Journal articles on the topic 'Decision making – Mathematical models'
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Novikov, Dmitry, Vsevolod Korepanov, and Alexander Chkhartishvili. "Reflexion in mathematical models of decision-making." International Journal of Parallel, Emergent and Distributed Systems 33, no. 3 (January 5, 2018): 319–35. http://dx.doi.org/10.1080/17445760.2017.1413189.
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Stanojević, Bogdana, Simona Dzitac, and Ioan Dzitac. "Fuzzy Numbers and Fractional Programming in Making Decisions." International Journal of Information Technology & Decision Making 19, no. 04 (July 2020): 1123–47. http://dx.doi.org/10.1142/s0219622020300037.
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This study surveys the use of fuzzy numbers in classic optimization models, and its effects on making decisions. In a wide sense, mathematical programming is a collection of tools used in mathematical optimization to make good decisions. There are many sectors of economy that employ it. Finance and government, logistics and manufacturing, the distribution of the electrical power are worth to be first mentioned. When real life problems are modeled mathematically, there is always a trade-off between model’s accuracy and complexity. By this survey, we aim to present in a concise form some mathematical models from the literature together with the methods to solve them. We will focus mainly on fuzzy fractional programming problems. We will also refer to but not describe in detail the multi-criteria decision-making problems involving fuzzy numbers and linear fractional programming models.
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Bustince, Humberto, Susana Montes, and Manuel Ojeda-Aciego. "Fuzzy Mathematical Models for Computer Science and Decision Making." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 1 (December 2017): 1–3. http://dx.doi.org/10.1142/s0218488517020020.
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Sukhodolov, Alexander, Valentina Marenko, and Vyacheslav Lozhnikov. "Mass Media Information Validation Models for Decision Making." Theoretical and Practical Issues of Journalism 9, no. 1 (March 23, 2020): 34–45. http://dx.doi.org/10.17150/2308-6203.2020.9(1).34-45.
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he authors study the mathematical models that formalize philosophical notions “information field” and “information” with mathematical means including Gauss’s theorem. They propose a radial hierarchy of information validation that reflects the taxonomic structure of factors affecting it. Besides, the authors work out a cognitive model that contributes to making scientifically based decisions concerning validity of information in the mass media. The model is tested with the help of simplicial analysis which enables to see unevident links between the factors influencing the formation of the structure of the cognitive model. Calculation procedures helped to correct the analyst’s speculations concerning the links between the factors in the cognitive model, applying the notion “coupling network”, which implies a connection between two separate simplexes through a network of intermediary simplexes. The results of the study may add to executive decision-making when designing and implementing programs of ideological purpose.
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Xu, Ting. "Uncertainty, Ignorance and Decision-Making." Amicus Curiae 3, no. 1 (October 27, 2021): 10–32. http://dx.doi.org/10.14296/ac.v3i1.5350.
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A great deal of decision-making during crises is about coping with uncertainty. For rulemakers, this poses a fundamental challenge, as there has been a lack of a rigorous framework for understanding and analysing the nature and function of uncertainty in the context of rulemaking. In coping with crises, modelling has become a governance tool to navigate and tame uncertainty and justify decisions. This is because models, in particular mathematical models, can be useful to produce precise answers in numbers. This article examines the challenges rulemakers are facing in an uncertain world and argues that one of the most important challenges lies in rulemakers’ failures to understand the nature of uncertainty and ignorance in the contested arena of science for decision-making. It focuses on the relationship between uncertainty, ignorance and decisionmaking through a case study of the interaction between modelling and rulemaking in the Covid-19 pandemic. In so doing, this article provides an alternative strategy to number- and model-based rulemaking in an uncertain world. It provokes a rethinking of using science to measure and govern human affairs and the impact of numbers and quantification on law.
Keywords: uncertainty; ignorance; decision-making; rulemaking; models; mathematical modelling; quantification; Covid-19.
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Tofallis, Christopher, Mik Wisniewski, and Tony Dacre. "Mathematical Programming: Optimization Models for Business and Management Decision Making." Journal of the Operational Research Society 42, no. 6 (June 1991): 514. http://dx.doi.org/10.2307/2583459.
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Gelrud, Yakov, and Evgeny Kibalov. "ECONOMIC-MATHEMATICAL AND LOGICAL-HEURISTIC DECISION-MAKING MODELS: NEOSYSTEMIC SYNTHESIS." Bulletin of South Ural State University series "Economics and management" 14, no. 4 (2020): 130–37. http://dx.doi.org/10.14529/em200415.
