...

Markov chain Monte Carlo (MCMC) techniques are usually used to infer model parameters when closed-form inference is not feasible, with one of the simplest MCMC methods being the random walk Metropolis–Hastings (MH) algorithm. The MH algorithm suffers from random walk behaviour, which results in inefficient exploration of the target posterior distri...

Magnetic Hamiltonian Monte Carlo (MHMC) is a Markov Chain Monte Carlo method that expands on Hamiltonian Monte Carlo (HMC) by adding a magnetic field to Hamiltonian dynamics. This magnetic field offers a great deal of flexibility over HMC and encourages more efficient exploration of the target posterior. This results in faster convergence and lower...

Sampling using integrator-dependent shadow Hamiltonian’s has been shown to produce improved sampling properties relative to Hamiltonian Monte Carlo. The shadow Hamiltonian’s are typically non-separable, requiring the expensive generation of momenta, with the recent trend being to utilise partial momentum refreshment. Separable Shadow Hamiltonian Hy...

Hamiltonian Monte Carlo (HMC) is a Markov Chain Monte Carlo algorithm that is able to generate distant proposals via the use of Hamiltonian dynamics, which are able to incorporate first-order gradient information about the target posterior. This has driven its rise in popularity in the machine learning community in recent times. It has been shown t...

A new data structure called the Nano Version Control (NanoVC) repo is shown to emerge from computer science and the software industry. This data structure is used to effectively encode entities at the nano-scale of the modelling spectrum and it gives us a natural place to encode the provenance data-lineage for that entity. The nature of the repo pr...

A reflection of the Corona pandemic highlights the need for more sustainable production systems using automation. The goal is to retain automation of repetitive tasks while allowing complex parts to come together. We recognize the fragility and how hard it is to create traditional automation. We introduce a method which converts one really hard pro...

Intracranial hypertension is an acute, life-threatening neurological condition that can lead to high risk of mortality. Its prompt identification and timely management are key to functional recovery and resuscitation of the patient. The objective of the present study is to propose quantitative measures for the early assessment of intracranial hyper...

Markov Chain Monte Carlo inference of target posterior distributions in machine learning is predominately conducted via Hamiltonian Monte Carlo and its variants. This is due to Hamiltonian Monte Carlo based samplers ability to suppress random-walk behaviour. As with other Markov Chain Monte Carlo methods, Hamiltonian Monte Carlo produces auto-corre...

Accurate wind speed and consequently wind power forecasts form a critical enabling tool for large scale wind energy adoption.Probabilistic machine learning models such as Bayesian Neural Network (BNN) models are often preferred in the forecasting task as they facilitate estimates of predictive uncertainty and automatic relevance determination (ARD)...

Historically, AI came into the spotlight driven by allies on the two sides of the Atlantic Ocean, the United Kingdom and the United States. In 1950, Turing put together and published Computing Machinery and Intelligence, which became a seminal work. In the United States, the concept of AI was inspired by science fiction known as Runaround created b...

This chapter discusses strategy implementation. From the onset, it highlights some key definitions of strategy implementation. This discussion is followed by a brief distinction between strategy implementation and strategy execution. Some activities in strategy implementation are briefly outlined, and some factors that firms need to consider as the...

This book examined how various forms of AI could shape the firm’s strategy. The book confines itself to AI forms such as machine learning (ML), natural language processing (NLP) and robotic process automation (RPA). Following the introductory chapter, the book discussed a high-level overview of artificial intelligence in Chap. 2. It discussed the s...

This chapter discusses the concept of robotics, which is followed by a discussion of the robotic process automation (RPA). The benefits of the RPA are also outlined in this chapter. The chapter further explores the RPA in strategy and strategy implementation. Robotics and RPA concepts are often confused. The confusion that occurs may be caused by t...

This chapter provides an overview of the concept of ML. This is followed by a discussion of the growing influence of ML and of the different forms of ML. A brief overview of deep learning is introduced, while the final part of the chapter explores ML in strategy and strategy implementation. It is apparent in the discussion that ML is a concept that...

This book consists of eight chapters dedicated at examining how various forms of artificial intelligence (AI) could shape a firm’s strategy. To assess how AI is likely going to shape a firm’s strategy, the book confines itself to AI forms such as machine learning (ML), natural language processing (NLP) and robotic process automation (RPA). After th...

