Relevant bibliographies by topics / Risk – Mathematical models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Risk – Mathematical models'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Risk – Mathematical models"

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Prokopjeva, Evgenija, Evgeny Tankov, Tatyana Shibaeva, and Elena Perekhozheva. "Behavioral models in insurance risk management." Investment Management and Financial Innovations 18, no. 4 (October 21, 2021): 80–94. http://dx.doi.org/10.21511/imfi.18(4).2021.08.

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Behavioral characteristics attributed to consumers of insurance services are a relevant factor for analyzing the current situation in the insurance market and developing effective strategies for insurers’ actions. In turn, considering these characteristics allows the insurer to be more successful in the highly competitive field, achieving mutual satisfaction in interacting with the customer. This study is aimed to develop cognitive models of the situation (frame) “Insurance”, taking into account the specifics of the Russian insurance market and systemic factors affecting participants’ behavior in the market. In this regard, the study involves systemizing risks at various levels of the economic system, generalizing factors for the motivation of insurance consumers, developing descriptive and economic-mathematical models for the behavior of economic entities in risky situations.The results obtained represent a behavioral model of interactions among insurance market entities, which determines opportunities for efficient and mutually beneficial coordination of their activities. The developed model includes the following elements: structured individual and institutional frames “Insurance”; a professional index of interest in insurance presented in the form of a mathematical model; methodology for governing the relationships among insurance participants in the digital environment.The recommendations enable predictions of the situation in the insurance market and allow most accurately defining the consumer needs in the conditions of market changes.
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Yarygina, I. Z., V. B. Gisin, and B. A. Putko. "Fractal Asset Pricing Models for Financial Risk Management." Finance: Theory and Practice 23, no. 6 (December 24, 2019): 117–30. http://dx.doi.org/10.26794/2587-5671-2019-23-6-117-130.

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The article presents the analysis findings of the problems and prospects of using the fractal markets theory to mathematically predict the price dynamics of assets as part of a financial risk management strategy. The aim of the article is to find out the features of value of bank assets and to develop recommendations for assessing financial risks based on mathematical methods for forecasting economic processes. Theoretical and empirical research methods were used to achieve the aim. The article reveals the features of mathematical modeling of economic processes related to asset pricing in a volatile market. It was proved that using financial mathematics in banking contributes to the stable development of the economy. Mathematical modeling of the price dynamics of financial assets is based on a substantive hypothesis and supported by an adequate apparatus of fractal pair pricing models in order to reveal specific market relations of business entities. According to the authors, the prospects of using forecast models to minimize the financial risks of derivative financial instruments are positive. The authors concluded that the considered methods contribute to managing financial risks and improving forecasts, including operations with derivatives. Besides, the studied fractal volatility parameters proved the predictive power regarding extreme events in financial markets, such as the bankruptcy of Lehman Brothers investment bank in 2008. The relevance of the article is due to the fact that the favorable investment climate and the use of modern financing methods largely depend on the effective financial risk management.
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&NA;. "Biologically Based Mathematical Models of Lung Cancer Risk." Epidemiology 4, no. 3 (May 1993): 193–94. http://dx.doi.org/10.1097/00001648-199305000-00002.

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Park, Colin N. "Mathematical models in quantitative assessment of carcinogenic risk." Regulatory Toxicology and Pharmacology 9, no. 3 (June 1989): 236–43. http://dx.doi.org/10.1016/0273-2300(89)90062-7.

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Antucheviciene, Jurgita, Gang Kou, Vida Maliene, and Egidijus Rytas Vaidogas. "Mathematical Models for Dealing with Risk in Engineering." Mathematical Problems in Engineering 2016 (2016): 1–3. http://dx.doi.org/10.1155/2016/2832185.

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Ifrim, Ana Maria. "Mathematical Models in Quality Engineering." International Journal of Innovation in the Digital Economy 8, no. 3 (July 2017): 18–34. http://dx.doi.org/10.4018/ijide.2017070102.

