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  1. Book ; Online: Relative rationality

    Marwala, Tshilidzi

    Is machine rationality subjective?

    2019  

    Abstract: Rational decision making in its linguistic description means making logical decisions. In essence, a rational agent optimally processes all relevant information to achieve its goal. Rationality has two elements and these are the use of relevant ... ...

    Abstract Rational decision making in its linguistic description means making logical decisions. In essence, a rational agent optimally processes all relevant information to achieve its goal. Rationality has two elements and these are the use of relevant information and the efficient processing of such information. In reality, relevant information is incomplete, imperfect and the processing engine, which is a brain for humans, is suboptimal. Humans are risk averse rather than utility maximizers. In the real world, problems are predominantly non-convex and this makes the idea of rational decision-making fundamentally unachievable and Herbert Simon called this bounded rationality. There is a trade-off between the amount of information used for decision-making and the complexity of the decision model used. This explores whether machine rationality is subjective and concludes that indeed it is.
    Keywords Computer Science - Artificial Intelligence
    Publishing date 2019-02-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Bayesian inference of COVID-19 spreading rates in South Africa.

    Mbuvha, Rendani / Marwala, Tshilidzi

    PloS one

    2020  Volume 15, Issue 8, Page(s) e0237126

    Abstract: The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for performing accurate inference with limited data. Fundamental to the design of rapid state responses is the ability to perform epidemiological model ... ...

    Abstract The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for performing accurate inference with limited data. Fundamental to the design of rapid state responses is the ability to perform epidemiological model parameter inference for localised trajectory predictions. In this work, we perform Bayesian parameter inference using Markov Chain Monte Carlo (MCMC) methods on the Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological models with time-varying spreading rates for South Africa. The results find two change points in the spreading rate of COVID-19 in South Africa as inferred from the confirmed cases. The first change point coincides with state enactment of a travel ban and the resultant containment of imported infections. The second change point coincides with the start of a state-led mass screening and testing programme which has highlighted community-level disease spread that was not well represented in the initial largely traveller based and private laboratory dominated testing data. The results further suggest that due to the likely effect of the national lockdown, community level transmissions are slower than the original imported case driven spread of the disease.
    MeSH term(s) Algorithms ; Bayes Theorem ; COVID-19 ; Coronavirus Infections/diagnosis ; Coronavirus Infections/epidemiology ; Coronavirus Infections/transmission ; Coronavirus Infections/virology ; Humans ; Markov Chains ; Monte Carlo Method ; Pandemics ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/transmission ; Pneumonia, Viral/virology ; South Africa/epidemiology
    Keywords covid19
    Language English
    Publishing date 2020-08-05
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0237126
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Nano Version Control and Robots of Robots

    Machowski, Lukasz / Marwala, Tshilidzi

    Data Driven, Regenerative Production Code

    2021  

    Abstract: A reflection of the Corona pandemic highlights the need for more sustainable production systems using automation. The goal is to retain automation of repetitive tasks while allowing complex parts to come together. We recognize the fragility and how hard ... ...

    Abstract A reflection of the Corona pandemic highlights the need for more sustainable production systems using automation. The goal is to retain automation of repetitive tasks while allowing complex parts to come together. We recognize the fragility and how hard it is to create traditional automation. We introduce a method which converts one really hard problem of producing sustainable production code into three simpler problems being data, patterns and working prototypes. We use developer seniority as a metric to measure whether the proposed method is easier. By using agent-based simulation and NanoVC repos for agent arbitration, we are able to create a simulated environment where patterns developed by people are used to transform working prototypes into templates that data can be fed through to create the robots that create the production code. Having two layers of robots allow early implementation choices to be replaced as we gather more feedback from the working system. Several benefits of this approach have been discovered, with the most notable being that the Robot of Robots encodes a legacy of the person that designed it in the form of the 3 ingredients (data, patterns and working prototypes). This method allows us to achieve our goal of reducing the fragility of the production code while removing the difficulty of getting there.

    Comment: Presented at the 3rd Electrical Engineering Postgraduate Symposium
    Keywords Computer Science - Robotics ; Computer Science - Artificial Intelligence
    Subject code 629
    Publishing date 2021-10-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Bayesian inference of local government audit outcomes.

