Artificial Intelligence and Economic Theory: Skynet in the Market
Abstract
This book theoretically and practically updates major economic ideas such as demand and supply, rational choice and expectations, bounded rationality, behavioral economics, information asymmetry, pricing, efficient market hypothesis, game theory, mechanism design, portfolio theory, causality and financial engineering in the age of significant advances in man-machine systems. The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence concepts such as the swarming of birds, the working of the brain and the pathfinding of the ants.
Artificial Intelligence and Economic Theory: Skynet in the Market analyses the impact of artificial intelligence on economic theories, a subject that has not been studied. It also introduces new economic theories and these are rational counterfactuals and rational opportunity costs. These ideas are applied to diverse areas such as modelling of the stock market, credit scoring, HIV and interstate conflict. Artificial intelligence ideas used in this book include neural networks, particle swarm optimization, simulated annealing, fuzzy logic and genetic algorithms. It, furthermore, explores ideas in causality including Granger as well as the Pearl causality models.
Chapters (15)
This chapter introduces this book, Artificial Intelligence and Economic Theory: Skynet in the market, and in the process studies some of the big ideas that have concerned economics and finance in the last 300 years. These ideas include Marxist thinking, the theory of invisible hand, the theory of equilibrium and the theory of comparative advantage. It, furthermore, describes methods in artificial intelligence such as learning, optimization and swarm intelligence. It sets a scene on how these theories can be better understood by using artificial intelligence techniques, thereby, setting a scene for the rest of the book.
The law of demand and supply is the fundamental law of economic trade. It consists of the demand characteristics of the customer which describes the relationship between price and quantity of goods. For example, if the price of a good is low the customer will buy more goods and services than if the price is high. The relationship between price and the willingness of the customers to buy goods and services is called the demand curve. The other aspect of the demand and supply law is the supply curve which relates the relationship between the price and the quantity of goods suppliers are willing to produce. For example, the higher the price the more the goods and services the suppliers are willing to produce. Conversely, the lower the price the lesser the goods and services the suppliers are willing to produce. The point at which the suppliers are willing to supply a specified quantity of goods and services which are the same as those that the customers are willing to buy is called equilibrium. This chapter studies how the law of demand and supply is changed by the advent of artificial intelligence (AI). It is observed that the advent of AI allows the opportunity for individualized demand and supply curves to be produced. Furthermore, the use of an AI machine reduces the degree of arbitrage in the market and therefore brings a certain degree of fairness into the market which is good for the efficiency of the economy.
The theory of rational choice assumes that when people make decisions they do so in order to maximize their utility. In order to achieve this goal they ought to use all the information available and consider all the options available to select an optimal choice. This chapter investigates what happens when decisions are made by artificial intelligent machines in the market rather than human beings. Firstly, the expectations of the future are more consistent if they are made by artificial intelligent machines than if they are made by human beings in that the bias and the variance of the error of the predictions are reduced. Furthermore, the decisions that are made are more rational and thus the marketplace becomes more rational.
Rational decision making involves using information which is almost always imperfect and incomplete, together with some intelligent machine, which if it is a human being is inconsistent in making a decision that maximizes utility. Since the world is not perfect and decisions are made irrespective of the fact that the information to be used is incomplete and imperfect, these decisions are rationally limited (bounded). Recent advances in artificial intelligence and the continual improvement of computer processing power due to Moore’s law have implications for the theory of bounded rationality. These advances expand the bounds within which a rational decision making process is exercised and, thereby, increases the probability of making rational decisions.
Behavioural economics is an approach to economics which takes into account human behavior. In his book “Thinking fast and slow”, which is based on the work he did with Tversky, Kahneman describes human thought as being divided into two systems i.e. System 1 which is fast, intuitive and emotional, and System 2, which is slow, rational and calculating. He further described these systems as being the basis for human reasoning, or the lack thereof, and the impact of these on the markets. Some of the findings are the inability of human beings to think statistically, called heuristics and biases, the concept of Anchoring, Availability effect, Substituting effect, Optimism and Loss aversion effect, Framing effect, Sunk costs and Prospect theory where a reference point is important in evaluating choices rather than economic utility. With the advent of decision making using intelligent machines, all these effects and biases are eliminated. System 1, which is intuitive, is eliminated altogether. System 2 becomes the norm, as advances in artificial intelligence are made. System 2 becomes fast because contemporary computational intelligent machines work fast. If one considers Moore’s Law, which states that computational power doubles every year, System 2 next year is faster than System 2 this year, thus making machines “Think Fast and Faster”.
Often when human beings interact to make decisions, one human agent has more information than the other and this phenomenon is called information asymmetry. The fact that information asymmetry distorts the markets won Akerlof, Stiglitz and Spence a Nobel Prize. Generally, when one human agent is set to manipulate a decision to its advantage, the human agent can signal misleading information. On the other hand, one human agent can screen for information to diminish the influence of asymmetric information on decisions. With the dawn of artificial intelligence (AI), signaling and screening are easier to achieve. This chapter investigates the impact of AI on the theory of asymmetric information. The simulated results demonstrate that AI agents reduce the degree of information asymmetry and, therefore, the market where these agents are used become more efficient. It is also observed that the more AI agents that are deployed in the market, the less is the volume of trades in the market. This is because of the fact that for trades to occur, asymmetry of information should exist, thereby, creating a sense of arbitrage.
Game theory has been used quite extensively in economics. In game theory agents with rules interact to obtain pay-off at some equilibrium point often called Nash equilibrium. The advent of artificial intelligence makes intelligent multi-agent games possible. This enriches the ability to simulate complex games. In this chapter, intelligent multi-agent system is applied to study the game of Lerpa.
Pricing theory is a well-established mechanism that illustrates the constant push-and-pull of buyers versus consumers and the final semi-stable price that is found for a given good. Embedded in the theory of pricing is the theory of value. This chapter studies various pricing models and, in particular, how they are changed by the advances in artificial intelligence (AI). The first pricing model studied is game theory based pricing where agents interact with each other until they reach a Nash equilibrium price. Multi-agent systems are found to enhance this pricing model. The second is rational pricing and here when pricing the amount of arbitrage is minimized and AI is found to improve this model. The third is capital asset pricing model, which is also improved by the advent of evolutionary programming. Then the fourth is the Black-Scholes pricing model, which is impacted by the use of fuzzy logic to model volatility. The last one is the law of demand and supply, and it is found that the advent of AI within the context of online shopping infrastructure results in individualized pricing model.
The efficient market hypothesis (in its varying forms) has allowed for the creation of financial models based on share price movements ever since its inception. This chapter explores the impact of artificial intelligence (AI) on the efficient market hypothesis. Furthermore, it studies theories that influence market efficiency and how they are changed by the advances in AI and how they impact on market efficiency. It surmises that advances in AI and its applications in financial markets make markets more efficient.