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Tofallis, Christopher. "Mathematical Programming: Optimization Models for Business and Management Decision Making." Journal of the Operational Research Society 42, no. 6 (June 1991): 514. http://dx.doi.org/10.1057/jors.1991.100.
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McGovern, Bernadette, M. Wisniewski, and T. Dacre. "Mathematical Programming: Optimization Models for Business and Management Decision Making." Statistician 41, no. 1 (1992): 127. http://dx.doi.org/10.2307/2348646.
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Mintz, Alex, Nehemia Geva, and Karl Derouen. "Mathematical models of foreign policy decision-making: Compensatory vs. noncompensatory." Synthese 100, no. 3 (September 1994): 441–60. http://dx.doi.org/10.1007/bf01063911.
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Bloom, Laura A., and Bernard S. Bloom. "DECISION ANALYTIC MODELING IN HEALTH CARE DECISION MAKING." International Journal of Technology Assessment in Health Care 15, no. 2 (May 1999): 332–39. http://dx.doi.org/10.1017/s0266462399015251.
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The need to choose among alternatives instead of allowing the market to make choices has led health care professionals to rely on scientific information as an aid in decision making. Mathematical modeling is one of the increasingly common tools used over the past three decades to produce new information. But we have used almost exclusively noncomplex models to help analyze complex systems problems. The need to integrate the complexity of the interactions of clinical, quality of life, and economic attributes into such models can no longer be ignored. The opportunity is available to use existing complex systems modeling techniques for health care questions to improve the quality of study outputs, which can, in turn, help produce more rational decisions.
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Croucher, John S., and Stephnie Hon. "Strategic Decision Making Using Waiting Line Models." International Journal of Strategic Decision Sciences 10, no. 3 (July 2019): 20–32. http://dx.doi.org/10.4018/ijsds.2019070102.
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This article considers issues facing management and their decision processes to deal with them effectively. The first develops an optimal strategy for leasing equipment in which there are three available management options, the criterion being to minimize the total cost of use. The first option is to stay with the status quo, the second to lease faster equipment, and the third to lease multiple pieces of equipment. Two scenarios are discussed, one where the waiting time in the queue is taken into account while the other includes the time spent using the equipment itself. A second problem deals with the sharing of resources and optimal configurations, using first a mathematical solution and then a discussion of the practicalities of implementation. The final case involves the optimal times to employ a faster server as a function of how busy the queueing system is.
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Kuchynskyi, Volodymyr, and Serhii Pohorielov. "USE OF ECONOMIC AND MATHEMATICAL MODELS TO MAKE EFFECTIVE DECISIONS IN THE FIELD OF HUMAN RESOURCE MANAGEMENT." Bulletin of the National Technical University "Kharkiv Polytechnic Institute" (economic sciences), no. 2 (March 21, 2023): 109–16. http://dx.doi.org/10.20998/2519-4461.2023.2.109.
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The role of using economic-mathematical models for making effective management decisions both in the process of managing socio-economic systems as a whole and in the field of personnel management is defined. Modeling is considered as the main specific method of science, which is used for the analysis and synthesis of control systems, as well as a special cognitive method, when the research subject instead of the directly studied object of knowledge chooses or creates an auxiliary object similar to it - an image or model, examines it, and transfers the acquired new knowledge to the original object. Economic-mathematical models allow combining complex and sometimes vaguely defined factors related to a decision-making problem into a logically clear scheme that can be analyzed in detail. Such an analysis allows obtaining and evaluating alternative possibilities for the functioning of the economic system and predicting the consequences of management decisions. In order to ensure the effectiveness of personnel management processes based on the use of economic-mathematical models for management decision-making, the following were defined: classification of economic-mathematical models by purpose and purpose of application, optimization nature of most economic-mathematical models, model building procedure and preparation of a management decision based on economic -mathematical methods, a generalized view of the problem of economic-mathematical programming and typical managerial problems of personnel management, which are solved with the help of economic-mathematical models.
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Kurakova, Oksana. "Methodological aspects of applying mathematical modeling in making managerial decisions." E3S Web of Conferences 389 (2023): 03111. http://dx.doi.org/10.1051/e3sconf/202338903111.