This chapter discusses the concept of natural language processing (NLP), specifically how the NLP is applied. The chapter further outlines the typical functions of the NLP. In addition, it explores the NLP in strategy and strategy implementation.

This chapter provides a high-level overview of strategy by discussing the historical account of strategy, followed by a discussion aimed at understanding the concept of strategy. It further outlines and discusses processes that firms ought to engage in as they formulate strategy. From the discussions, it is apparent that strategy has long existed w...

We use gradient boosting machines and logistic regression to predict academic throughput at a South African university. The results highlight the significant influence of socio-economic factors and field of study as predictors of throughput. We further find that socio-economic factors become less of a predictor relative to the field of study as the...

Hamiltonian Monte Carlo is a Markov Chain Monte Carlo method that has been widely applied to numerous posterior inference problems within the machine learning literature. Markov Chain Monte Carlo estimators have higher variance than classical Monte Carlo estimators due to autocorrelations present between the generated samples. In this work we prese...

Gaussian processes (GPs) are nonparametric Bayesian models that have been applied to regression and classification problems. One of the approaches to alleviate their cubic training cost is the use of local GP experts trained on subsets of the data. In particular, product-of-expert models combine the predictive distributions of local experts through...

Bayesian methods have become very popular in the area of finite element model updating (FEMU) in the last decade, where these methods are considered as powerful tools in quantifying the uncertainties associated with the modelling and the experimental processes. Based on Bayes’ theorem, the posterior distribution function can be employed to describe...

This book explains how various forms of artificial intelligence, namely machine learning, natural language processing, and robotic process automation, could provide a source of competitive advantage to firms deploying them compared to those firms that would not have deployed these technologies. The advantages of machine learning, natural language p...

Magnetic Hamiltonian Monte Carlo (MHMC) has been shown to provide more efficient sampling of the target posterior compared to Hamiltonian Monte Carlo (HMC). It achieves this by utilising a user specified magnetic field and the resultant non-canonical Hamiltonian dynamics. This is important for multi-modal distributions which are common in machine l...

Hybrid Monte Carlo (HMC) has been widely applied to numerous posterior inference problems in machine learning and statistics. HMC has two main practical issues, the first is the deterioration in acceptance rates as the system size increases and the second is its sensitivity to two user-specified parameters: the step size and trajectory length. The...

The problem of local minimum in k-means clustering, is commonly addressed by running the algorithm repeatedly in order to choose the best run. Although effective, the approach is computationally expensive. In this paper, we observe that the approach is effectively a comparison among different initializations. Thus, if there is a way to compare thes...

We introduce a highly efficient k-means clustering approach. We show that the classical central limit theorem addresses a special case (k = 1) of the k-means problem and then extend it to the general case. Instead of using the full dataset, our algorithm named k-means-lite applies the standard k-means to the combination C (size nk) of all sample ce...

The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for performing accurate inference with limited data. Fundamental to the design of rapid state responses is the ability to perform epidemiological model parameter inference for localised trajectory predictions. In this work, we perform Bayesian paramet...

Speech emotion recognition (SER) is a machine learning problem where the speech utterances are classified depending on their underlying emotions. This chapter presents an overview of the prominent classification techniques used in SER. There are two broad categories of classifiers used in SER, the linear classifiers and the non‐linear classifiers....

Emotions are ubiquitous to human life. Previous studies suggest that ninety percent of the life experience of humankind is affected by at least one emotion, and that emotional state guides their thoughts and behavior. The neurological study suggests that the design of human brain ensures that the emotional brain almost always influences the rationa...

In this chapter, we discuss creative destruction theory. This is defined as the process by which information and communication technology destroys previous technological solutions and lays waste old companies in order to make room for the new companies. One may think about the creative destruction in recent times, where searches were previously don...

In this chapter, we discuss the agency theory. The agency theory is a principle utilized in an attempt to explain the complicated relationship that exists between the owners (principal) and managers (agents) of the business. Based on this, we propose that the agency theory is an attempt to explain the complexity of human behaviour in the principal-...

In this chapter, we discuss the concept of adverse selection, which is a problem that stems from the information asymmetry where a strategic behaviour by the more informed counterparty in a contract works against the interest of the less informed counterparties. We looked at the two ways in which management literature has suggested that it is used...