In game theory, players have rules and pay-off and they interact until some point of equilibrium is achieved. This way, we are able to see how a game with sets of rules and a pay-off reaches equilibrium. Mechanism design is the inverse of that, we know what the end-state should look like and our task is to identify the rules and pay-off function which will ensure that the desired end-state is achieved. This is done by assuming that the agents in this setting act rationally. However, these agents are bounded rationally because the degree of rationality is limited. This chapter also discusses how artificial intelligence impacts mechanism design.
The basis of portfolio theory is rooted in statistical models based on Brownian motion. These models are surprisingly naïve in their assumptions and resultant application within the trading community. The application of artificial intelligence (AI) to portfolio theory and management have broad and far-reaching consequences. AI techniques allow us to model price movements with much greater accuracy than the random-walk nature of the original Markowitz model. Additionally, the job of optimizing a portfolio can be performed with greater optimality and efficiency using evolutionary computation while still staying true to the original goals and conceptions of portfolio theory. A particular method of price movement modelling is shown that models price movements with only simplistic inputs and still produces useful predictive results. A portfolio rebalancing method is also described, illustrating the use of evolutionary computing for the portfolio rebalancing problem in order to achieve the results demanded by investors within the framework of portfolio theory.
The concept of rational counterfactuals is an idea of identifying a counterfactual from the factual (whether perceived or real), and knowledge of the laws that govern the relationships between the antecedent and the consequent, that maximizes the attainment of the desired consequent. In counterfactual thinking, factual statements like: ‘Greece was not financially prudent and consequently its finances are in tatters’, and with its counterfactual being: ‘Greece was financially prudent and consequently its finances are in good shape’. In order to build rational counterfactuals, artificial intelligence (AI) techniques are applied. The interstate conflict example considered uses AI to create counterfactuals that are able to maximize the attainment of peace.
Financial engineering has grown with the advent of computing and this growth has accelerated in the last decade with the advances in artificial intelligence (AI). This chapter explores how subjects such as evolution, deep learning and big data are changing the effectiveness of quantitative finance. This chapter explores the problem of estimating HIV risk, simulating the stock market using multi-agent systems, applying control systems for inflation targeting and factor analysis. The results demonstrate that AI improves the estimation of HIV risk, makes stock markets homogeneous and efficient, is a good basis for building models that target inflation and enhances the identification of factors that drive inflation.
Causality is a powerful concept which is at the heart of markets. Often, one wants to establish whether a particular attribute causes another. As human beings, we have perceived causality through correlation. Because of this fact, causality has often been confused for correlation. This chapter studies the evolution of causality including the influential work of David Hume and its relevance to economics and finance. It studies various concepts and models of causality such as transmission, Granger and Pearl models of causality. The transmission model of causality states that for causality to exist, there should be a flow of information from the cause to the effect. Simple example of the study on the link between circumcision and risk of HIV are used in this chapter.
This chapter concludes this book and summarizes the general direction of artificial intelligence and economics. It summarizes all the key concepts addressed in this book such as rational expectations and choice, bounded rationality, behavioral economics, information asymmetry, game theory, pricing, efficient market hypothesis, mechanism design, portfolio theory, rational counterfactuals, financial engineering and causality. Additionally, it evaluates how all these ideas are influenced by the advent of artificial intelligence. This chapter also studies the concept of decision making which is based on the principles of causality and correlation. Then it proposes a way of combining neoclassical, Keynesian and behavioral economics together with artificial intelligence to form a new economic theory. Furthermore, it postulates on how the interplay between advances in automation technologies and human attributes that can be automated determine a limit of the extent of automation in an economy or a firm.
... Thus, an AI agent is able to form expectations in a high-dimensional environment. Furthermore, the incompleteness of an information set might become less of an issue, as AI can fill in the information gaps using techniques for estimating missing data (Marwala and Hurwitz 2017). Thus, according to Marwala and Hurwitz (2017), AI extends the limits of bounded rationality and necessitates the concept of flexibly-bounded rationality: ...
... Furthermore, the incompleteness of an information set might become less of an issue, as AI can fill in the information gaps using techniques for estimating missing data (Marwala and Hurwitz 2017). Thus, according to Marwala and Hurwitz (2017), AI extends the limits of bounded rationality and necessitates the concept of flexibly-bounded rationality: ...
... As mentioned above, human economic actors' ability to engage in this type of thinking suggests the superiority of fictional and narrative expectations over AI agents. However, this advantage might disappear because some recent AI techniques can be used to develop counterfactuals (Marwala and Hurwitz, 2017). Counterfactual explanations are particularly crucial for explainable AI (Chou et al. 2021;Baron 2023) Finally, we observe a quite fast development of computational narrative intelligence that aims to develop artificial intelligence and machine learning technologies which can create and understand stories. ...
... With the recent advances in the field of computation, artificial intelligence (AI), science and technology, etc., there has been a growing thought on humans, being assisted with machine intelligence, achieving optimal decision and moving towards higher levels of rationality [8]. Several question arise in this context [9]. ...
... In a repeated zero-sum game, the unbounded rational player has a strategy to bring down the payoff of the bounded rational player. With recent advances in AI and computing for the past one and a half decade, if agents are bounded rational and they are aided by machines to help obtain the optimal decision most of the time, then bounds of bounded rationality are likely to be extended [8]. ...
... One group of research works believe that advances in computing and other related fields will enhance the understanding of bounded rationality [18]. The third group believes that the advances in computing, AI, etc., can actually enhance to bounds of bounded rationality towards perfect rationality [8]. ...
Rationality has been an intriguing topic for several decades. Even the scope of definition of rationality across different subjects varies. Several theories (e.g., game theory) initially evolved on the basis that agents (e.g., humans) are perfectly rational. One interpretation of perfect rationality is that agents always make the optimal decision which maximizes their expected utilities. However, subsequently this assumption was relaxed to include bounded rationality where agents have limitations in terms of computing resources and biases which prevents them to take the optimal decision. However, with recent advances in (quantum) computing, artificial intelligence (AI), science and technology etc., has led to the thought that perhaps the concept of rationality would be augmented with machine intelligence which will enable agents to take decision optimally with higher regularity. However, there are divergent views on this topic. The paper attempts to put forward a recent survey (last five years) of research on these divergent views. These viewsmay be grouped into three schools of thoughts. The first school is the one which is sceptical of progress of AI and believes that human intelligencewill always supersede machine intelligence. The second school of thought thinks that advent of AI and advances in computing will help in better understanding of bounded rationality. Third school of thought believes that bounds of bounded rationality will be extended by advances in AI and various other fields. This survey hopes to provide a starting point for further research.
... There is no time in history when virtually every aspect of human life has been affected by the rapid change brought about through information technology (Harari, 2018). According to Marwala and Hurwitz (2017), the world has witnessed four phases of revolution in human history. These revolutions are described below: ...