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The present paper deals with the factors that contribute to assuring the quality of the processes involved in project management. The novelty of the approach consists in the fact that the project management processes are analysed with the help of quality indicators in case of time variance. By studying the numeric variable for the proposed economic phenomenon, a smaller discrete interval is obtained, which accounts for the numeric variable being treated as a continuous variable. The practical application of such an analysis is that a risk management plan can be designed based on the parameters which define the quality of the management process.
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Bondareva, Irina Olegovna, Sabina M. Sidagalieva, and Evgeniya T. Nesterova. "MATHEMATICAL MODELING OF RISK MANAGEMENT IN TRANSPORT LOGISTICS." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2021, no. 2 (April 30, 2021): 75–88. http://dx.doi.org/10.24143/2072-9502-2021-2-75-88.

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The article considers the business processes at the transport logistics enterprises as a chain of clear regulations, where noncompliance or delay of one of them results in disruption of the whole process. Risk management is one of the key tasks requiring the development of modeling tools and prevention of undesirable situations. There has been shown a structural model of the risk of failure to achieve the strategic goal of a cargo port, supplemented by several levels of consideration. The tree of goals of the transport logistics enterprise was built. Failure to achieve a particular goal is considered as a risk situation, or a risk. A set of factors for assessing its implementation is opposed to each goal, formulas for calculating the indicators used are given. A model of scenarios of all existing significant risks has been developed. A multi-level hybrid logical-probabilistic model, a cascade logical-probabilistic model and a multi-level cascade hybrid logical-probabilistic model of the risk of failure to achieve the main strategic goal of the port/transport enterprise are proposed. The main idea is the need to link together the technology of formalizing risks using the constructed logical-probabilistic models and simulation, where the interpretation of the results is possible using logical and probabilistic models and scenarios. The proposed models make it possible to carry out a comprehensive analysis of the risk of failure to achieve the strategic goal of a cargo port based on the scenario formalization of risks of various levels of management, as well as to simplify the process of interpreting the results of simulation modeling taking into account external factors of influence. The integrated use of all these models is the basis for the development of timely management decisions. Particular attention is paid to the description of the technology for constructing logical, probabilistic and scenario models of various types.
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Gosling, John Paul. "Harnessing mathematical models and uncertainty in toxicological risk assessments." Toxicology Letters 229 (September 2014): S160. http://dx.doi.org/10.1016/j.toxlet.2014.06.551.

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Kodell, Ralph L. "The use of mathematical models in carcinogenesis risk assessment." Mathematical and Computer Modelling 11 (1988): 146–51. http://dx.doi.org/10.1016/0895-7177(88)90470-0.

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Voina, O. A., and A. O. Voyna. "Mathematical Models of Risk Control for Regenerating Markov Processes." Cybernetics and Systems Analysis 55, no. 5 (September 2019): 817–27. http://dx.doi.org/10.1007/s10559-019-00192-x.

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Dissertations / Theses on the topic "Risk – Mathematical models"

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Capelli, Giacomo <1991&gt. "Mathematical Models for Operational Risk Management." Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/8599.

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The thesis presents the state-of-the-art mathematical statistical models for Operational Risk measurement and management as well as the organisational and managerial processes supporting the creation of risk OR measures. We emphasise the most interesting probabilistic ideas employed in the field and apply the models on real data; these are used in the actuarial modelling paradigm following a Loss Distribution Approach. Extreme Value Theory, convolution transforms and copulæ theory will all be part of OR analysis to arrive to a regulatory risk measure. The thesis accompanies the theoretical modelling part with the management processes that are needed to implement these models in financial institutions, along with the primary risk mitigation techniques used against OR events. Finally, in light of the recent consultations carried out by the Bank for International Settlements regarding the partial substitution of actuarial models, we will also reflect on the differences, commonalities, and limitations of present and future OR modelling. The thesis is organised as follows. Chapter I introduces the major risk types institutions face. Chapter II describes the non-actuarial risk measurement techniques used by banks without internally developed actuarial models. The central mathematics of the thesis is presented and applied in Chapter III. Chapter IV compares the present modelling techniques with recently proposed modifications. Chapter VI treats the managerial aspects of OR and Chapter VI studies risk mitigation tools usually adopted. We conclude in Chapter VII with reflections and final comments.
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Ngwenza, Dumisani. "Quantifying Model Risk in Option Pricing and Value-at-Risk Models." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31059.