In this chapter, we discuss the moral hazard. We make a point that it is a concept that cannot be separated from the adverse selection, which is a problem that stems from information asymmetry. Further, we looked at the two ways in which management literature has suggested to manage asymmetric information, adverse selection and moral hazard. These...

In this chapter, we discuss the Laffer Curve. We look at the effect of AI on the curve. Arthur Laffer advanced an argument that changes in tax rates affect government revenues differently in the short term and an extended basis. Initially, the increase in the tax rate would be followed by an increase in tax revenues generated by the government. Thi...

This chapter provides a high-level introduction of artificial intelligence (AI) in economics and finance theories. It describes what AI is and how it is changing the field of finance and economics, particularly some of the key theories embedded in this field. Further, the chapter outlines the 13 chapters that are covered in this book. Following the...

In this chapter, we discuss the Dual-Sector Model, our primary aim being the determination of the impact of AI on the model. In reviewing the literature on the dual-sector model, what is clear is that labour is the crucial factor of production in both the agricultural sector and the industrial sector. It is common cause that in the era that is char...

In this chapter, we discuss the Dynamic Inconsistency theory, which reflects a changing nature of economic agents’ preference over a period of time, and which could result in these preferences differing at some point in the preference continuum; this yields inconsistencies. This means that not all selected preferences are aligned, and that there is...

This chapter pays attention to the Philipps Curve. This theory states that inflation and unemployment have a stable and inverse relationship (Phillips 1958). In this theory, economic growth is expected to generate inflation and more work opportunities, which decrease unemployment. We review how the application of AI would impact assumptions of the...

This chapter introduces David Ricardo’s theory, sometimes known as the Ricardian Model of comparative advantage. Primarily, we intend to examine its underlying assumptions. We do this to lay the ground for understanding the critical pillars of the model. In essence, the Ricardian Model assumed two countries, producing two goods that are homogeneous...

This chapter introduces the growth model. It proceeds to the examination of the tenets of this theory, such as the catching-up growth phenomenon, the steady growth path phenomenon and the cutting edge growth path phenomenon. Further, this chapter introduces artificial intelligence (AI) by presenting two scenarios that seek to explain how AI could a...

In this chapter, we discuss the legitimacy theory and the legitimacy gap. Organizations seek to be perceived by stakeholders as legitimate. Because legitimacy is a moving target, organizations have to be pragmatic. The legitimacy gap will be formed due to the concept of time, which informs the movement of expectations. As time progresses, the envir...

In Chap. 1, we submitted that the world is changing rapidly. We pointed out that there is no other time in history when virtually every aspect, whether it is human life, economies or politics, among other things, has been affected the rapid change brought by developments in information technology (Harari, 21 lessons for the 21st century. Jonathan C...

The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for the development of prompt mitigating responses under conditions of high uncertainty. Fundamental to the design of rapid state reactions is the ability to perform epidemiological model parameter inference for localised trajectory predictions. In th...

The rapid spread of the novel coronavirus (SARS-CoV-2) has highlighted the need for the development of rapid mitigating responses under conditions of extreme uncertainty. While numerous works have provided projections of the progression of the pandemic, very little work has been focused on its progression in Africa and South Africa, in particular....

We present an Automatic Relevance Determination prior Bayesian Neural Network(BNN-ARD) weight l2-norm measure as a feature importance statistic for the model-x knockoff filter. We show on both simulated data and the Norwegian wind farm dataset that the proposed feature importance statistic yields statistically significant improvements relative to s...

As Artificial Intelligence (AI) seizes all aspects of human life, there is a fundamental shift in the way in which humans are thinking of and doing things. Ordinarily, humans have relied on economics and finance theories to make sense of, and predict concepts such as comparative advantage, long run economic growth, lack or distortion of information...

In this paper we give introduction to the concepts of Ubuntu and how we used Mechanism design concepts to construct Ubuntu as an optimisation algorithm. Ubuntu philosophy is old and consists of many oral proverbs that have been documented in recent years. This work thus introduces an incentive mechanism based on Ubuntu, thus called Ubuntu Incentive...

Orientation: This article is related to Finances and Optimisation. The auctioneer designs every auction mechanism such that utility is maximised and cost is minimised. Research purpose: This article proposes an optimal auction mechanism through which auctioneers can assign fairly and efficiently assets to the highest bidders and maximise utility a...