... • The first industrial revolution brought mechanical innovations with the development of steam engine which was key to the then industrial revolution; • The second industrial revolution which started in the second half of the nineteenth century brought the oil-powered internal combustion engine and electrical communication. Major technological advances during this period included the telephone, light bulbs and phonograph (Marwala & Hurwitz, 2017); • The third industrial revolution or digital revolution which came in the 1980s brought computerization, that is, mainframe computers, personal computers and the internet, and the information and communication technology (ICT) available today. This has been a period of advancement of technology from analog electronic and mechanical devices to the digital technology (Agrawal, Gans, & Goldfarb, 2018;Marwala & Hurwitz, 2017); and • The fourth industrial revolution which has arrived at the beginning of the twenty-first century is the advent of cyber-physical systems representing new ways in which technology becomes embedded within societies, that is, business, government, civil society, and so on, and the human body; it is driven by the rapid convergence of advanced technologies across the biological, physical and digital worlds. ...
... Major technological advances during this period included the telephone, light bulbs and phonograph (Marwala & Hurwitz, 2017); • The third industrial revolution or digital revolution which came in the 1980s brought computerization, that is, mainframe computers, personal computers and the internet, and the information and communication technology (ICT) available today. This has been a period of advancement of technology from analog electronic and mechanical devices to the digital technology (Agrawal, Gans, & Goldfarb, 2018;Marwala & Hurwitz, 2017); and • The fourth industrial revolution which has arrived at the beginning of the twenty-first century is the advent of cyber-physical systems representing new ways in which technology becomes embedded within societies, that is, business, government, civil society, and so on, and the human body; it is driven by the rapid convergence of advanced technologies across the biological, physical and digital worlds. It is marked by emerging technology breakthroughs in a number of fields, including robotics, artificial intelligence, biotechnology, and so on. ...
The whole business world is undergoing continuous transformation due to the innovations taking place in the business process and models as part of digital transformation. Earlier worries about ‘digital disruptions’ are not annoying anyone anymore and everyone is in a race to leverage the changes and opportunities of digital technologies at their best for the benefit of their organization and people. The percentage of ‘digital natives’ among various groups of stakeholders such as consumers, employees, and so on is showing a phenomenal increase and poses a greater challenge to the business organizations in terms of building loyalty and commitment among the people concerned. The intention, behavior, and involvement of digital natives in digital platforms and their use are characteristically different from others such as ‘digital immigrants’ or mere ‘digital literates.’ This chapter attempts to throw some light on how to manage digital natives both as internal and external stakeholders of the business. The authors elaborate on various ways and means, including the extent of application of the advantages of social media, for engaging digital natives both as a consumer and as an employee. Skills and abilities needed for employees to make a successful digital transition are also narrated.
... Big data, or extensive datasets with the Five Vs (volume, velocity, variety, veracity, and value) in mind, are the result of the exponential increase of data, which has been driven by the widespread use of social media and the development of the Internet of Things (IoT). Big data is becoming an increasingly useful tool for comprehending market dynamics and consumer behavior, which is changing the definition of market expertise for B2B organizations [46]. ...
... The rapid adoption of AI in industries like professional services, as demonstrated by the text, highlights the technology's potential to transform B2B marketing and sales operations completely. AI's capacity to continuously learn and adapt, in addition to processing data, is the foundation of its disruptive potential [46]. ...
Background: This research examines how market knowledge and artificial intelligence (AI) interact in different market designs such as business-to-business (B2B) settings while taking emerging technologies and the changing digitalization landscape into account. Objective: The main goal is to understand how AI affects market knowledge in different market designs such as business-to-business (B2B) contexts, taking into account language barriers, practical difficulties, and the revolutionary effects on decision-making and customer interactions. Result: They underscore the transformative potential of artificial intelligence (AI) by highlighting how it shapes market knowledge, encourages customized approaches, and improves marketing efficacy in the business-to-business (B2B) space. Conclusion: In order to create a path for responsible AI integration in B2B marketing, the study concludes with recommendations for standardized terminology related to AI, practical insights into implementation challenges, and ethical issues.
... P2P trading enables individual EV owners to trade electricity at their preferred prices. However, human beings such as EV owners and EVCS operators are unpredictable and easily influenced by external factors such as prejudices, mood swings, and lack of capacity to process large data [145]. Therefore, artificial intelligence machines are expected to be used for decision-making in the fourth industrial revolution [145]. ...
... However, human beings such as EV owners and EVCS operators are unpredictable and easily influenced by external factors such as prejudices, mood swings, and lack of capacity to process large data [145]. Therefore, artificial intelligence machines are expected to be used for decision-making in the fourth industrial revolution [145]. Using an intelligent machine to make decisions can minimize human behavioral characteristics such as heuristics, risk aversion, and diminishing sensitivity to losses and gains. ...
The high penetration of electric vehicles (EVs) will burden the existing power delivery infrastructure if their charging and discharging are not adequately coordinated. Dynamic pricing is a special form of demand response that can encourage EV owners to participate in scheduling programs. Therefore, EV charging and discharging scheduling and its dynamic pricing model are important fields of study. Many researchers have focused on artificial intelligence-based EV charging demand forecasting and scheduling models and suggested that artificial intelligence techniques perform better than conventional optimization methods such as linear, exponential, and multinomial logit models. However, only a few research studies focused on EV discharging scheduling (i.e., vehicle-to-grid, V2G) because the concept of EV discharging electricity back to the power grid is relatively new and evolving. Therefore, a review of existing EV charging and discharging-related studies is needed to understand the research gaps and to make some improvements in future studies. This paper reviews EV charging and discharging-related studies and classifies them into forecasting, scheduling, and pricing mechanisms. The paper determines the linkage between forecasting, scheduling, and pricing mechanism and identifies the research gaps in EV discharging scheduling and dynamic pricing models.
... Simultaneously they are heading towards becoming more intelligent with the help of Artificial Intelligence (AI). Alan Turing theorized that a machine is artificially intelligent if and only if we interact with them and we cannot tell we are interacting with a human or machine, called Turing test (Marwala & Hurwitz, 2017). ...
The global economic scenario is changing very fast just in a short time period of two to four years, as we have seen in the last couple of years in terms of trade wars between the US and China, Shocks like the Covid-19 pandemic, and Russia-Ukraine and other wars, cyclic expectation of a global recessions and now further the evolution of new age of man-made non-biological intelligence technology. Managers of the new age need to be super sensitive to these fast-paced economic changes happening around the business environment and have to gauge in advance how these are going to impact their business. Keeping this in mind, managers need to upskill themselves. There is a revolution due to technology in every field. Parts of human tasks are becoming automated and now the main task of humans, that is thinking, and decision making is going to be augmented and, in some cases, replaced by Artificial intelligence, making the technology much more powerful and efficient than previously thought of. In this chapter, we underline the current developments and possible future possibilities of Artificial intelligence and the preparedness a 21st-century Management has to be ready with, in the current era of rapidly evolving technology.
... South African organizations face similar obstacles, compounded by historical and socio-economic factors. Resistance to change, lack of digital skills, and insufficient senior management support are prevalent issues (Marwala, 2021). Efforts to improve organizational dynamics focus on enhancing leadership and fostering a culture of innovation (Sanchez & Zuntini, 2021). ...