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Financial practitioners use models in order to price, hedge and measure risk. These models are reliant on assumptions and are prone to ”model risk”. Increased innovation in complex financial products has lead to increased risk exposure and has spurred research into understanding model risk and its underlying factors. This dissertation quantifies model risk inherent in Value-at-Risk (VaR) on a variety of portfolios comprised of European options written on the ALSI futures index across various maturities. The European options under consideration will be modelled using the Black-Scholes, Heston and Variance-Gamma models.
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Siu, Kin-bong Bonny, and 蕭健邦. "Expected shortfall and value-at-risk under a model with market risk and credit risk." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37727473.

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Gu, Jiawen, and 古嘉雯. "On credit risk modeling and credit derivatives pricing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/202367.

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In this thesis, efforts are devoted to the stochastic modeling, measurement and evaluation of credit risks, the development of mathematical and statistical tools to estimate and predict these risks, and methods for solving the significant computational problems arising in this context. The reduced-form intensity based credit risk models are studied. A new type of reduced-form intensity-based model is introduced, which can incorporate the impacts of both observable trigger events and economic environment on corporate defaults. The key idea of the model is to augment a Cox process with trigger events. In addition, this thesis focuses on the relationship between structural firm value model and reduced-form intensity based model. A continuous time structural asset value model for the asset value of two correlated firms with a two-dimensional Brownian motion is studied. With the incomplete information introduced, the information set available to the market participants includes the default time of each firm and the periodic asset value reports. The original structural model is first transformed into a reduced-form model. Then the conditional distribution of the default time as well as the asset value of each name are derived. The existence of the intensity processes of default times is proven and explicit form of intensity processes is given in this thesis. Discrete-time Markovian models in credit crisis are considered. Markovian models are proposed to capture the default correlation in a multi-sector economy. The main idea is to describe the infection (defaults) in various sectors by using an epidemic model. Green’s model, an epidemic model, is applied to characterize the infectious effect in each sector and dependence structures among various sectors are also proposed. The models are then applied to the computation of Crisis Value-at-Risk (CVaR) and Crisis Expected Shortfall (CES). The relationship between correlated defaults of different industrial sectors and business cycles as well as the impacts of business cycles on modeling and predicting correlated defaults is investigated using the Probabilistic Boolean Network (PBN). The idea is to model the credit default process by a PBN and the network structure can be inferred by using Markov chain theory and real-world data. A reduced-form model for economic and recorded default times is proposed and the probability distributions of these two default times are derived. The numerical study on the difference between these two shows that our proposed model can both capture the features and fit the empirical data. A simple and efficient method, based on the ordered default rate, is derived to compute the ordered default time distributions in both the homogeneous case and the two-group heterogeneous case under the interacting intensity default contagion model. Analytical expressions for the ordered default time distributions with recursive formulas for the coefficients are given, which makes the calculation fast and efficient in finding rates of basket CDSs.
published_or_final_version
Mathematics
Doctoral
Doctor of Philosophy
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Gong, Qi, and 龔綺. "Gerber-Shiu function in threshold insurance risk models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40987966.

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Liu, Binbin, and 刘彬彬. "Some topics in risk theory and optimal capital allocation problems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48199291.