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This paper considers the use of mathematical models in management. In today's world, managerial decision making is becoming an increasingly complex process and requires the use of various tools and methods. Any company makes decisions on a continuous basis, since the management process is continuous, and the content of management is determined by the content of decisions made. The author of this paper considers methodological approaches to the application of mathematical modeling in making managerial decisions based on data analysis and computational methods. Mathematical modeling helps analyze complex systems and processes, find optimal decisions, and predict the results. This paper describes the general algorithm of making a managerial decision and types of mathematical models that can be used in making managerial decisions. In addition, the paper presents an algorithm for applying mathematical modeling in making managerial decisions and considers an example of using mathematical modeling when solving a real problem. In conclusion, the author emphasizes the importance of applying a correct approach to the use of mathematical modeling in management and offers her recommendations. To achieve the maximum efficiency, it is necessary to choose the right methods and algorithms, as well as to have a sufficient amount of data for analysis. The article will be of interest to specialists in the field of management and economics, as well as to anyone who is interested in the use of mathematical methods in solving management problems.
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Kekytė, Ieva, and Viktorija Stasytytė. "Comparative Analysis of Investment Decision Models." Mokslas - Lietuvos ateitis 9, no. 2 (June 2, 2017): 197–208. http://dx.doi.org/10.3846/mla.2017.1023.
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Rapid development of financial markets resulted new challenges for both investors and investment issues. This increased demand for innovative, modern investment and portfolio management decisions adequate for market conditions. Financial market receives special attention, creating new models, includes financial risk management and investment decision support systems.Researchers recognize the need to deal with financial problems using models consistent with the reality and based on sophisticated quantitative analysis technique. Thus, role mathematical modeling in finance becomes important. This article deals with various investments decision-making models, which include forecasting, optimization, stochatic processes, artificial intelligence, etc., and become useful tools for investment decisions.
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Cortés Pellicer, Pascual, and Faustino Alarcón Valero. "Identification of reverse logistics decision types from mathematical models." Journal of Industrial Engineering and Management 11, no. 2 (April 6, 2018): 239. http://dx.doi.org/10.3926/jiem.2530.
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Purpose: The increase in social awareness, politics and environmental regulation, the scarcity of raw materials and the desired “green” image, are some of the reasons that lead companies to decide for implement processes of Reverse Logistics (RL). At the time when incorporate new RL processes as key business processes, new and important decisions need to be made. Identification and knowledge of these decisions, including the information available and the implications for the company or supply chain, will be fundamental for decision-makers to achieve the best results. In the present work, the main types of RL decisions are identified.Design/methodology/approach: This paper is based on the analysis of mathematical models designed as tools to aid decision making in the field of RL. Once the types of interest work to be analyzed are defined, those studies that really deal about the object of study are searched and analyzed. The decision variables that are taken at work are identified and grouped according to the type of decision and, finally, are showed the main types of decisions used in mathematical models developed in the field of RL. Findings: The principal conclusion of the research is that the most commonly addressed decisions with mathematical models in the field of RL are those related to the network’s configuration, followed by tactical/operative decisions such as the selections of product’s treatments to realize and the policy of returns or prices, among other decisions.Originality/value: The identification of the main decisions types of the reverse logistics will allow the managers of these processes to know and understand them better, while offer an integrated vision of them, favoring the achievement of better results.
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Ivanov, Aleksandr K., Aleksandr L. Savkin, and Ilia V. Chernyshev. "THE MATHEMATICAL MODELS OF INFORMATION DECISIONS IN OPERATIONS CONTROLS." АВТОМАТИЗАЦИЯ ПРОЦЕССОВ УПРАВЛЕНИЯ 63, no. 1 (2021): 56–73. http://dx.doi.org/10.35752/1991-2927-2021-1-63-56-73.
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The article proves the relevance of developing decision-support systems that ensure an automation of intellectual activity of operation controls officers. It gives a brief review of scientific researches in terms of psychology of behavior, perception and thinking; and shows the viability of mathematical models for decision-making based on the analogue ones in other science and technology domains having a corresponding mathematical apparatus. The authors create the scheme of information decisions made by operation controls, which includes several stages of initial data conversion and gradient distribution of information resources in a one-dimensional space of structural units. The complex of mathematical models for information decision making for three stages of conversion and for various modes of initial data entry has been developed. The first model describes the gradient distribution of initial data in the form of a partial differential equation, while converting stages are given as an ordinary differential one. The second model includes the gradient distribution of initial data and subsequently converted data in the space of operating controls. It is represented as a partial differential system. The third and fourth models additionally incorporate convective terms of partial differential equations. The article gives the analytical solutions of all models and the estimated structures of information resources allocation
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Kulakova, L. I., A. V. Polyanin, and V. V. Tarnovskiy. "Modeling management decision making in the socially oriented business structures." POWER AND ADMINISTRATION IN THE EAST OF RUSSIA 96, no. 3 (2021): 78–86. http://dx.doi.org/10.22394/1818-4049-2021-96-3-78-86.