This article seeks to analyse the impact of systemic hurdles, organisational inertia, and technology limitations on the digital transformation initiatives of the State Information Technology Agency (SITA) in South Africa. The study aims to present a comprehensive framework to improve SITA's digital transformation strategy by tackling these obstacles. This article aims to identify key areas including policy restructuring, capacity building, and the adoption of modern technologies such as cloud computing, big data, and the Internet of Things (IoT). We utilised a mixed qualitative-analytical approach for data collecting and processing. Primary data were collected via semi-structured interviews with SITA personnel, while secondary data were sourced from official SITA papers, such as annual reports and strategy plans. Thematic analysis was employed to discern reoccurring patterns and themes. An analysis was conducted on a dataset of SITA's digital activities spanning from 2012 to 2021. The study's principal findings indicate that (i) systemic obstacles, including antiquated infrastructure and reluctance to change, substantially hinder digital transformation, (ii) effective security protocols and strategic alliances are crucial for surmounting these obstacles, and (iii) the establishment of a comprehensive framework encompassing capacity building and the integration of advanced technologies can significantly bolster SITA's digital transformation initiatives, thereby enhancing. Nonetheless, the study possesses drawbacks, notably its dependence on qualitative data, which may not encompass the entirety of challenges encountered by SITA. Subsequent study ought to adopt a mixed methodologies approach to facilitate a more thorough analysis. Notwithstanding these constraints, the report provides significant insights and pragmatic recommendations for policymakers and practitioners engaged in digital transformation programs at SITA.
... By calculating individual demand and supply curves, AI techniques provide individualised pricing (Marwala & Hurwitz, 2017).Marketers can now use AI to analyse customer data and make educated guesses about their online habits in order to better tailor ads and products to individual users. Advertising budgets have been cut thanks to the incorporation of personality computing AI techniques, which add psychological targeting to more common behavioural targeting. ...
... The Internet and AI technologies make global interaction possible. This allows companies to address diverse geographic markets where different needs and requirements may exist (Marwala & Hurwitz, 2017). Consequently, regional and global markets, in the context of AI development, become more fragmented, with a greater number of small and medium-sized companies competing for consumer attention and loyalty. ...
This article addresses one of the pressing issues regarding the role of artificial intelligence (AI) in international relations and international law. The research question revolves around defining the theoretical and methodological approaches applicable to the strategic analysis of AI utilization in these fields. In the contemporary world, there is a demand at both interstate and societal levels to define the role of AI in the political and legal spheres. This is because AI development affects crucial areas of state relations such as security, international law, ethical norms, and dependencies. The prospective use of AI technologies without corresponding legal regulation may disrupt the already fragile balance of the world order, which could be exacerbated by state competition in AI technologies and AI applications in the military domain, a grey area in international law. Analyzing this issue from the perspective of international relations and international law theory allows for examining AI's impact on state interactions and developing new application strategies. Similarly, it helps understand how international law regulates state relations, including aspects related to AI applications. By examining various theoretical concepts and methodological approaches necessary for understanding AI's impact on global affairs, including its influence on diplomacy, security, and governance structures, as well as legal and ethical issues, this article contributes to Kazakhstan's evolving discourse on AI governance and its implications for state actors. KEYWORDS: Artificial Intelligence, theory and methodology, international law, international relations. Artificial Intelligence (AI) is a rapidly evolving technology that is already exerting significant influence on international relations and international law. When analyzing PP 4-21 D O I. O R G / 1 0. 5 2 5 3 6 / 3 0 0 6-8 0 7 X. 2 0 2 4-1. 0 1
... The Internet and AI technologies make global interaction possible. This allows companies to address diverse geographic markets where different needs and requirements may exist (Marwala & Hurwitz, 2017). Consequently, regional and global markets, in the context of AI development, become more fragmented, with a greater number of small and medium-sized companies competing for consumer attention and loyalty. ...
This article addresses one of the pressing issues regarding the role of artificial intelligence (AI) in international relations and international law. The research question revolves around defining the theoretical and methodological approaches applicable to the strategic analysis of AI utilization in these fields. In the contemporary world, there is a demand at both interstate and societal levels to define the role of AI in the political and legal spheres. This is because AI development affects crucial areas of state relations such as security, international law, ethical norms, and dependencies. The prospective use of AI technologies without corresponding legal regulation may disrupt the already fragile balance of the world order, which could be exacerbated by state competition in AI technologies and AI applications in the military domain, a grey area in international law. Analyzing this issue from the perspective of international relations and international law theory allows for examining AI's impact on state interactions and developing new application strategies. Similarly, it helps understand how international law regulates state relations, including aspects related to AI applications. By examining various theoretical concepts and methodological approaches necessary for understanding AI's impact on global affairs, including its influence on diplomacy, security, and governance structures, as well as legal and ethical issues, this article contributes to Kazakhstan's evolving discourse on AI governance and its implications for state actors.
... There are also a lot of variables to take in the calculus, like: the causal impact of media, investor's behavior, time-varying local economic conditions, history of prices, natural calamities, etc. that can systematically affect the fluctuation of the market. Some specialists in financial economics and big data consider that trying to predict and invest in a random manner can direct to same results, this is known as the EMH (Efficient Market Hypothesis) 1 or the RWH (Random Walk Hypothesis) [Fama, 1965; Jonathan Clarke et al., 2001;Zunino et al., 2012; Bariviera et al., 2014; Marwala, 2015;Marwala and Hurwitz, 2017]. ...
In this paper, we present a set of experiments on predicting the rise or fall of a cryptocurrency using machine learning algorithms and sentiment analysis of the afferent media (online press mainly). The machine learning part is using the data of the currencies (the prices at a specific time) to predict in a mathematical sense. The sentiment analysis of the media (articles about a cryptocurrency) will influence the mathematical prediction, depending on the feeling created around the currency. The study can be useful for entrepreneurs, investors, and normal users, to give them a clue on how to invest. Furthermore, the study is intended for research regarding natural language processing and human psychology (deducting the influence of masses through media) and also in pattern recognition.
... Simultaneously they are heading towards becoming more intelligent with the help of Artificial Intelligence (AI). Alan Turing theorized that a machine is artificially intelligent if and only if we interact with them and we cannot tell we are interacting with a human or machine, called Turing test (Marwala & Hurwitz, 2017). ...
Effect of Industrial revolution on marketing and managing customers
... Simultaneously they are heading towards becoming more intelligent with the help of Artificial Intelligence (AI). Alan Turing theorized that a machine is artificially intelligent if and only if we interact with them and we cannot tell we are interacting with a human or machine, called Turing test (Marwala & Hurwitz, 2017). ...
The global economic scenario is changing very fast just in a short time period of two to four years, as we have seen in the last couple of years in terms of trade wars between the US and China, Shocks like the Covid-19 pandemic, and Russia-Ukraine war and now further the expectation of a global recession. Managers of the new age need to be super sensitive to these fast-paced economic changes happening around the business environment and have to gauge in advance how these are going to impact their business.
Keeping this in mind managers need to upskill themselves. There is a revolution due to technology in every field. Parts of human tasks are becoming automated and now the main task of humans, that is thinking and decision making is going to be augmented and, in some cases, replaced by Artificial intelligence, making the technology much more powerful and efficient than previously thought of.
