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Artificial Intelligence and Economic Theories: Skynet in the market

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.

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Artificial Intelligence and Economic Theories: Skynet in the market

  1. 1. Artificial Intelligence and Economic Theory: Skynet in the Market 24 August 2017, Professor Tshilidzi Marwala and Dr. Evan Hurwitz
  2. 2. 2 Skynet in the Market Professor Tshilidzi Marwala l University of Johannesburg
  3. 3. 3Professor Tshilidzi Marwala l University of Johannesburg
  4. 4. 4 KAIST Robot Professor Tshilidzi Marwala l University of Johannesburg
  5. 5. 5 Human–Robot Interaction Professor Tshilidzi Marwala l University of Johannesburg
  6. 6. 6Professor Tshilidzi Marwala l University of Johannesburg What is Artificial Intelligence (AI)
  7. 7. 7Professor Tshilidzi Marwala l University of Johannesburg
  8. 8. 8 Fundamentals of AI Professor Tshilidzi Marwala l University of Johannesburg Artificial Intelligence is concerned with modeling complex systems with computational tools. Artificial: man-made. Intelligence: the ability to make sense of information beyond the obvious. Building machines that are able to do sophisticated tasks that are at least in the domain of human capabilities. A machine is intelligent if when humans interact with it, they are not able to know if they are interacting with a human being or a machine. (This is a Turing test) Algorithms are developed to mimic physiological systems such as the human brain in order to carry out certain tasks. Mathematical models are used to build systems that exhibit advanced intelligence in carrying out goals.
  9. 9. 9 Individual versus Group Intelligence Professor Tshilidzi Marwala l University of Johannesburg Two types of intelligence: Individual versus group intelligence. Individual intelligence: A single agent (single brain) learns on its own. Group intelligence: Multiple agents (many brains) learn together.
  10. 10. 10Professor Tshilidzi Marwala l University of Johannesburg Learning
  11. 11. 11 Neural Networks Professor Tshilidzi Marwala l University of Johannesburg A neural network is an information-processing paradigm that is inspired by the way biological nervous systems, like the human brain, process information.
  12. 12. 12Professor Tshilidzi Marwala l University of Johannesburg Optimization
  13. 13. 13 Ant Colony Optimization Professor Tshilidzi Marwala l University of Johannesburg Ant movement leaves a trail of pheromones on the way from one point to another. More and more of these pheromones are deposited because of the increased traffic. This method has found use in applications such as scheduling. From the Eugene Marais book Die Siel van die Mier. The path with the strongest pheromones then is the shortest path between one point to another.
  14. 14. 14 Particle Swarm Optimization: Invisible Hand Professor Tshilidzi Marwala l University of Johannesburg Each bird updates its position and velocity based on: Local best known position (individual intelligence) Global best known position (group intelligence) • Adam Smith Invisible Hand • Utilitarianism: John Stuart Mill • Maximization of Utility Function
  15. 15. 15Professor Tshilidzi Marwala l University of Johannesburg AI and Economic Theories
  16. 16. 16 Supply and Demand as well as Pricing Professor Tshilidzi Marwala l University of Johannesburg • AI through learning and evolution is able to ensure that the demand and supply curves are better modelled. • 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. • Artificial intelligence based platforms such as Amazon allows for individualized demand curves. • Flexible manufacturing and 3-D printing allows for individualized supply curves.
  17. 17. 17 Rational Expectations and Choice Professor Tshilidzi Marwala l University of Johannesburg • Rational expectations and rational choice and how they are changed through the advances of artificial intelligence. • It proposes a framework of rational choice which is based on the theory of rational expectations to create a causal model between the input and output using neural networks and optimization to change the input to obtain the desired output. • The theory of rational choice and AI were used to maximize the attainment of peace.
  18. 18. 18 Bounded Rationality Professor Tshilidzi Marwala l University of Johannesburg • The concept of that the bounds in the theory of bounded rationality are variable. • Advanced signal processing, missing data estimation methods and artificial intelligence techniques change the principle of rationality. • Flexibly-bounded rationality expands the bounds within which a rational decision making process can be exercised and, thereby, increases the probability of making accurate decisions when compared to the theory of bounded rationality.
  19. 19. 19 Prospect Theory Professor Tshilidzi Marwala l University of Johannesburg • System 1 is fast, intuitive and emotional whereas System 2 is slow, rational and calculating. • With the advent of AI machines, all these effects and biases are eliminated. System 1, which is itutitive is eliminated altogether. • System 2 becomes the norm as advances in AI are made. • System 2, becomes fast ( contemporary computational machines work fast. • With Moore’s Law, System 2 next year is faster than System 2 this year thus making machines “Think Fast and Faster”.
