arXiv subjects

Tshilidzi Marwala

Publications and source records attributed to Tshilidzi Marwala.

At least 37 records · Page 2Linked to original sources

arXiv2017

Rational Choice and Artificial Intelligence

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 choices available to choose an optimal choice. This paper investigates what happens when decisions are made by artificially intelligent machines in the market rather than human beings. Firstly, the expectations of the future are more consistent if they are made by an artificially intelligent machine and the decisions are more rational and thus marketplace becomes more rational.

cs.AI↗

arXiv2017

Artificial Intelligence and Economic Theories

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 such as the swarming of birds, the working of the brain and the pathfinding of the ants. These techniques have impact on economic theories. This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied. The theories that are considered are: demand and supply, asymmetrical information, pricing, rational choice, rational expectation, game theory, efficient market hypotheses, mechanism design, prospect, bounded rationality, portfolio theory, rational counterfactual and causality. The benefit of this book is that it evaluates existing theories of economics and update them based on the developments in artificial intelligence field.

cs.AI↗

arXiv2017

Implications of the Fourth Industrial Age on Higher Education

Higher education in the fourth industrial revolution, HE 4.0, is a complex, dialectical and exciting opportunity which can potentially transform society for the better. The fourth industrial revolution is powered by artificial intelligence and it will transform the workplace from tasks based characteristics to the human centred characteristics. Because of the convergence of man and machine, it will reduce the subject distance between humanities and social science as well as science and technology. This will necessarily require much more interdisciplinary teaching, research and innovation. This paper explores the impact of HE 4.0 on the mission of a university which is teaching, research (including innovation) and service.

cs.CY↗

arXiv2016

Missing Data Estimation in High-Dimensional Datasets: A Swarm Intelligence-Deep Neural Network Approach

In this paper, we examine the problem of missing data in high-dimensional datasets by taking into consideration the Missing Completely at Random and Missing at Random mechanisms, as well as theArbitrary missing pattern. Additionally, this paper employs a methodology based on Deep Learning and Swarm Intelligence algorithms in order to provide reliable estimates for missing data. The deep learning technique is used to extract features from the input data via an unsupervised learning approach by modeling the data distribution based on the input. This deep learning technique is then used as part of the objective function for the swarm intelligence technique in order to estimate the missing data after a supervised fine-tuning phase by minimizing an error function based on the interrelationship and correlation between features in the dataset. The investigated methodology in this paper therefore has longer running times, however, the promising potential outcomes justify the trade-off. Also, basic knowledge of statistics is presumed.

cs.AI↗

arXiv2015

Proposition of a Theoretical Model for Missing Data Imputation using Deep Learning and Evolutionary Algorithms

In the last couple of decades, there has been major advancements in the domain of missing data imputation. The techniques in the domain include amongst others: Expectation Maximization, Neural Networks with Evolutionary Algorithms or optimization techniques and K-Nearest Neighbor approaches to solve the problem. The presence of missing data entries in databases render the tasks of decision-making and data analysis nontrivial. As a result this area has attracted a lot of research interest with the aim being to yield accurate and time efficient and sensitive missing data imputation techniques especially when time sensitive applications are concerned like power plants and winding processes. In this article, considering arbitrary and monotone missing data patterns, we hypothesize that the use of deep neural networks built using autoencoders and denoising autoencoders in conjunction with genetic algorithms, swarm intelligence and maximum likelihood estimator methods as novel data imputation techniques will lead to better imputed values than existing techniques. Also considered are the missing at random, missing completely at random and missing not at random missing data mechanisms. We also intend to use fuzzy logic in tandem with deep neural networks to perform the missing data imputation tasks, as well as different building blocks for the deep neural networks like Stacked Restricted Boltzmann Machines and Deep Belief Networks to test our hypothesis. The motivation behind this article is the need for missing data imputation techniques that lead to better imputed values than existing methods with higher accuracies and lower errors.

cs.NE↗

arXiv2015

Monte Carlo Dynamically Weighted Importance Sampling For Finite Element Model Updating