In this chapter, we underline the current developments and possible future possibilities of Artificial intelligence and the preparedness a 21st-century Management has to be ready with, in the current era of rapidly evolving technology.
... Simultaneously they are heading towards becoming more intelligent with the help of Artificial Intelligence (AI). Alan Turing theorized that a machine is artificially intelligent if and only if we interact with them and we cannot tell we are interacting with a human or machine, called Turing test (Marwala & Hurwitz, 2017). ...
Digitalization across a range of industry and service sectors is transforming the workplace and human resources. The adoption of disruptive technologies associated with the Fourth Industrial Revolution or known as Industry 4.0 is reshaping the way people work, learn, lead, manage, recruit, and interact with each
other. The aim of this book chapter is to contribute to the theoretical development of human resource management (HRM) in the context of Industry 4.0, promoting directions for the sector and the HRM professionals, organizations, and the
workforce that are required to face the challenges of Industry 4.0. This book chapter promote insights on digital trends resulting from Industry 4.0 affect the field of human resource management, HRM Industrial Revolutions, interaction of
digitalization in HR for the evolution of the digital age, competences needed in the Industrial Revolution in order to become more productive, human and digital.
... Simultaneously they are heading towards becoming more intelligent with the help of Artificial Intelligence (AI). Alan Turing theorized that a machine is artificially intelligent if and only if we interact with them and we cannot tell we are interacting with a human or machine, called Turing test (Marwala & Hurwitz, 2017). ...
In the information era, leveraging the power of the internet of
things and knowledge elements, the customer comes to the
purchasing table well prepared. The brands are challenged to
upkeep the customer requirements and create delight. There is
a lot of buzz on how content marketing can influence target
customer groups. This chapter provides a perspective of the
fuelling rise of visual communication strategies adopted by
brands in creating experiential engagement. A glimpse into the
cosmetic brands' digital engagement perspective is presented.
There is presently a lack of content-focused research on the
application of these Industry 4.0 cutting-edge technologies in
environmentally friendly production. To clarify how these
revolutionary technologies could affect the economic, social,
and environmental aspects of the manufacturing industries, a
thorough literature review was done.
... Simultaneously they are heading towards becoming more intelligent with the help of Artificial Intelligence (AI). Alan Turing theorized that a machine is artificially intelligent if and only if we interact with them and we cannot tell we are interacting with a human or machine, called Turing test (Marwala & Hurwitz, 2017). ...
This book provides a comprehensive guide to Industry 4.0 applications, presenting not only implementation aspects but also a conceptual framework for the design principles of these applications. Additionally, it discusses the new business models, new managerial skills, and workforce transformation for decision makers that need to be teaching both existing and future leaders and reflected in Industry 4.0 for the management profession. The book then examines the eminent technological advances that form the pillars of Management 4.0 and explores their potential technical and economic benefits through real-world examples. This book covers various issues related to the successful management of emerging technologies for the first time.
... Applied to the previously mentioned expectations about inflation, agents can form their expectations based on the expectations of other agents and the expectations coming from the market. Thus, AI makes decisions based on rational choice, which is a process of making decisions based on relevant information, in a logical, timely and optimized manner (Marwala & Hurwitz, 2017). However, if the system is based on decisions made by AI and on decisions made by humans, it is uncertain whether rational or behavioral choice will prevail. ...
The fourth industrial revolution at the beginning of the twenty-first century was marked by the significant development of new technologies. This development resulting in the transformation of the social and economic system. As a consequence of that transformation, it is necessary to consider the impact of technology on social and economic theories that study those systems. The aim of this paper is to consider the influence of artificial intelligence on one of the important economic concepts-the Phillips curve. Artificial intelligence refers to the development of software that enables machines to behave "intelligently". This means that AI uses algorithms to perform autonomous actions. The significance of the application of AI is reflected in her accuracy, speed, and practical applicability, because the results of the application of algorithms are more precise and based on innovative sources of information. The increasing use of these technologies is causing tectonic social changes. After the Great Depression of 1929, the problem of unemployment reached dramatic proportions, and at the same time it becomes the main subject of macroeconomic analysis. With the Keynesian revolution, the concept of the Phillips curve as the inverse relationship between the inflation rate and the unemployment rate have become one of the key instruments in the economic policy decision-making process. In the 1970s economic growth slowed while inflation rose. The Phillips curve loses its stability, while economic systems are hit by stagflation. The relationship between inflation and unemployment has been redefined within monetarism: the inflation-unemployment trade-off is achieved only in the long run at the level of the natural unemployment rate. New Keynesians added expectations about inflation to the Phillips curve (hybrid New Keynesian Phillips curve). In that Phillips curve, the relationship between individuals with adaptive and individuals with rational expectations is crucial. If the system is based on decisions made by AI and on decisions made by humans, it is uncertain whether rational or behavioral choice will prevail. If there are only rational agents in the system, then rational expectations dominate. The emergence of new technologies raised the question of its impact on the labor market. There are usually two points of view: firstly, that the emergence of new technologies leads to mass unemployment or secondly, that the emergence of new technologies will create new professions and new fields of work. If there is complementarity of human and machine labour, this leads to an increase in average wages. But, if AI dominates in the future data-driven society, policymakers should provide protection for low-and middle-skilled workers with the aim of reducing inequality, encouraging growth and preventing social difficulties.
... Researchers now have more resources to work with as AI techniques improve and datasets become more widely available, opening up new directions for investigation. According to Marwala and Hurwitz [35], advances in AI technologies have influenced the EMH and fuelled a need to learn from the market. According to a growing corpus of studies, capital markets can be predicted to some extent, according to a growing corpus of studies [36,37]. ...
Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicated with batch processing techniques since stock prices are highly correlated. In recent years, advances in machine learning have given us a lot of chances to use forecasting theory and risk optimization together. e study postulates a unique two-stage framework. First, the mean-variance approach is utilized to select probable stocks (portfolio construction), thereby minimizing investment risk. Second, we present an online machine learning technique, a combination of "perceptron" and "passive-aggressive algorithm," to predict future stock price movements for the upcoming period. We have calculated the classi cation reports, AUC score, accuracy, and Hamming loss for the proposed framework in the real-world datasets of 20 health sector indices for four di erent geographical reasons for the performance evaluation. Lastly, we conduct a numerical comparison of our method's outcomes to those generated via conventional solutions by previous studies. Our aftermath reveals that learning-based ensemble strategies with portfolio selection are e ective in comparison.
... The development of artificial intelligence techniques and the increased number of datasets that are easily publicly available brings about new opportunities for researchers to explore something new from the market. According to Tshilidzi [41], the rapid development of AI techniques influences the EMH theory and provides an efficient way to learn from the market. A growing amount of research has been conducted [42][43][44][45][46][47][48], finding that post attestation demonstrates that the financial market may be anticipated to some extent [37,49]. ...