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The article discusses the main economic and mathematical models used in making and implementing management decisions. It has been established that the beginning of making a managerial decision is determined by the nature of solution of managerial problem: creative and standard, implementation of a managerial decision is subordinate to the certainty of the result, that is, its probabilistic or deterministic outcome. The procedure and modeling of the process of making and implementing management decisions will be linear or non-linear. On this basis, the types of mathematical models for solving managerial problems are considered when making and implementing managerial decisions to optimize the chosen option. The author's model is proposed based on a two-phase system from the theory of queues with elements of nonlinear programming for making and implementing managerial decisions in socially oriented business structures. The model includes a combination of linear and non-linear programming. Since when conducting business, socially oriented entrepreneurial structures are aimed at obtaining two types of effects, both commercial and social.
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Sanfey, Alan G. "Social Decision-Making: Insights from Game Theory and Neuroscience." Science 318, no. 5850 (October 26, 2007): 598–602. http://dx.doi.org/10.1126/science.1142996.
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By combining the models and tasks of Game Theory with modern psychological and neuroscientific methods, the neuroeconomic approach to the study of social decision-making has the potential to extend our knowledge of brain mechanisms involved in social decisions and to advance theoretical models of how we make decisions in a rich, interactive environment. Research has already begun to illustrate how social exchange can act directly on the brain's reward system, how affective factors play an important role in bargaining and competitive games, and how the ability to assess another's intentions is related to strategic play. These findings provide a fruitful starting point for improved models of social decision-making, informed by the formal mathematical approach of economics and constrained by known neural mechanisms.
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Sun, Richie. "Economic Mathematical Models: Examining Their Impact on Individuals." Advances in Economics, Management and Political Sciences 50, no. 1 (December 1, 2023): 16–22. http://dx.doi.org/10.54254/2754-1169/50/20230542.
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Economic mathematical models are essential tools for comprehending intricate economic phenomena, shaping policies, and aiding decision-making. This paper provides a comprehensive analysis of the positive and negative effects of these models on individuals in the context of economic analysis and decision-making. The benefits of economic mathematical models lie in their ability to enhance understanding through data-driven approaches and advanced algorithms, enabling evidence-based policy formulation and solutions to economic challenges. Moreover, economic models play a crucial role in policy formulation, enabling proactive analysis to minimize unintended consequences and maximize policy effectiveness, benefiting both individuals and society. These models also aid decision-making by providing quantitative frameworks to evaluate different options, reducing uncertainty and risk. However, it is essential to acknowledge the limitations of these models, as they are simplifications of reality and may lead to biases and inaccuracies in predictions and recommendations. Ethical implications arise when decision-makers prioritize economic efficiency over social welfare, potentially leading to unfair outcomes for certain individuals or groups. To address these concerns, this paper presents case studies illustrating the successes and shortcomings of economic models in real-world applications. It also explores alternative methodologies, such as qualitative research and mixed-method approaches, to complement economic analysis. By fostering informed discussions on the appropriate role of economic mathematical models, this research contributes to the development of more equitable and effective economic policies that benefit society as a whole.
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Vanagas, Giedrius, Tomas Krilavičius, and Ka Lok Man. "Mathematical Modeling and Models for Optimal Decision-Making in Health Care." Computational and Mathematical Methods in Medicine 2019 (August 14, 2019): 1–4. http://dx.doi.org/10.1155/2019/2945021.
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Khoroshko, Volodymyr, Yuliia Khokhlachova, and Natalia Vyshnevska. "Decision-making in a state cybersecurity." Information Security of the Person, Society, State. 2022. № 1–3 (34–36), no. 34-36 (December 12, 2022): 149–57. http://dx.doi.org/10.51369/2707-7276-2022-(1-3)-17.