  20. 20. 20 Information Asymmetry Professor Tshilidzi Marwala l University of Johannesburg • Two used car salesmen one sells good cars another bad cars but the customers don’t know. • The salesman who sells bad cars is willing to give discount. • Customers don’t know which cars are good or bad. This drives the good car salesman out of the market. • This is called information asymmetry and (Stiglitz et al – Nobel Prize) substituting customers by artificial intelligent machines which crawl the internet for information (two salesmen). • This reduces information asymmetry.
  21. 21. 21 Information Asymmetry Professor Tshilidzi Marwala l University of Johannesburg • The degree of asymmetry of information between two artificial intelligent agents is less than that between two human agents. • The more artificial intelligence there is in the market, the less is the volume of trades in the market, and the overall efficiency of the market is likely to improve over time as the market becomes more saturated with intelligent trading and analysis agents.
  22. 22. 22 Game Theory Professor Tshilidzi Marwala l University of Johannesburg • In game theory agents with rules interact to obtain pay-off at some equilibrium point often called Nash equilibrium. • The advent of AI makes the multi-agent game theory much more effective. • Intelligent multi-agent systems were used to study a game of Lerpa.
  23. 23. 23 Efficient Market Hypothesis Professor Tshilidzi Marwala l University of Johannesburg • Impact of AI on the efficient market hypothesis. • Concepts that influence market efficiency are changed by AI machines and this impact on market efficiency. • Advances in AI make markets more efficient.
  24. 24. 24 Mechanism Design Professor Tshilidzi Marwala l University of Johannesburg • In game theory, players have rules and pay-off and they interact until some point of equilibrium is achieved. • Mechanism design is the reverse game theory which can be used to design sets of rules which give rise to a particular outcome. • The implications of AI on the theory of mechanism design were also outlined. • Examples on how mechanism design has been applied to the creation of a kidney exchange market was discussed.
  25. 25. 25 Portfolio Theory Professor Tshilidzi Marwala l University of Johannesburg • Creating a dynamic portfilio that adapts to the changing environment is a natural extension of Markowitz’s portfolio theory. • It is appropriate when decisions are being made by intelligent machines which are enabled by neural networks. • Adaptation to the environment is enabled by evolutionary optimization e.g. genetic algorithm. • AI is used to create an automated portfolio optimization technique that is able to adapt to the environment by learning from the data to improve its performance.
  26. 26. 26 Financial Engineering Professor Tshilidzi Marwala l University of Johannesburg • Financial engineering has grown with the advent of computing and this growth has accelerated in the last decade with the advances in 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. • It is a good basis for building models that target inflation and enhances the identification of factors that drive inflation.
  27. 27. 27 Counterfactuals Professor Tshilidzi Marwala l University of Johannesburg • 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 its counterfactual is: ‘If Greece was financially prudent and consequently its finances are in good shape’. • To build rational counterfactuals, AI techniques are applied. • The example considered uses AI to create counterfactuals that maximizes the attainment of a targetted interest rate.
  28. 28. 28 Causality Professor Tshilidzi Marwala l University of Johannesburg • 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. • Links between circumcision and risk of HIV are used in this chapter.
  29. 29. 29 Lewis Development Theory Professor Tshilidzi Marwala l University of Johannesburg • In a country there are people located in the subsistence economy and the second economy and in the formal advanced economy the first economy. • The first economy can then absorb from the second economy, without the need to increase the wages of the first economy. • The profits in the first economy obtained from the introduction of labor from the second economy is used to improve the productive forces of the first economy. • This is repeated until it is no longer economical to migrate labor from the second economy to the first economy and the only way the first economy can expand capital is by increasing the cost of labor in the first economy and this point is called the Lewis turning point. • Arthur Lewis theory can be used to understand the development of the economy into the fourth industrial revolution where absolutely everything in the economy will be automated by artificial intelligent agents and the other related technologies. • In this scenario, a point of equilibrium is when it does not make economic sense for labor to move from humans to machines.
  30. 30. 30 AI can make Rational Decisions Professor Tshilidzi Marwala l University of Johannesburg
  31. 31. 31Professor Tshilidzi Marwala l University of Johannesburg AI and the Fourth Industrial Revolution
  32. 32. 32 Internet of Things: AI can monitor the Condition of Structures Professor Tshilidzi Marwala l University of Johannesburg
  33. 33. 33 Data Economy: AI can estimate Missing Data Professor Tshilidzi Marwala l University of Johannesburg
  34. 34. 34 Cyber-Physical Systems: AI can improve Finite Element Models Professor Tshilidzi Marwala l University of Johannesburg
  35. 35. 35 Sustainable Development Goals: AI can predict Interstate Conflict Professor Tshilidzi Marwala l University of Johannesburg
  36. 36. Thank You Siyabonga

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