People continuously hunt for a precise and productive strategy to control the stock exchange because the monetary trade is recognised for its unbelievably different character and unpredictability. Even a minor gain in predicting performance will be extremely profitable and significant. Our novel study implemented six boosting techniques, i.e., XGBoost, AdaBoost, Gradient Boosting, LightGBM, CatBoost, and Histogram-based Gradient Boosting, and these boosting techniques were hybridised using a stacking framework to find out the direction of the stock market. Five different stock datasets were selected from four different countries and were used for our experiment. We used two-way overfitting protection during our model building process, i.e., dynamic reduction technique and cross-validation technique. For model evaluation purposes, we used the performance metrics, i.e., accuracy, ROC curve (AUC), F-score, precision, and recall. The aim of our study was to propose and select a predictive model whose training and testing accuracy difference was minimal in all stocks. The findings revealed that the meta-classifier Meta-LightGBM had training and testing accuracy differences that were very low among all stocks. As a result, a proper model selection might allow investors the freedom to invest in a certain stock in order to successfully control risk and create short-term, sustainable profits.
The progress and incorporation of artificial intelligence (AI) into different facets of society require efficient governance frameworks to ensure ethical, secure, and accountable utilization. There are two main methods of AI governance: self-regulation and government regulation. Self-regulation encompasses industry-driven efforts, standards, and optimal methodologies created by AI developers and organizations to foster adaptability and ingenuity. Government regulation includes implementing official legislative actions and supervising public authorities. This ensures that the regulations are enforceable and accountable and safeguard the public interests. This chapter examines the advantages and constraints of self-regulation and government regulation in the governance of AI, emphasizing the significance of striking a balance between the two methods to provide a comprehensive and flexible governance framework. An equitable strategy can promote creativity while ensuring AI technologies conform to social norms, ethical standards, and legal obligations.
As artificial intelligence (AI) progresses, it brings substantial prospects and challenging obstacles. This chapter provides an overview of the two main characteristics of AI: seeking opportunities and avoiding risks. It highlights the importance of having a balanced governance system to maximize AI’s benefits while reducing negative consequences. The promise of AI to completely transform industries such as healthcare, finance, and transportation is contrasted with the problems it brings, including ethical dilemmas, security vulnerabilities, and socio-economic inequality. Efficient governance systems are essential, necessitating openness, responsibility, security, and equity to traverse these two difficulties effectively. The chapter suggests implementing regulatory laws that can adapt to technological improvements. It also emphasizes the importance of international cooperation in establishing standardized regulations and inclusive stakeholder participation to promote fair and equal growth of AI. The effectiveness of AI governance in striking a balance will ultimately decide society’s ability to effectively utilize AI’s capabilities for the benefit of the public while minimizing its potential risks.
Governance is crucial in the fast-growing artificial intelligence (AI) field to ensure ethical, legal, and social benefits. AI governance must balance the inflexibility of laws and regulations against the flexibility of policies and standards. Standards and laws set explicit expectations and protections, assuring consistency and responsibility. Policies and regulations are flexible, allowing quick responses to AI technology concerns and advancements. Innovation requires balancing these governance instruments to protect public interests, privacy, and security. Harmonizing strict laws and regulations with adaptive policies and standards creates a robust and responsive AI governance framework. Managing AI development and deployment complexity requires a balanced strategy to ensure ethical and responsible use.
This chapter explores the ethical implications of artificial intelligence (AI) algorithms, focusing on avoidable and unavoidable discrimination. Avoidable discrimination can be addressed through improved data governance, algorithm design, and regulatory practices, while unavoidable discrimination is inherent limitations due to technological constraints or legal and ethical standards. The study uses case studies from healthcare, finance, and interstate conflict to illustrate the impact of both types of discrimination. A multi-disciplinary approach proposes a framework for identifying, assessing, and addressing both forms of discrimination. Strategies for avoidable discrimination include data augmentation, algorithmic transparency, and fairness-aware machine learning techniques. The chapter recommends best practices for AI developers, policymakers, and regulatory bodies to create efficient, innovative, and fair AI systems that minimize algorithmic discrimination.
High-performance computing (HPC) has significantly impacted various fields, including scientific research, machine learning, and artificial intelligence (AI). The balance between Central Processing Units (CPUs) and Graphics Processing Units (GPUs) is crucial for HPC. CPUs are adaptable and robust, while GPUs offer exceptional performance in parallel processing, making them ideal for tasks requiring extensive data throughput, such as AI. This chapter explores the governance balance between CPU and GPU utilization, analyzing their advantages and limitations and suggesting strategies to enhance their collective performance. It emphasizes the importance of allocating hardware resources for each task, using hybrid computing frameworks, and optimizing software to achieve an optimal balance of efficiency. We can optimize computational efficiency, minimize processing times, and tackle complex computational challenges by utilizing the synergistic capabilities of CPUs and GPUs.
Integrating artificial intelligence (AI) into governance processes presents opportunities and challenges. The chapter explores the role of AI in memorizing and reasoning, aiming to create an ethical, transparent, and efficient governance framework. It highlights the importance of robust data management procedures and AI's cognitive capacity in governance contexts. The chapter suggests governance principles and regulatory methods to create efficient, independent AI systems that balance memorization versus thinking and are aligned with societal norms and values.
The chapter delves into the complex balance between truth and deception in artificial intelligence (AI) systems, a critical issue as AI continues influencing society. The potential for AI to uncover truths through data analysis and propagate deception, either unintentionally or intentionally, poses significant ethical and practical challenges. It also discusses the use of AI in industries like healthcare, finance, and interstate conflict, emphasizing the importance of truth vs deception. The chapter suggests a framework for embedding ethical considerations into AI development and operational processes, including rigorous testing protocols, transparent AI systems, and robust oversight mechanisms. The chapter concludes that balancing truth and deception in AI requires collaboration from policymakers, end-users, and ethicists, fostering an interdisciplinary approach to AI governance to harness AI benefits while minimizing risks.
Synthetic and real-world data are used in AI training, creating potential problems. A governance framework is proposed for the ethical and successful merging of synthetic and authentic (measured) data in AI training. The framework covers technological, legal, and ethical issues to set norms and standards for synthetic data production, validation, and integration with measured data. Technically, the framework requires transparent disclosure of synthetic data proportion and characteristics, rigorous quality assurance mechanisms to validate synthetic data against real-world counterparts, and ongoing monitoring of AI models trained on fused data to detect and address biases or inconsistencies. It supports unambiguous synthetic data restrictions to comply with privacy and data protection legislation. Ethically, it emphasizes the need to overcome synthetic data biases and ensure that AI models trained on fused data do not perpetuate discrimination. By applying this governance framework, stakeholders may ensure the ethical integration of synthetic and measured data in AI training, increasing confidence in AI systems and reducing threats to individuals and society.
Artificial Intelligence (AI) systems can be classified into two functional modes according to Daniel Kahneman's dual-process theory: fast thinking (System 1) and slow thinking (System 2). AI's ability to think quickly allows it to carry out rapid, automated activities like identifying patterns and providing immediate answers, akin to human instincts. Slow thinking refers to using methodical and calculated methods, enabling AI to manage complicated decision-making and strategic planning effectively. This chapter investigates integrating cognitive processes in AI across several industries, such as healthcare, finance, and politics. It evaluates the effectiveness of this integration and explores the ethical considerations associated with it. This work seeks to deepen comprehension of AI's capabilities and guide its growth towards more advanced, dependable, and ethically sound implementations by investigating the dual-process theory in AI.