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In recent years, the Markov Regime-Switching model and the class of Archimedean copulas have been widely applied to a variety of finance-related fields. The Markov Regime-Switching model can reflect the reality that the underlying economy is changing over time. Archimedean copulas are one of the most popular classes of copulas because they have closed form expressions and have great flexibility in modeling different kinds of dependencies. In the thesis, we first consider a discrete-time risk process based on the compound binomial model with regime-switching. Some general recursive formulas of the expected penalty function have been obtained. The orderings of ruin probabilities are investigated. In particular, we show that if there exists a stochastic dominance relationship between random claims at different regimes, then we can order ruin probabilities under different initial regimes. Regarding capital allocation problems, which are important areas in finance and risk management, this thesis studies the problems of optimal allocation of policy limits and deductibles when the dependence structure among risks is modeled by an Archimedean copula. By employing the concept of arrangement increasing and stochastic dominance, useful qualitative results of the optimal allocations are obtained. Then we turn our attention to a new family of risk measures satisfying a set of proposed axioms, which includes the class of distortion risk measures with concave distortion functions. By minimizing the new risk measures, we consider the optimal allocation of policy limits and deductibles problems based on the assumption that for each risk there exists an indicator random variable which determines whether the risk occurs or not. Several sufficient conditions to order the optimal allocations are obtained using tools in stochastic dominance theory.
published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy
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蕭德權 and Tak-kuen Siu. "Risk measures in finance and insurance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31242297.

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Rong, Yian, and 戎軼安. "Applications of comonotonicity in risk-sharing and optimal allocation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/207205.

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Over the past decades, researchers in economics, financial mathematics and actuarial science have introduced results to the concept of comonotonicity in their respective fields of interest. Comonotonicity is a very strong dependence structure and is very often mistaken as a dependence structure that is too extreme and unrealistic. However, the concept of comonotonicity is actually a useful tool for solving several research and practical problems in capital allocation, risk sharing and optimal allocation. The first topic of this thesis is focused on the application of comonotonicity in optimal capital allocation. The Enterprise Risk Management process of a financial institution usually contains a procedure to allocate the total risk capital of the company into its different business units. Dhaene et al. (2012) proposed a unifying capital allocation framework by considering some general deviation measures. This general framework is extended to a more general optimization problem of minimizing separable convex function with a linear constraint and box constraints. A new approach of solving this constrained minimization problem explicitly by the concept of comonotonicity is developed. Instead of the traditional Kuhn-Tucker theory, a method of expressing each convex function as the expected stop-loss of some suitable random variable is used to solve the optimization problem. Then, some results in convex analysis with infimum-convolution are derived using the result of this new approach. Next, Borch's theorem is revisited from the perspective of comonotonicity. The optimal solution to the Pareto optimal risk-sharing problem can be obtained by the Lagrangian method or variational arguments. Here, I propose a new method, which is based on a Breeden-Litzanbeger type integral representation formula for increasing convex functions. It enables the transform of the objective function into a sum of mixtures of stop-losses. Necessary conditions for the existence of optimal solution are then discussed. The explicit solution obtained allows us to show that the risk-sharing problem is indeed a “point-wise” problem, and hence the value function can be obtained immediately using the notion of supremum-convolution in convex analysis. In addition to the above classical risk-sharing and capital allocation problems, the problem of minimizing a separable convex objective subject to an ordering restriction is then studied. Best et al. (2000) proposed a pool adjacent violators algorithm to compute the optimal solution. Instead, we show that using the concept of comonotonicity and the technique of dynamic programming the solution can be derived in a recursive manner. By identifying the right-hand derivative of the convex functions with distribution functions of some suitable random variables, we rewrite the objective function into a sum of expected deviations. This transformation and the fact that the expected deviation is a convex function enable us to solve the minimizing problem.
published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy
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Basak, Rishi. "Environmental management systems and the intra-firm risk relationship." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0034/MQ64316.pdf.

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Li, Tang, and 李唐. "Markov chain models for re-manufacturing systems and credit risk management." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40203700.

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Books on the topic "Risk – Mathematical models"

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1957-, Willmot G. E., ed. Insurance risk models. Schaumburg, Ill: Society of Acturaries, 1992.