The Finite Element Method (FEM) is generally unable to accurately predict natural frequencies and mode shapes of structures (eigenvalues and eigenvectors). Engineers develop numerical methods and a variety of techniques to compensate for this misalignment of modal properties, between experimentally measured data and the computed result from the FEM of structures. In this paper we compare two indirect methods of updating namely, the Adaptive Metropolis Hastings and a newly applied algorithm called Monte Carlo Dynamically Weighted Importance Sampling (MCDWIS). The approximation of a posterior predictive distribution is based on Bayesian inference of continuous multivariate Gaussian probability density functions, defining the variability of physical properties affected by forced vibration. The motivation behind applying MCDWIS is in the complexity of computing normalizing constants in higher dimensional or multimodal systems. The MCDWIS accounts for this intractability by analytically computing importance sampling estimates at each time step of the algorithm. In addition, a dynamic weighting step with an Adaptive Pruned Enriched Population Control Scheme (APEPCS) allows for further control over weighted samples and population size. The performance of the MCDWIS simulation is graphically illustrated for all algorithm dependent parameters and show unbiased, stable sample estimates.

math.NA↗

arXiv2015

Artificial Intelligence and Asymmetric Information Theory

When human agents come together to make decisions, it is often the case that one human agent has more information than the other. This phenomenon is called information asymmetry and this distorts the market. Often if one human agent intends to manipulate a decision in its favor the human agent can signal wrong or right information. Alternatively, one human agent can screen for information to reduce the impact of asymmetric information on decisions. With the advent of artificial intelligence, signaling and screening have been made easier. This paper studies the impact of artificial intelligence on the theory of asymmetric information. It is surmised that artificial intelligent agents reduce the degree of information asymmetry and thus the market where these agents are deployed become more efficient. It is also postulated that the more artificial intelligent agents there are deployed in the market the less is the volume of trades in the market. This is because for many trades to happen the asymmetry of information on goods and services to be traded should exist, creating a sense of arbitrage.

cs.AI↗

arXiv2015

Causal Model Analysis using Collider v-structure with Negative Percentage Mapping

A major problem of causal inference is the arrangement of dependent nodes in a directed acyclic graph (DAG) with path coefficients and observed confounders. Path coefficients do not provide the units to measure the strength of information flowing from one node to the other. Here we proposed the method of causal structure learning using collider v-structures (CVS) with Negative Percentage Mapping (NPM) to get selective thresholds of information strength, to direct the edges and subjective confounders in a DAG. The NPM is used to scale the strength of information passed through nodes in units of percentage from interval from 0 to 1. The causal structures are constructed by bottom up approach using path coefficients, causal directions and confounders, derived implementing collider v-structure and NPM. The method is self-sufficient to observe all the latent confounders present in the causal model and capable of detecting every responsible causal direction. The results are tested for simulated datasets of non-Gaussian distributions and compared with DirectLiNGAM and ICA-LiNGAM to check efficiency of the proposed method.

cs.AI↗

arXiv2015

Impact of Artificial Intelligence on Economic Theory

Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.

q-fin.GN↗

arXiv2014

Rational Counterfactuals

This paper introduces the concept of rational countefactuals which is an idea of identifying a counterfactual from the factual (whether perceived or real) that maximizes the attainment of the desired consequent. In counterfactual thinking if we have a factual statement like: Saddam Hussein invaded Kuwait and consequently George Bush declared war on Iraq then its counterfactuals is: If Saddam Hussein did not invade Kuwait then George Bush would not have declared war on Iraq. The theory of rational counterfactuals is applied to identify the antecedent that gives the desired consequent necessary for rational decision making. The rational countefactual theory is applied to identify the values of variables Allies, Contingency, Distance, Major Power, Capability, Democracy, as well as Economic Interdependency that gives the desired consequent Peace.

cs.AI↗

arXiv2013

Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification

In this paper, we propose a new methodology based on the Negative Selection Algorithm that belongs to the field of Computational Intelligence, specifically, Artificial Immune Systems to identify takeover targets. Although considerable research based on customary statistical techniques and some contemporary Computational Intelligence techniques have been devoted to identify takeover targets, most of the existing studies are based upon multiple previous mergers and acquisitions. Contrary to previous research, the novelty of this proposal lies in its ability to suggest takeover targets for novice firms that are at the beginning of their merger and acquisition spree. We first discuss the theoretical perspective and then provide a case study with details for practical implementation, both capitalizing from unique generalization capabilities of artificial immune systems algorithms.

cs.AI↗

arXiv2013

Flexibly-bounded Rationality and Marginalization of Irrationality Theories for Decision Making