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Nowadays, the issue of cyber security as a component of information security of the state is extremely relevant for Ukraine and the World community. Even so, it should be taken into account that the use of cyberspace expands people's communication capabilities, promotes the development of information technologies, research and novelty, and stimulates the development of industry and the economy. Nevertheless, the advantages of modern cyberspace can lead to new threats to people, society, national and international security. Along with initiatives of natural (unintentional) origin, the number and power of cyberattacks is increasing. These cyberattacks are inspired by the interests of individuals, groups, states, and associations of states. Increasing the effectiveness of mathematical modeling of state cyber security information systems can be ensured by modeling both the complex system and the subsystems that are the essential part of it. It stimulates the progress of models and algorithms that allow solving complex problems of system management and processing of the information flow. Furthermore, it is necessary to limit the tolerance interval of the solution for each task of the complex system of cyber security information systems. The following steps should be taken into account: – assignment of directive terms for solving problems; – information related tasks of information processing and transmission. The analysis of the research shows the weakness of a conventional approach to the complex solution of the problem of the synthesis of mathematical models and algorithms for determining the time of decision-making in the system of both security and information cyber security; methods of countering threats, mathematical methods of modeling, creating and evaluating models of both cyberattacks and cybersecurity and solving the problem of synthesis of mathematical models and algorithms for determining the time of decision-making in the system of both security and cyber security information systems. The defined issue is an unperformed part of the general problem of ensuring information security in complex systems of technical protection and information cyber security. The tasks belong to the class of problems of nonlinear mathematical programming. Performing them, methods of the theory of nonlinear programming can be used. The main priority of the tasks is its large dimension, which has a number of tasks that are performed in the network and are introduced in the information correlation. One of the current approaches to perform non-linear programming problems of large dimensions is the use of approximation methods of the theory of non-linear programming, the essence is that the solution of the finite-dimensional nonlinear complementarity problem is carried out as a result of solving a sequence of problems of a simpler type, which require significantly optimization operating costs than the original problem. Linear approximation is not always effective as it allows to obtain only a necessarily approximate value. Lately, the new research has introduced an approach to eliminate this fault with the help of auxiliary quadratic equation. The minimization method takes a special place among these methods. Unlike other methods of this class, it converges to any initial approximation and does not require assumptions about the convexity of functions, does not require firm positive certainty of the matrix of second derivatives of Lagrange functions, and has a fairly simple structure of the auxiliary quadratic problem. Hence, the linearization method has a linear rate of ascent. However, there is a modification of the method that means that at a considerable distance from the extremum point, the speed of convergence is linear, and with sufficient proximity to it, it is quadratic. Solving quadratic problems is not exact and only those restrictions with the maxima acceptable limits can be taken into account. This feature minimizes the dimension of auxiliary problems and thereby minimizes the computer complexity of the original nonlinear problem. The described algorithm is implemented in the form of applied programs for evaluating the effectiveness of algorithms for determining the time of decision-making by cyber security information systems. Thus, the given results can be used in the development of effective decision-making time determination algorithms based on mathematical models for decision-making support systems of the cyber security information system, as well as for modeling complex technical systems and assessing the effective use of several computing information systems. Key words: cyber security, cyber defense, cyber attacks, cyber defense system, information protection, state protection, cyber space, communication channels.
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Terentyev, A., M. Karelina, and E. Karelina. "Analytical models of decision-making theory in digital transport logistics." Journal of Physics: Conference Series 2061, no. 1 (October 1, 2021): 012129. http://dx.doi.org/10.1088/1742-6596/2061/1/012129.
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Abstract The active introduction of information and communication (digital) technologies into the modern reality of transport systems operation requires the development of modeling methods for the creation of software that allows to do the following: determine the formal efficiency of decisions made in digital transport systems (DTS); process large volumes of DTS data; perform analysis of DTS functioning environment using artificial intelligence algorithms, analytical analogues of neural networks, etc. A digital transport system, being a complex system, is determined by a large set of formalized indicators (database) and requires finding effective solutions for a sufficiently large number of criteria or signs of effectiveness. Obtaining reliable solutions in multi-criteria information situations causes difficulties, which are objective. In most cases modern mathematical models artificially reduce multi-criteria information situations to single-criteria categories. This approach, based on the use of integral criteria, has a fundamental disadvantage - the use of integral criteria to obtain estimates of the efficiency of actions or processes in complex systems is characterized by a high level of subjectivism. In this case, the obtained solution may be acceptable, but not an objective result. Therefore, it is necessary to develop mathematical models for solving multi-criteria problems applicable to the solution of problems in complex transport systems, allowing one to operate in the environment of large databases for operational reconfiguration of a management system in conditions of uncertainty and/or possible counteraction of the external environment. The article presents the results of development of mathematical methods of modeling, which allow us to construct algorithms for solving optimization tasks, formulated as multi-criteria models and in the presence of a high degree of uncertainty in the interaction of the system with the environment.