    Mongwe, Wilson Tsakane / Mbuvha, Rendani / Marwala, Tshilidzi

    PloS one

    2021  Volume 16, Issue 12, Page(s) e0261245

    Abstract: The scandals in publicly listed companies have highlighted the large losses that can result from financial statement fraud and weak corporate governance. Machine learning techniques have been applied to automatically detect financial statement fraud with ...

    Abstract The scandals in publicly listed companies have highlighted the large losses that can result from financial statement fraud and weak corporate governance. Machine learning techniques have been applied to automatically detect financial statement fraud with great success. This work presents the first application of a Bayesian inference approach to the problem of predicting the audit outcomes of financial statements of local government entities using financial ratios. Bayesian logistic regression (BLR) with automatic relevance determination (BLR-ARD) is applied to predict audit outcomes. The benefit of using BLR-ARD, instead of BLR without ARD, is that it allows one to automatically determine which input features are the most relevant for the task at hand, which is a critical aspect to consider when designing decision support systems. This work presents the first implementation of BLR-ARD trained with Separable Shadow Hamiltonian Hybrid Monte Carlo, No-U-Turn sampler, Metropolis Adjusted Langevin Algorithm and Metropolis-Hasting algorithms. Unlike the Gibbs sampling procedure that is typically employed in sampling from ARD models, in this work we jointly sample the parameters and the hyperparameters by putting a log normal prior on the hyperparameters. The analysis also shows that the repairs and maintenance as a percentage of total assets ratio, current ratio, debt to total operating revenue, net operating surplus margin and capital cost to total operating expenditure ratio are the important features when predicting local government audit outcomes using financial ratios. These results could be of use for auditors as focusing on these ratios could potentially speed up the detection of fraudulent behaviour in municipal entities, and improve the speed and quality of the overall audit.
    MeSH term(s) Algorithms ; Bayes Theorem ; Financial Audit/methods ; Financial Audit/standards ; Financial Audit/statistics & numerical data ; Fraud/economics ; Fraud/prevention & control ; Fraud/statistics & numerical data ; Humans ; Local Government ; Models, Statistical ; Monte Carlo Method
    Language English
    Publishing date 2021-12-14
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0261245
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Quantum-Inspired Magnetic Hamiltonian Monte Carlo.

    Mongwe, Wilson Tsakane / Mbuvha, Rendani / Marwala, Tshilidzi

    PloS one

    2021  Volume 16, Issue 10, Page(s) e0258277

    Abstract: Hamiltonian Monte Carlo (HMC) is a Markov Chain Monte Carlo algorithm that is able to generate distant proposals via the use of Hamiltonian dynamics, which are able to incorporate first-order gradient information about the target posterior. This has ... ...

    Abstract Hamiltonian Monte Carlo (HMC) is a Markov Chain Monte Carlo algorithm that is able to generate distant proposals via the use of Hamiltonian dynamics, which are able to incorporate first-order gradient information about the target posterior. This has driven its rise in popularity in the machine learning community in recent times. It has been shown that making use of the energy-time uncertainty relation from quantum mechanics, one can devise an extension to HMC by allowing the mass matrix to be random with a probability distribution instead of a fixed mass. Furthermore, Magnetic Hamiltonian Monte Carlo (MHMC) has been recently proposed as an extension to HMC and adds a magnetic field to HMC which results in non-canonical dynamics associated with the movement of a particle under a magnetic field. In this work, we utilise the non-canonical dynamics of MHMC while allowing the mass matrix to be random to create the Quantum-Inspired Magnetic Hamiltonian Monte Carlo (QIMHMC) algorithm, which is shown to converge to the correct steady state distribution. Empirical results on a broad class of target posterior distributions show that the proposed method produces better sampling performance than HMC, MHMC and HMC with a random mass matrix.
    MeSH term(s) Bayes Theorem ; Databases as Topic ; Magnetic Phenomena ; Monte Carlo Method ; Multivariate Analysis ; Quantum Theory ; Regression Analysis
    Language English
    Publishing date 2021-10-05
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0258277
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book: Causality, correlation and artificial intelligence for rational decision making

