Bøker av Tshilidzi Marwala

Recent advances in AI and Mechanism Design provide a vital tool for solving collective action problems, common in international relations. By using AI to optimize mechanisms for cooperation and coordination, we can better address issues such as climate change, trade, and security. Mechanism Design, Behavioral Science and Artificial Intelligence in International Relations shows readers how the intersection of Mechanism Design and Artificial Intelligence is revolutionizing the way we approach international relations. By using AI to optimize mechanisms, we can design better institutions, policies, and agreements that are more effective and efficient. Dr. Tshilidzi Marwala, United Nations University Rector and UN Under-Secretary General, presents the essential technologies used in Game Theory, Mechanism Design and AI and applies these to significant global issues such as interstate conflict, cybersecurity, and energy. International relations are a complex field, with many different actors and interests in play. By incorporating AI into our analysis and decision-making processes, we can better understand and predict the behavior of multiple actors and design mechanisms that take these behaviors into account, thereby producing more desirable and creative interdisciplinary approaches. The book presents real-world applications of these rapidly evolving technologies in crucial research fields such as Interstate Conflict, International Trade, Climate Change, Water management, Energy, cybersecurity, and global finance.

This book examines enterprise risk management in the fourth industrial revolution, and the technologies associated with this phenomenon. In doing so, it seeks to understand these technologies' potential capabilities, and how they could be utilised in the enterprise risk management setting. With this, the book first details the fourth industrial revolution (4IR), and discusses the concept of enterprise risk management, the stakeholders involved, the typical information stakeholders will be responsible for, and their role in integrating risk management information. The book then examines the information processing steps and the new capabilities in the enterprise risk setting necessitated by the capabilities of the 4IR technologies to harness, analyse and integrate information for decision-making and understanding internal and external contexts. In the final chapter, the book conceptualises enterprise risk management in the 4IR, and maps out potential role changes in this space.

This book discusses the impact of artificial intelligence (AI) on international relations theories. As a phenomenon, AI is everywhere in the real world and growing. Through its transformative nature, it is simultaneously simplifying and complicating processes. Importantly, it also overlooks and ¿misunderstands¿. Globally, leaders, diplomats and policymakers have had to familiarise themselves and grapple with concepts such as algorithms, automation, machine learning, and neural networks. These and other features of modern AI are redefining our world, and with it, the long-held assumptions scholars of IR have relied on for their theoretical accounts of our universe. The book takes a historic, contemporary and long-term approach to explain and anticipate AI¿s impact on IR ¿ and vice versa ¿ through a systematic treatment of 9 theoretical paradigms and schools of thought including realism, liberalism, feminism, postcolonial theory and green theory. This book draws on original datasets, innovative empirical case studies and in-depth engagement with the core claims of the traditional and critical theoretical lenses to reignite debates on the nature and patterns of power, ethics, conflict, and systems among states and non-state actors.

Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts.

What happened to the 'old' intellectual movement? What happened to the thinkers who inspired and led our struggle? In pursuit of answers to these questions, MISTRA in partnership with the Liliesleaf Trust, hosted a roundtable in March 2015 on the theme 'The Role of Intellectuals in the State-Society Nexus'.

This self-contained book, written by active researchers, presents up-to-date information on smart maintenance strategies for human-robot interaction (HRI) and the associated applications of novel search algorithms in a single volume, eliminating the need to consult scattered resources.

The issue of missing data imputation has been extensively explored in information engineering. This book presents methods and technologies in estimation of missing values given the observed data. It covers techniques such as radial basis functions, support vector machines, and principal component analysis.

Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical EngineeringTshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South AfricaSondipon Adhikari, Swansea University, UKCovers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineeringFinite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering.The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure. The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering.Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering.Key features:* Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students.* Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations.The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.

This book examines the application of artificial intelligence methods to model economic data. It addresses causality and proposes new frameworks for dealing with this issue. It also applies evolutionary computing to model evolving economic environments.