Governing artificial intelligence (AI) poses intricate difficulties, requiring a well-balanced approach involving local and global legal frameworks. local governance enables the implementation of customized legislation that caters to specific cultural, economic, and sociological requirements, thereby ensuring that AI technologies align with national interests and values. Global governance, in contrast, facilitates international collaboration, uniformity, and the control of risks that cross borders, creating a unified structure capable of tackling the global consequences of AI advancement and implementation. This chapter examines the need to combine local and global governance methods to establish a robust regulatory framework for AI. By balancing local and global perspectives, we can ensure the responsible, ethical, and efficient development and utilization of AI technology, benefiting individual states and the international community. Implementing this balanced governance model is vital in navigating the rapidly changing AI landscape, fostering innovation, and protecting the public's interests worldwide.
The fast advancement in artificial intelligence and machine learning has affected
economic factors in financial institutions and laws. Artificial intelligence has
improved financial services including smart advice, lending, monitoring systems,
and customer assistance, but it has also created concerns and obstacles. This
document summarizes financial AI and machine learning research, its applications,
and its impacts. The study showed how artificial intelligence affects the financial
industry in the country and the uses of Python libraries and applications related to
the financial economics aspect.
This paper explores the impact of machine learning (ML) algorithms on reducing information asymmetry in the labor market and forecasting social changes, presenting challenges and opportunities for employers and job seekers alike. It addresses the issues of information asymmetry stemming from disparities in data access between labor market participants, as well as variations in the quality of information obtained, complicating the hiring and job search processes. Theoretical foundations of big data and its analysis using ML are discussed for identifying trends and patterns conducive to more efficient labor market functioning. ML methods including predictive models, clustering, classification, and text analysis are presented, describing their application in reducing information asymmetry and adapting to changing market demands. Examples of ML algorithm usage in real business processes are provided, showcasing their contribution to optimizing recruitment and personnel management processes, as well as forecasting future trends regarding significant competencies and skills. The importance of adapting to new challenges and opportunities presented by ML algorithms for maintaining competitiveness and sustainable development in the labor market is emphasized. By leveraging big data analytics, employers and job seekers can make more informed decisions, leading to more efficient labor market outcomes. However, challenges such as data privacy and algorithmic bias must be addressed to fully realize the benefits of ML in the labor market.
In contemporary times, it has been noted that data has become highly valuable, akin to gold. The ability to successfully handle and monetize substantial amounts of data is a crucial characteristic of the contemporary world, necessitating human, technological, and infrastructural capabilities. These elements are becoming more and more critical in the generation of new wealth. However, what stands out is the evident discrepancy among countries that have a more significant portion of the technology sector.
As we conclude this book on the complex and constantly evolving connection between AI and the law, it is evident that we are at a critical point in the history of technology and legal principles (Leith, 1988; Becerra, 2018). Our exploration of this book has led us from the foundational ideas of AI to the intricate legal, ethical, and sociological dilemmas it presents. AI has been observed to be utilized by and subject to legal examination and control within the legal profession (Marwala, 2013, 2014).
Artificial intelligence (AI) has impacted many industries in the modern world, and the legal profession is no exception. Some of the areas that AI has impacted include modeling complex structures (Marwala in Finite-element-model updating using computational intelligence techniques: Applications to structural dynamics. Springer, 2010), condition monitoring of mechanical and electrical structures (Marwala, Condition monitoring using computational intelligence methods: applications in mechanical and electrical systems. Springer, 2012), predicting interstate conflict (Marwala and Lagazio in Militarized conflict modeling using computational intelligence. Springer, 2011) as well as understanding financial systems (Marwala in Economic modeling using artificial intelligence methods. Springer, 2013; Marwala and Hurwitz in Artificial intelligence and economic theory: Skynet in the market, Springer, 2017). The intersection of AI and the law alters how legal systems are used and understood, creating benefits and difficulties. This chapter analyzes the relationship between AI and the law, emphasizing its effects on legal practice, ethical issues, and potential regulatory ramifications.
Social protection refers to policies and programs implemented by governments to reduce poverty and vulnerability among citizens. The main objective is to create efficient labor markets, minimize exposure to risks, and enhance people’s capability to protect themselves against hazards and income interruption or loss (Dixon, J.E., 1999. Social Security in Global Perspective. Connecticut, US: Greenwood.). Social security, on the other hand, is the protection provided by society through public measures against economic and social distress that could result from sickness, maternity, employment injury, unemployment, invalidity, old age, death, medical care provision, and subsidies for families with children (International Labour Organization, International Labour Office, World Labour Report 2000: Income Security and Social Protection in a Changing World, International Labour Organization, 2000).
Access to justice, a fundamental principle of the rule of law, is critical for operating a fair and unbiased legal system. It ensures that communities and individuals have the necessary means to seek legal redress, defend their rights, and participate in the judicial system. Nevertheless, numerous regions face obstacles in pursuing justice, including geographical barriers, inadequate legal resources, and exorbitant fees.
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 ground for the chapters that follow. This is followed by Chapter 3, which examines the technologies of the fourth industrial revolution. This chapter is crucial as it lays the background for understanding what these technologies are, their potential capabilities and the beginning of the experimentation on how some of these technologies could be utilised in the enterprise risk management setting. Furthermore, Chapter 5 of the book looks at the concept of enterprise risk management, whereas Chapter 6 examines the information processing steps and the new capabilities in the enterprise risk setting owing to the capabilities of the fourth industrial revolution technologies to harness, analyse, and integrate information for decision-making and understanding internal and external contexts. In addition, Chapter 7 conceptualises enterprise risk management in the fourth industrial revolution, whilst Chapter 8 maps out the potential role changes in enterprise risk management as a result of the fourth industrial revolution, and Chapter 9 provides the synopsis of enterprise risk management in the fourth industrial revolution.
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, Robotics and autonomous robots, Quantum computing, Biotechnology, Advanced materials such as graphene, Blockchain technologies, Augmented reality and Virtual reality, System integration, Cyber security, Simulation, and Digital twin. The greatest strengths that have been identified in the fourth industrial revolution are the ability of technologies to reinforce each other, and their integration/ interconnectedness. For the purposes of the treatise, technologies deemed to be crucial in the entreprise risk management process include supervised, unsupervised, semi-supervised, and reinforcement machine learning, natural language processing, robotic process automation, augmented reality/simulation, and big data analytics. Reinforcement and the ability to integrate are thought to have potential of improving the entreprise risk management process, making decision-making far much quicker, enhanced, and more informed for the stakeholders in the risk management process.