In this paper the theory of flexibly-bounded rationality which is an extension to the theory of bounded rationality is revisited. 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 to make decisions. In bounded rationality, this decision is made irrespective of the fact that the information to be used is incomplete and imperfect and that the human brain is inconsistent and thus this decision that is to be made is taken within the bounds of these limitations. In the theory of flexibly-bounded rationality, advanced information analysis is used, the correlation machine is applied to complete missing information and artificial intelligence is used to make more consistent decisions. Therefore flexibly-bounded rationality expands the bounds within which rationality is exercised. Because human decision making is essentially irrational, this paper proposes the theory of marginalization of irrationality in decision making to deal with the problem of satisficing in the presence of irrationality.

cs.AI↗

arXiv2013

Semi-bounded Rationality: A model for decision making

In this paper the theory of semi-bounded rationality is proposed as an extension of the theory of bounded rationality. In particular, it is proposed that a decision making process involves two components and these are the correlation machine, which estimates missing values, and the causal machine, which relates the cause to the effect. Rational decision making involves using information which is almost always imperfect and incomplete as well as some intelligent machine which if it is a human being is inconsistent to make decisions. In the theory of bounded rationality this decision is made irrespective of the fact that the information to be used is incomplete and imperfect and the human brain is inconsistent and thus this decision that is to be made is taken within the bounds of these limitations. In the theory of semi-bounded rationality, signal processing is used to filter noise and outliers in the information and the correlation machine is applied to complete the missing information and artificial intelligence is used to make more consistent decisions.

cs.AI↗

arXiv2012

Soft Computing in Product Recovery: A Survey Focusing on Remanufacturing System

This paper focuses on the application of soft computing in remanufacturing system, in which end-of-life products are disassembled into basic components and then remanufactured for both economic and environmental reasons. The disassembly activities include disassembly sequencing and planning, while the remanufacturing process is composed of product design, production planning & scheduling, and inventory management. This paper presents a review of the related articles and suggests the corresponding further research directions.

cs.OH↗

arXiv2011

Organizational adaptation to Complexity: A study of the South African Insurance Market as a Complex Adaptive System through Statistical Risk Analysis

South Africa assumes a significant position in the insurance landscape of Africa. The present research based upon qualitative and quantitative analysis, shows that it shows the characteristics of a Complex Adaptive System. In addition, a statistical analysis of risk measures through Value at risk and Conditional tail expectation is carried out to show how an individual insurance company copes under external complexities. The authors believe that an explanation of the coping strategies, and the subsequent managerial implications would enrich our understanding of complexity in business.

cs.CY↗

arXiv2011

Suitability of using technical indicators as potential strategies within intelligent trading systems

The potential of machine learning to automate and control nonlinear, complex systems is well established. These same techniques have always presented potential for use in the investment arena, specifically for the managing of equity portfolios. In this paper, the opportunity for such exploitation is investigated through analysis of potential simple trading strategies that can then be meshed together for the machine learning system to switch between. It is the eligibility of these strategies that is being investigated in this paper, rather than application. In order to accomplish this, the underlying assumptions of each trading system are explored, and data is created in order to evaluate the efficacy of these systems when trading on data with the underlying patterns that they expect. The strategies are tested against a buy-and-hold strategy to determine if the act of trading has actually produced any worthwhile results, or are simply facets of the underlying prices. These results are then used to produce targeted returns based upon either a desired return or a desired risk, as both are required within the portfolio-management industry. Results show a very viable opportunity for exploitation within the aforementioned industry, with the Strategies performing well within their narrow assumptions, and the intelligent system combining them to perform without assumptions.

q-fin.PM↗

arXiv2011

Fuzzy Inference Systems Optimization

This paper compares various optimization methods for fuzzy inference system optimization. The optimization methods compared are genetic algorithm, particle swarm optimization and simulated annealing. When these techniques were implemented it was observed that the performance of each technique within the fuzzy inference system classification was context dependent.

cs.AI↗

arXiv2011

The fuzzy gene filter: A classifier performance assesment

The Fuzzy Gene Filter (FGF) is an optimised Fuzzy Inference System designed to rank genes in order of differential expression, based on expression data generated in a microarray experiment. This paper examines the effectiveness of the FGF for feature selection using various classification architectures. The FGF is compared to three of the most common gene ranking algorithms: t-test, Wilcoxon test and ROC curve analysis. Four classification schemes are used to compare the performance of the FGF vis-a-vis the standard approaches: K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and Artificial Neural Network (ANN). A nested stratified Leave-One-Out Cross Validation scheme is used to identify the optimal number top ranking genes, as well as the optimal classifier parameters. Two microarray data sets are used for the comparison: a prostate cancer data set and a lymphoma data set.

cs.LG↗