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Marshall, E. I. Mathematical models and political risk. Bradford: Bradford University Management Centre, 1986.

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Schmidli, Hanspeter. Characteristics of ruin probabilities in classical risk models with and without investment, Cox risk models and perturbed risk models. Århus, Denmark: University of Aarhus, Dept. of Theoretical Statistics, Institute of Mathematical Sciences, 2000.

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Gaspar, Raquel M. Credit risk & forward price models. Stockholm: Stockholm School of Economics, 2006.

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Heilmann, Wolf-Rüdiger. Fundamentals of risk theory. Karlsruhe: VVW, 1988.

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Sébastien, Lleo, ed. Risk-sensitive investment management. New Jersey: World Scientific, 2015.

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Bertrand, Munier, and Machina Mark J, eds. Models and experiments in risk and rationality. Dordrecht: Kluwer Academic, 1994.

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author, Frey Rüdiger, and Embrechts Paul 1953 author, eds. Quantitative risk management: Concepts, techniques and tools. Princeton, NJ: Princeton University Press, 2015.

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Rüdiger, Frey, and Embrechts Paul 1953-, eds. Quantitative risk management: Concepts, techniques, and tools. Princeton, N.J: Princeton University Press, 2005.

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Mathematical methods of environmental risk modeling. Boston, USA: Kluwer Academic Publishers, 2001.

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Book chapters on the topic "Risk – Mathematical models"

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Brannigan, Vincent, and Carol Smidts. "Risk Based Regulation Using Mathematical Risk Models." In Probabilistic Safety Assessment and Management ’96, 721–25. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-3409-1_115.

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Kusuoka, Shigeo. "A Remark on default risk models." In Advances in Mathematical Economics, 69–82. Tokyo: Springer Japan, 1999. http://dx.doi.org/10.1007/978-4-431-65895-5_5.

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Fleming, Wendell H. "Optimal Investment Models and Risk Sensitive Stochastic Control." In Mathematical Finance, 75–88. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2435-6_6.

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Ammann, Manuel. "Review of Credit Risk Models." In Lecture Notes in Economics and Mathematical Systems, 47–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-22330-7_3.

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Tapiero, Charles S., and Pierre Vallois. "Financial Modelling and Memory: Mathematical System." In Future Perspectives in Risk Models and Finance, 149–246. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07524-2_6.

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Hill, M. D. "Use of Mathematical Models in Risk Assessment and Risk Management." In Radionuclides in the Food Chain, 335–61. London: Springer London, 1988. http://dx.doi.org/10.1007/978-1-4471-1610-3_24.

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Kusuoka, Shigeo, and Takenobu Nakashima. "A remark on credit risk models and copula." In Advances in Mathematical Economics, 53–84. Tokyo: Springer Japan, 2012. http://dx.doi.org/10.1007/978-4-431-54114-1_3.

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Spangler, Manuela. "Credit Risk Models: A Literature Review." In Mathematische Optimierung und Wirtschaftsmathematik | Mathematical Optimization and Economathematics, 19–55. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-23915-2_3.

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Arai, Takuji. "Local Risk-Minimization for Barndorff-Nielsen and Shephard Models with Volatility Risk Premium." In Advances in Mathematical Economics Volume 20, 3–22. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0476-6_1.

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Benkert, Christoph. "On the Economic Content of Models of Default Risk." In Lecture Notes in Economics and Mathematical Systems, 7–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17039-3_2.

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Conference papers on the topic "Risk – Mathematical models"

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Jensen, Emily, Maya Luster, Hansol Yoon, Brandon Pitts, and Sriram Sankaranarayanan. "Mathematical Models of Human Drivers Using Artificial Risk Fields." In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022. http://dx.doi.org/10.1109/itsc55140.2022.9922389.