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Lone, MA, MS Puktha, and SA Mir. "Fuzzy linear mathematical programming in agriculture." BIBECHANA 13 (December 3, 2015): 72–76. http://dx.doi.org/10.3126/bibechana.v13i0.13363.
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In this paper we present a Fuzzy linear Mathematical programming approach for optimal allocation of land under cultivation. Fuzzy Mathematical programming approach is more realistic and flexible optimal solution for the agricultural land cultivation problem. In this study we have discussed how to deal with decision making problems that are described by Fuzzy linear programming (Flp) models and formulated with the elements of uncertainty. This form of approximation can be convenient and sufficient for making good decisions. BIBECHANA 13 (2016) 72-76
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Ngowtanasuwan, Grit. "Applied Mathematical Approach for Decision-Making in Building Plan Design." Asian Journal of Behavioural Studies 3, no. 9 (January 5, 2018): 39. http://dx.doi.org/10.21834/ajbes.v3i9.59.
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This article presents a method for solving decision in building plan design by using a mathematical model (nonlinear programming). First objective is to formulate mathematical models for analysis in dividing rooms and dimensions in a building plan. Secondly, to calculate the dimensions and room sizes which have minimum construction cost. A case study of a condominium building plan was analyzed in this research. The results found application of the mathematical model was applicable. The mathematical models were formulated, the minimum construction cost was ฿723,000 (US$24,100) and usable area in the condominium was 67.5 m2 and followed the assigned design constraints.Keywords: Building plan design; Mathematical model; Unit cost;eISSN 2398-4295 © 2018. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI:
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Bathory, David S. "Personality, Relationships, and Decision Making with Relational Dynamics." International Journal of Applied Behavioral Economics 4, no. 4 (October 2015): 52–74. http://dx.doi.org/10.4018/ijabe.2015100104.
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Within the field of Economics there is great interest in predicting the future. In creating Economic Models the rationale has been to create fixed equations that can account for all variables associated with the issues of capital, labor, wages, prices, tariffs and taxes but few models explored the human variable. Probability Statistics bases decision making models upon mathematical predictions. Game Theory is an economic model that begins to explain the rationale of human decision making, but fails to account for flawed thinking and pathology. Relational Dynamics attempts to provide a means of understanding the strategies used in communication and decision making. Within humanity, not all decisions are made rationally and to account for illogical choices, psychology has provided theories of pathology to explain human idiosyncrasies. This paper will explore personality disorders as described by the DSM V and Relational Dynamics in an attempt to understand how pathology influences relationships, decision making and behavioral economics.
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Druzhynin, V. A., M. M. Stepanov, G. B. Zhyrov, and L. O. Rіaba. "ALGORITHM FOR USING FUZZY LOGIC IN MANAGEMENT AND DECISION-MAKING MODELS." Collection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University, no. 69 (2020): 75–81. http://dx.doi.org/10.17721/2519-481x/2020/69-08.
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In real conditions, when the task of formally describing the control process of a rather complex process arises, it is necessary to take into account several external factors (parameters) and their values, which potentially tend to Infinity. At the same time, the system's response is not limited to just one control action. To automate the process of composing all possible combinations of linguistic descriptions of variables at the stage of fuzzy conditional statements and the decision-making mechanism on the use of control actions in the development of control and decision-making models, it is proposed to use fuzzy logical models. Ways to construct algorithms for converting input perturbations of complex systems into conceptual relations for automating the control process and supporting decision-making are considered. The fuzzy logic apparatus relation is used to formalize, process, and make decisions about the use of system control signals in response to external disturbances. Fuzzy control systems combine information from human experts (natural language) with measurements and mathematical models. Fuzzy Systems will turn the knowledge base into a mathematical formulation that has proven very effective in many applications. When designing a fuzzy system, many questions need to be answered, in particular in creating linguistic models to describe the functioning of complex systems, in particular radar mapping systems with recognition of objects on the ground and making decisions for controlling unmanned systems. Thus, at the stage of composing a set of fuzzy instructions (statements), it is of interest to formalize the following processes, such as determining all possible combinations of terms of linguistic variables and making a decision on the application of control actions, depending on external factors. In the process of formalizing the process of determining all possible combinations and terms of linguistic variables, it is necessary to create fuzzy instructions (rules) for managing a system or object for fuzzy-logical control models and decision-making in the process of developing models for the functioning of complex systems.
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Srinivasan, Madhumita, Robert Clarke, and Pavel Kraikivski. "Mathematical Models of Death Signaling Networks." Entropy 24, no. 10 (October 1, 2022): 1402. http://dx.doi.org/10.3390/e24101402.