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 manufacturing supply chains. Other key technologies of the fourth industrial revolution include artificial intelligence, robotics, the Internet of Things (IoT), 3D printing, genetic engineering, and quantum computing. Of interest is that many countries have moved quickly to develop national 4IR strategies for the purpose of taking advantage of advances in technologies. The two leading countries are the People’s Republic of China (PRC) and the United States of America (USA). The PRC and the US appear to be following two distinct approaches to 4IR which could be broadly categorised as a market led approach and a state backed approach. The PRC does not look at 4IR as having to do only with competition between companies, but it seems to have a geopolitical approach to this; for instance, they see it as a catalysts which will determine the country that will lead the next revolution. The US seems to have some advantages over the PRC when it comes to the fourth industrial revolution, but for how long this advantage will persist remains a subject for further research. There are, however, concerns in Washington that the USA’s resilience, competitivenes, and security have weakened. To tame the weaknesses, and to ensure that the United States remains a leader in this revolution, suggestions are that organisational and institutional structures need to be adjusted. One of the key advantages for the United States in the fourth industrial revolution continues to be its huge culture of entrepreneurship.
This article explores challenges in developing a Fourth Industrial Revolution (4IR) health strategy in South Africa against the background of the country’s ailing public health care system, on which around 80% of the population is dependent. This situation presents a problem of inequities in access to health care which, if not addressed, may be made worse as technological innovations increase. As such, a deliberate approach regarding 4IR is essential. The article is a desk research that utilises a qualitative approach by collecting and analysing data from various secondary sources in both published and unpublished literature. It considers theoretical literature on policy formulation, design and tools, as well as literature on the challenges of the country’s health system. The article finds that while there is a need for a 4IR health-focused policy, the South African state of health makes it a challenge to do so. Access to and implementation of the 4IR in the health sector need greater engagement with the problems in health care, as opposed to the implementation in a developed world. Further, until the NHI is fully implemented, a 4IR health strategy may not only be difficult to formulate, but to implement as well. The article contributes to the scholarship on policy formulation in South Africa and identifies the key features of what a South African health-focused 4IR strategy may contain. It further helps us to appreciate the challenges that confront an African health care system, particularly in view of the 4IR.
This paper presents an exhaustive investigation into the potential of integrating blockchain and Artificial Intelligence (AI) technologies within aerospace engineering, explicitly emphasizing supply chain management and operational efficiency. Given the decentralized nature of blockchain, it has the potential to enhance diverse facets of an aircraft’s lifecycle management significantly. At the same time, AI stands to revolutionize predictive supply chain models and structural fault detection. This paper provides a comprehensive overview of the current state, potential applications, challenges, and future research directions in this field based on an analysis of previous relevant literature. Further, it compares blockchain technology against traditional record management systems, underlining its data storage, security, transparency, and traceability advantages. Although these technologies promise significant advancements, many legal, regulatory, and technological readiness issues need addressing for broader acceptance within the industry. The findings highlight the importance of targeted research and development to unfold an array of new applications, driving innovation in aerospace engineering. This paper serves as a comprehensive survey for researchers, practitioners, policymakers, and industry stakeholders, illustrating the transformative potential of AI and blockchain in the aerospace sector.
Artificial intelligence (AI) has recently emerged as a potent force for change, touching every business and every aspect of modern life. AI’s immense powers are drastically transforming international politics, decision-making and security. Because AI can swiftly analyze massive volumes of data and discover patterns, it opens up new opportunities for more effective policy creation, sophisticated diplomatic conversations, and preemptive threat detection. However, bringing AI into international politics raises challenges and moral concerns like accountability, algorithmic bias, and data privacy. Therefore, understanding the implications of AI in international politics to develop a collaborative mindset is critical as governments navigate this new frontier to achieve responsible, equitable, and secure outcomes for all stakeholders.KeywordsArtificial intelligenceNeural networksEvolutionary programmingDeep learningFuzzy logic
The subject of this study is researching the challenges of artificial general intelligence systems-systems that can independently solve problems from different domains of human life. The purpose of this review monographic research is to explore the nature, application and risks of current artificial narrow intelligence systems and the possibility of their evolution into solutions with general intelligence. The main research thesis of the paper is that despite the undeniable evolutionary development of artificial intelligence technology since the beginning of the twentieth century, the implementation of artificial general intelligence systems has not yet been proven possible and should be sought in a long-term time range.
Though considerably more recent than realism, liberalism has a pedigree which stretches centuries and remains as relevant today as ever. Initially a set of descriptions of the ideal society, it has—in the post-Westphalian world—evolved into a complex explanation of world politics. In its wake and under the influence of liberal-leaning statesmen and stateswomen, lasting institutions and processes of global cooperation have been established. Moreover, unlike its rival paradigm of realism, liberalism is able to offer explanations of our modern world in which non-state actors are shaping global politics.
The ability to find the right price recommendation will determine the fate of product sales in the market. This is necessary to prevent whey concentrate products from being sold in the market and to avoid customers fleeing or switching to other competitors. This study uses a price intelligence approach using the k-means clustering method for price grouping based on the closest competitor and demand forecasting using linear regression to determine fair and competitive prices. The results of the k-means clustering price of 145000 from dk nutritionindo are included in C4. The closest competitor has 7 prices cheaper and 5 prices more expensive. The highest price is 495000 and the lowest price is 90000. The results of the 26th month to 33rd month demand forecasting have 2 graphs up and 6 graphs down. Forecasting confusion matrix test produces 62.5% accuracy, 75% precision, 60% recall. With MAPE = 28.95% according to Lewis (1982) then the influence of forecasting is declared feasible (good enough). Because the trend chart illustrates a decline, it is recommended that the shop lowers the price with a recommended price range from 135000 to 90000.
The ability to find the right price recommendation will determine the fate of product sales in the market. This is necessary to prevent whey concentrate products from being sold in the market and to avoid customers fleeing or switching to other competitors. This study uses a price intelligence approach using the k-means clustering method for price grouping based on the closest competitor and demand forecasting using linear regression to determine fair and competitive prices. The results of the k-means clustering price of 145000 from dk nutritionindo are included in C4. The closest competitor has 7 prices cheaper and 5 prices more expensive. The highest price is 495000 and the lowest price is 90000. The results of the 26th month to 33rd month demand forecasting have 2 graphs up and 6 graphs down. Forecasting confusion matrix test produces 62.5% accuracy, 75% precision, 60% recall. With MAPE = 28.95% according to Lewis (1982) then the influence of forecasting is declared feasible (good enough). Because the trend chart illustrates a decline, it is recommended that the shop lowers the price with a recommended price range from 135000 to 90000.
Today, artificial intelligence (AI) is becoming increasingly important in both industry and academics. To investigate AI in marketing, we have used bibliometric study, social network analysis (SNA), main path analysis, and content analysis to examine the top 10 authors, top 20 most cited articles, and top 11 milestone papers from our 628 articles sample. Bibliometric study identified leading authors, documents, universities, countries, and sources of these articles. By using SNA, we spotted an academic social network of crucial publications. Moreover, we recognized eleven milestone articles that constitute the main knowledge flow in AI marketing through main path analysis. Finally, we discussed future directions based on our findings. Our study is one among a few studies that have used bibliometric analysis methods to analyze and visualize the citation network of the AI-marketing interface.
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