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Ajmal, Ayesha, Seemal Irfan, Nosheen Sabahat, and Saad Bin Saleem. "Analysis of Flood Risk Management in the Context of Mathematical Models." In 2019 International Conference on Innovative Computing (ICIC). IEEE, 2019. http://dx.doi.org/10.1109/icic48496.2019.8966678.

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Razzaghi, Mehdi. "Teaching Statistical Models for Risk Assessment." In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t6c1.

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Risk analysis is used across many disciplines. Biological scientists use risk analysis to determine the chance of an adverse effect occurring. Engineers use it to assess the risk of structural failures. People in business and industry perform risk analysis to estimate profit and loss in decision making. Although there are many definitions of risk, the most accurate definition is based on probabilistic principles. To estimate risk quantitatively, a statistical model is used to describe the process and the associated risk is thereupon calculated. The underlying model and derivation of risk can lead to some non-trivial mathematical expressions. Here, we discuss challenges in teaching probabilistic risk assessment to researchers who are non-statisticians and non-mathematicians. Specific reference will be made to toxicological risk assessment.
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Kovtun, Dmitriy, Matvey Koptelov, and Anna Guseva. "Megaproject Risk Management Based on Loyalty Program Using Neural Network Models." In 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). IEEE, 2020. http://dx.doi.org/10.1109/summa50634.2020.9280581.

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Balaji, N., N. B. Sanjana, E. P. Siva, R. Lokesh, and A. J. Catherina. "Risk and effective analysis of COVID-19 vaccine using mathematical models in the pandemic situation." In 2ND INTERNATIONAL CONFERENCE ON MATHEMATICAL TECHNIQUES AND APPLICATIONS: ICMTA2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0108456.

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Zappalà, G., S. Bonamano, A. Madonia, G. Caruso, and M. Marcelli. "Microbiological risk assessment in a coastal marine environment through the use of mathematical models." In WATER POLLUTION 2012. Southampton, UK: WIT Press, 2012. http://dx.doi.org/10.2495/wp120011.

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Schnabel, Freya, Jennifer Chun, Shira Schwartz, Amber Guth, Deborah Axelrod, Richard Shapiro, Karen Hiotis, and Julia Smith. "Abstract A29: Mathematical models are not the be-all and end-all for breast cancer risk assessment." In Abstracts: AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; November 16-19, 2016; Orlando, FL. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7755.carisk16-a29.

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Moskvichev, V. V., U. S. Postnikova, and O. V. Taseiko. "Cluster analysis and individual anthropogenic risk." In Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.54.88.063.

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Models and assessment methods of anthropogenic risk are analyzed at this article, general basis of mathematical approach for risk analysis is disclosed. Based on multivariate statistic methods, algorithm of analysis for Siberian territories safety is formulated, it allows to define acceptable level of risk for each territorial group (cities with population density more than 70 000, towns with population less than 70 000, and municipals areas).
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Manan, Norhafizah A., and Basir Abidin. "Risk prediction models for major adverse cardiac event (MACE) following percutaneous coronary intervention (PCI): A review." In THE 2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II): Empowering the Applications of Statistical and Mathematical Sciences. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4907524.

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Sakli, Leila, Jean Marc Mercantini, and Jean Claude Hennet. ""Study of supply chain vulnerabilities based on cognitive engineering and ARIMA formal models"." In The 11th International Conference on Integrated Modeling and Analysis in Applied Control and Automation. CAL-TEK srl, 2018. http://dx.doi.org/10.46354/i3m.2018.imaaca.009.