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This review provides an overview of the progress made by computational and systems biologists in characterizing different cell death regulatory mechanisms that constitute the cell death network. We define the cell death network as a comprehensive decision-making mechanism that controls multiple death execution molecular circuits. This network involves multiple feedback and feed-forward loops and crosstalk among different cell death-regulating pathways. While substantial progress has been made in characterizing individual cell death execution pathways, the cell death decision network is poorly defined and understood. Certainly, understanding the dynamic behavior of such complex regulatory mechanisms can be only achieved by applying mathematical modeling and system-oriented approaches. Here, we provide an overview of mathematical models that have been developed to characterize different cell death mechanisms and intend to identify future research directions in this field.
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Dorsey, David W., and Michael D. Coovert. "Mathematical Modeling of Decision Making: A Soft and Fuzzy Approach to Capturing Hard Decisions." Human Factors: The Journal of the Human Factors and Ergonomics Society 45, no. 1 (March 2003): 117–35. http://dx.doi.org/10.1518/hfes.45.1.117.27228.
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This research focuses on a modeling approach and set of mathematical tools that were derived from research on intelligence systems, namely fuzzy system modeling. This study systematically evaluates these tools as an approach for modeling human decision making, contrasting the approach with more traditional methods based on regression. The research was conducted using experts and a simulated task environment related to allocating rewards in the form of merit pay. The results indicate that fuzzy system models generally perform as well as or better than both linear and nonlinear regression methods in terms of model fit. These results are discussed in terms of issues regarding modeling precision versus parsimony, the value of adaptive modeling techniques, empirical versus subjective approaches to model building, and individual differences in judgment strategies. Potential applications of this research include using the modeling approach studied to build higher-fidelity models that yield new insights and a better understanding of decision-making strategies and environments.
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Amoli, Mohammad Khorasani, Seyyed Milad Talebzadeh Hosseini, and Morteza Salehi. "The Necessity of Implementation of Operations Research for Managers for Decision-Making and Productivity Increase in Production." Advanced Materials Research 488-489 (March 2012): 1651–56. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1651.
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In today world, decision-making has been converted into a vital issue for managers such that in some conditions, managers are encountered with some cases which correct decision- making guarantees the survival of company. Operations research is among the sciences which in direction for decision-making, helps the managers quantitatively and using of mathematical models which are called linear programming. In this paper, it is addressed to the significance and the role of operations Research in decision-making of managers and a definition about operations research is presented and two mathematical models are presented to help the decision-making of managers to provide customers orders with the least wastes and determination of human force and over-working.
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Báquiro Guerrero, Sebastián Alberto. "Mathematical models, objectivity and free choice." Desde el Sur 15, no. 2 (April 25, 2023): 0023. http://dx.doi.org/10.21142/des-1502-2023-0023.
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Technical progress has allowed the development of increasingly powerful, fast and accurate tools, expressed in mathematical models. These are used for a more assertive and faster decision-making, lightening the individual burden of responsibility. However, these technical advances are more than just tools because what emerges from them is not simply neutral and impartial information pointing unequivocally towards the truth. In this way freedom, both individual and collective, is affected by the models and the context that encourages and sponsors these models from capitalism.
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Ayub, Bilal, Muhammad Jamaluddin Thaheem, and Fahim Ullah. "Contingency Release During Project Execution: The Contractor’s Decision-Making Dilemma." Project Management Journal 50, no. 6 (June 10, 2019): 734–48. http://dx.doi.org/10.1177/8756972819848250.
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Risk is inherent in construction projects and managed through contingency. Dynamic management of contingency escrow accounts during project execution poses decision-making challenges. Project managers use key performance indicators (KPIs) for contingency release decisions. However, their subjective mental models influence risk perception, exacerbating the decision-making dilemma. This research integrates project KPIs with future risk perception to develop a mathematical model for facilitating such decision making. Results suggest timely completion, project cost, stakeholder satisfaction, quality and safety as top KPIs, and the influence of managerial pressure as a significant decision contributor. The proposed model helps project managers in dynamic decision making for cost contingency.
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Young, W. A., and G. R. Weckman. "A Team-Compatibility Decision Support System for the National Football League." International Journal of Computer Science in Sport 19, no. 1 (July 1, 2020): 60–101. http://dx.doi.org/10.2478/ijcss-2020-0005.