    Marwala, Tshilidzi

    2015  

    Author's details Tshilidzi Marwala (University of Johannesburg, South Africa)
    Keywords Artificial intelligence ; Decision making
    Language English
    Size XIII, 192 S., graph. Darst., Tab.
    Publisher World Scientific
    Publishing place Singapore u.a.
    Document type Book
    Note Literaturangaben
    ISBN 9789814630863 ; 9814630861
    Database Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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  7. Book: Causality, correlation and artificial intelligence for rational decision making

    Marwala, Tshilidzi

    2015  

    Author's details Tshilidzi Marwala (University of Johannesburg, South Africa)
    Keywords Artificial intelligence ; Decision making ; Künstliche Intelligenz ; Entscheidung ; Entscheidungstheorie
    Language English
    Size xiii, 192 Seiten, Illustrationen, Diagramme, 26 cm
    Publisher World Scientific
    Publishing place New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai
    Document type Book
    Note Literaturangaben
    ISBN 9789814630863 ; 9789814630870 ; 9814630861 ; 981463087X
    Database ECONomics Information System

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    Inter-library loan at ZB MED

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  8. Article ; Online: Bayesian inference of COVID-19 spreading rates in South Africa

    Mbuvha, Rendani / Marwala, Tshilidzi

    PLOS ONE

    2020  Volume 15, Issue 8, Page(s) e0237126

    Keywords General Biochemistry, Genetics and Molecular Biology ; General Agricultural and Biological Sciences ; General Medicine ; covid19
    Language English
    Publisher Public Library of Science (PLoS)
    Publishing country us
    Document type Article ; Online
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0237126
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  9. Article ; Online: Bayesian Inference of COVID-19 Spreading Rates in South Africa

    Mbuvha, Rendani / Marwala, Tshilidzi

    medRxiv

    Abstract: The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for the development of prompt mitigating responses under conditions of high uncertainty. Fundamental to the design of rapid state reactions is the ability ... ...

    Abstract The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for the development of prompt mitigating responses under conditions of high uncertainty. Fundamental to the design of rapid state reactions is the ability to perform epidemiological model parameter inference for localised trajectory predictions. In this work, we perform Bayesian parameter inference using Markov Chain Monte Carlo (MCMC) methods on the Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological models with time-varying spreading rates for South Africa. The results find two change points in the spreading rate of COVID-19 in South Africa as inferred from the confirmed cases. The first change point coincides with state enactment of a travel ban and the resultant containment of imported infections. The second change point coincides with the start of a state-led mass screening and testing programme which has highlighted community-level disease spread that was not well represented in the initial largely traveller based and private laboratory dominated testing data. The results further suggest that due to the likely effect of the national lockdown, community level transmissions are slower than the original imported case driven spread of the disease.
    Keywords covid19
    Language English
    Publishing date 2020-04-30
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.04.28.20083873
    Database COVID19

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  10. Article ; Online: On Data-Driven Management of the COVID-19 Outbreak in South Africa

    Mbuvha, Rendani / Marwala, Tshilidzi

    Abstract: The rapid spread of the novel coronavirus (SARS-CoV-2) has highlighted the need for the development of rapid mitigating responses under conditions of extreme uncertainty. While numerous works have provided projections of the progression of the pandemic, ... ...

    Abstract The rapid spread of the novel coronavirus (SARS-CoV-2) has highlighted the need for the development of rapid mitigating responses under conditions of extreme uncertainty. While numerous works have provided projections of the progression of the pandemic, very little work has been focused on its progression in Africa and South Africa, in particular. In this work, we calibrate the susceptible-infected-recovered (SIR) compartmental model to South African data using initial conditions inferred from progression in Hubei, China and Lombardy, Italy. The results suggest two plausible hypotheses - either the COVID-19 pandemic is still at very early stages of progression in South Africa or a combination of prompt mitigating measures, demographics and social factors have resulted in a slowdown in its spread and severity. We further propose pandemic monitoring and health system capacity metrics for assisting decision-makers in evaluating which of the two hypotheses is most probable.
    Keywords covid19
    Publisher MedRxiv; WHO
    Document type Article ; Online
    DOI 10.1101/2020.04.07.20057133
    Database COVID19

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