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"This research concerns the formulation of models and methods for supply chains risk analysis. An ontological approach using the KOD method (Knowledge Oriented Design) has been implemented to clearly identify relationships between the concepts of supply chain, risk, vulnerability and disturbances (critical scenarios). As a result, conceptual models of supply chains facing risk situations and critical scenarios are proposed. From the resulting conceptual models and mathematical models proposed in the literature, a multi-stage supply chain model using ARIMA models incorporating the randomness of the demand has been elaborated. In order to adapt this model to scenario criticality, constraints on orders and inventories have been taken into account. Under critical disturbances on information flows (demand) and physical flows (quality of the product supplied), constraints can be reached and supply chain behaviours can evolve toward critical dynamics or even become unstable. Supply chain vulnerabilities has been assessed and discussed."
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Reports on the topic "Risk – Mathematical models"

1

Shabelnyk, Tetiana V., Serhii V. Krivenko, Nataliia Yu Rotanova, Oksana F. Diachenko, Iryna B. Tymofieieva, and Arnold E. Kiv. Integration of chatbots into the system of professional training of Masters. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4439.

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Abstract:
The article presents and describes innovative technologies of training in the professional training of Masters. For high-quality training of students of technical specialties, it becomes necessary to rethink the purpose, results of studying and means of teaching professional disciplines in modern educational conditions. The experience of implementing the chatbot tool in teaching the discipline “Mathematical modeling of socio-economic systems” in the educational and professional program 124 System Analysis is described. The characteristics of the generalized structure of the chatbot information system for investment analysis are presented and given: input information, information processing system, output information, which creates a closed cycle (system) of direct and feedback interaction. The information processing system is represented by accounting and analytical data management blocks. The investment analysis chatbot will help masters of the specialty system analysis to manage the investment process efficiently based on making the right decisions, understanding investment analysis in the extensive structure of financial management and optimizing risks in these systems using a working mobile application. Also, the chatbot will allow you to systematically assess the disadvantages and advantages of investment projects or the direction of activity of a system analyst, while increasing interest in performing practical tasks. A set of software for developing a chatbot integrated into training is installed: Kotlin programming, a library for network interaction Retrofit, receiving and transmitting data, linking processes using the HTTP API. Based on the results of the study, it is noted that the impact of integrating a chatbot into the training of Masters ensures the development of their professional activities, which gives them the opportunity to be competent specialists and contributes to the organization of high-quality training.
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2

Pritchett, Lant, Kirsty Newman, and Jason Silberstein. Focus to Flourish: Five Actions to Accelerate Progress in Learning. Research on Improving Systems of Education (RISE), December 2022. http://dx.doi.org/10.35489/bsg-rise-misc_2022/07.

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Abstract:
There is a severe global learning crisis. While nearly all children start school, far too many do not learn even the most foundational skills of reading, writing, and basic mathematics during the years they spend there. The urgent need to address this crisis requires no elaborate reasoning. If one starts with love for a child, a human universal, it is easy to see that in the modern world a child’s dignity, self-worth, and freedom to define their own destiny require an adequate education. An adequate education is what will then enable that child to lead a full adult life as a parent, community member, citizen, and worker in the 21st century. To enable every child to leave school with the foundational skills they need will require fundamental changes to education systems. Since 2015, the Research on Improving Systems of Education (RISE) Programme, with which we are affiliated, has been conducting research exploring how to make these changes through country research teams in seven countries (Ethiopia, India, Indonesia, Nigeria, Pakistan, Tanzania, and Vietnam) and crosscutting teams on the political economy of education reform. Drawing on the cumulative body of research on learning outcomes and systems of education in the developing world, both from the RISE Programme and other sources, we advocate for five key actions to drive system transformation. (See next page.) A message cutting across all five actions is “focus to flourish”. Education systems have been tremendously successful at achieving specific educational goals, such as expanding schooling, because that is what they committed to, that is what they measured, that is what they were aligned for, and that is what they supported. In order to achieve system transformation for learning, systems must focus on learning and then act accordingly. Only after a system prioritises learning from among myriad competing educational goals can it dedicate the tremendous energies necessary to succeed at improving learning. The research points to these five actions as a means to chart a path out of the learning crisis and toward a future that offers foundational skills to all children. The first section that follows provides background on the depth and nature of the learning crisis. The remainder of the document explains each of the five actions in turn, synthesising the research that informs each action, contrasting that action with the prevailing status quo, and describing what the action would entail in practice.
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