Risk Analysis and Risk Management for the Artificial Superintelligence Research and Development Process

Author(s):  
Anthony M. Barrett ◽  
Seth D. Baum
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Matthew Moorhead ◽  
Lynne Armitage ◽  
Martin Skitmore

PurposeThe purpose is to examine the risk management processes and methods used in determining project feasibility in the early stages of the property development process by Australia/New Zealand property developers, including Monte Carlo simulation, Bayesian models and real option theory embedded in long-term property development and investment decision-making as instruments for providing flexibility and managing risk, uncertainty and change.Design/methodology/approachA questionnaire survey of 225 Australian and New Zealand trader developers, development managers, investors, valuers, fund managers and government/charities/other relating to Australia/New Zealand property development companies' decision-making processes in the early stages of the development process prior to site acquisition or project commencement – the methods used and confidence in their organisations' ability to both identify and manage the risks involved.FindingsFew of the organisations sampled use sophisticated methods; those organisations that are more likely to use such methods for conducting risk analysis include development organisations that undertake large projects, use more risk analysis methods and have more layers in their project approval process. Decision-makers have a high level of confidence in their organisation's ability to both identify and manage the risks involved, although this is not mirrored in their actual risk management processes. Although the majority of property developers have a risk management plan, less than half have implemented it, and a third need improvement.Practical implicationsProperty development organisations should incorporate more modern and sophisticated models of risk analysis to determine the uncertainty of, and risk in, a change of input variables in their financial viability appraisals. Practical application includes using such multiple techniques as what-if scenarios and probability analysis into feasibility processes and utilise these specific techniques in the pre-acquisition stages of the property development process and, specifically, in the site acquisition process to support decision-making, including a live risk register and catalogue of risks, including identification of and plans for mitigation of project risks, as a form of risk management.Originality/valueFirst study to examine the extent of the decision-making methods used by property developers in the pre-acquisition stage of the development process.


2019 ◽  
Vol 16 (6) ◽  
pp. 60-77
Author(s):  
E. V. Vasilieva ◽  
T. V. Gaibova

This paper describes the method of project risk analysis based on design thinking and explores the possibility of its application for industrial investment projects. Traditional and suggested approaches to project risk management have been compared. Several risk analysis artifacts have been added to the standard list of artifacts. An iterative procedure for the formation of risk analysis artifacts has been developed, with the purpose of integrating the risk management process into strategic and prompt decision-making during project management. A list of tools at each stage of design thinking for risk management within the framework of real investment projects has been proposed. The suggested technology helps to determine project objectives and content and adapt them in regards to possible; as well as to implement measures aimed at reducing these risks, to increase productivity of the existing risk assessment and risk management tools, to organize effective cooperation between project team members, and to promote accumulation of knowledge about the project during its development and implementation.The authors declare no conflict of interest.


2021 ◽  
Vol 13 (4) ◽  
pp. 2034
Author(s):  
Chien-Liang Lin ◽  
Bey-Kun Chen

Risks inevitably exist in all stages of a project. In a construction project, which is highly dynamic and complex, risk factors affect the expected achievement rates of the three main performance goals, namely schedule, cost, and quality. A comprehensive risk management procedure requires three crucial steps: risk confirmation, analysis, and treatment. Risk analysis is the core of risk management. Through structural equation modeling, this study developed a risk analysis model that takes a different perspective and considered the occurrence probability of risk events and the extent to which these events affect a project. The contractor dimension was discovered to exert the strongest influence on an overall project, followed by the subcontractor and design dimensions. This paper proposes a novel construction project risk analysis model, which considers the entire project. The proposed model can be used as a reference for risk managers to make decisions about project risks, so as to achieve the ultimate goal of saving resources and the sustainable operation of the construction project.


Author(s):  
Анатолий Михайлович Лепихин ◽  
Николай Андреевич Махутов ◽  
Юрий Иванович Шокин ◽  
Андрей Васильевич Юрченко

Рассмотрены основные методологические аспекты анализа рисков технических систем с использованием цифровых двойников. Сформулирована концепция рисканализа и предложена базовая модель для ее реализации. Рассмотрены информационные аспекты анализа неопределенностей модели риска. Показано, что технологии цифровых двойников позволяют эффективно сочетать результаты компьютерного моделирования с данными мониторинга реальных объектов, обеспечивая более глубокий анализ объектов, с учетом множества вариантов конструкции, технологий и условий эксплуатации Development of technology and technical systems significantly increases in the volume of information. Traditional methods for designing, manufacturing and operating of technical systems do not allow processing such volumes of information. In this regard, the modern strategy for creating technical systems is based on the use of digital twins. Solving the problems of risk analysis and risk management for technical systems at all stages of the life cycle appears to be one of the promising areas for application of the digital twins technology. Despite of active research, using digital twins in risk analysis currently do not have appropriate methodological justifications and technical solutions in a number of key aspects. In particular, effective reductions of the order of risk models and quantifying uncertainty factors of various types have not been solved. The concept of the risk-informed decision making in product lifecycle management has not been implemented. In fact, there are very few publications on the risk analysis and risk management methodology using digital twins. The article discusses the main methodological aspects of risk analysis of technical systems using digital twins. The concept of risk analysis is formulated and a basic model for its implementation is proposed. The informational aspects of the analysis of uncertainties of the risk model are considered. It is shown that digital twin technologies allow effective combination of the results of computer modelling with the data monitoring of real objects, providing a deeper analysis of objects, taking into account a variety of design options, technologies and operating conditions.


2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Jia Liu ◽  
Shiyong Li ◽  
Xiaoxia Zhu

In recent years, internet development provides new channels and opportunities for small- and middle-sized enterprises’ (SMEs) financing. Supply chain finance is a hot topic in theoretical and practical circles. Financial institutions transform materialized capital flows into online data under big data scenario, which provides networked, precise, and computerized financial services for SMEs in the supply chain. By drawing on the risk management theory in economics and the distributed hydrological model in hydrology, this paper presents a supply chain financial risk prediction method under big data. First, we build a “hydrological database” used for the risk analysis of supply chain financing under big data. Second, we construct the risk identification models of “water circle model,” “surface runoff model,” and “underground runoff model” and carry on the risk prediction from the overall level (water circle). Finally, we launch the supply chain financial risk analysis from breadth level (surface runoff) and depth level (underground runoff); moreover, we integrate the analysis results and make financial decisions. The results can enrich the research on risk management of supply chain finance and provide feasible and effective risk prediction methods and suggestions for financial institutions.


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