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AbstractMany factors are considered when making a hiring decision in the National Football League (NFL). One difficult decision that executives must make is who they will select in the offseason. Mathematical models can be developed to aid humans in their decision-making processes because these models are able to find hidden relationships within numeric data. This research proposes the Heuristic Evaluation of Artificially Replaced Teammates (HEART) methodology, which is a mathematical model that utilizes machine learning and statistical-based methodologies to aid managers with their hiring decisions. The goal of HEART is to determine expected and theoretical contribution values for a potential candidate, which represents a player’s ability to increase or decrease a team’s forecasted winning percentage. In order to validate the usefulness of the methodology, the results of a 2007 case study were presented to subject matter experts. After analyzing the survey results statistically, five of the eight decision-making categories were found to be “very useful” in terms of the information that the methodology provided.
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Shepard, Richard B. "Regulatory Science: Mathematical vs. Statistical Models." Journal of Regulatory Science 4, no. 4 (September 16, 2016): 7–17. http://dx.doi.org/10.21423/jrs-v04n04p010.
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Natural resource companies do not object to environmental regulations that are consistent and support predictability. Consistency and predictability are critical for decision making under conditions of uncertainty. Natural ecosystems are inherently variable across a broad range of temporal and spatial scales; climate change, drought, and societal desires for sustainability make people more aware of this variability. The science used for development and enforcement of environmental regulations has not kept pace with developments in ecological theory and the analytical tools capable of describing, characterizing, classifying, and predicting natural ecosystems as well as distinguishing natural variability from anthropogenic changes.Because natural resource industries (agriculture, energy, mining) provide the base for all economic and societal activities it is critical that environmental statutes and regulations be regularly updated to use the most technically sound and legally defensible scientific knowledge and tools.Mathematical models were the tools of choice when environmental statutes and regulations were introduced, perhaps because they were successfully applied to static components of the built environment such as buildings and bridges. While their limitations for highly variable natural ecosystems were accepted then, there is now no benefit to not replacing them with statistical models.This paper describes limitations in policy and regulatory decision-making based on mathematical models and explains how the appropriate statistical models avoid the subjectivity and rigidity of the former. Changing the basis of determining and justifying policy and environmental regulations is consistent with the concepts of regulatory science applied to human health.https://doi.org/10.21423/jrs-v04n04p010 (DOI assigned 5/14/2019)
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Day, W., E. Audsley, and A. R. Frost. "An engineering approach to modelling, decision support and control for sustainable systems." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1491 (July 26, 2007): 527–41. http://dx.doi.org/10.1098/rstb.2007.2168.
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Engineering research and development contributes to the advance of sustainable agriculture both through innovative methods to manage and control processes, and through quantitative understanding of the operation of practical agricultural systems using decision models. This paper describes how an engineering approach, drawing on mathematical models of systems and processes, contributes new methods that support decision making at all levels from strategy and planning to tactics and real-time control. The ability to describe the system or process by a simple and robust mathematical model is critical, and the outputs range from guidance to policy makers on strategic decisions relating to land use, through intelligent decision support to farmers and on to real-time engineering control of specific processes. Precision in decision making leads to decreased use of inputs, less environmental emissions and enhanced profitability—all essential to sustainable systems.
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O'Connell, Redmond G., and Simon P. Kelly. "Neurophysiology of Human Perceptual Decision-Making." Annual Review of Neuroscience 44, no. 1 (July 8, 2021): 495–516. http://dx.doi.org/10.1146/annurev-neuro-092019-100200.
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The discovery of neural signals that reflect the dynamics of perceptual decision formation has had a considerable impact. Not only do such signals enable detailed investigations of the neural implementation of the decision-making process but they also can expose key elements of the brain's decision algorithms. For a long time, such signals were only accessible through direct animal brain recordings, and progress in human neuroscience was hampered by the limitations of noninvasive recording techniques. However, recent methodological advances are increasingly enabling the study of human brain signals that finely trace the dynamics of the unfolding decision process. In this review, we highlight how human neurophysiological data are now being leveraged to furnish new insights into the multiple processing levels involved in forming decisions, to inform the construction and evaluation of mathematical models that can explain intra- and interindividual differences, and to examine how key ancillary processes interact with core decision circuits.
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KOZMA, ROBERT, MARKO PULJIC, and LEONID PERLOVSKY. "MODELING GOAL-ORIENTED DECISION MAKING THROUGH COGNITIVE PHASE TRANSITIONS." New Mathematics and Natural Computation 05, no. 01 (March 2009): 143–57. http://dx.doi.org/10.1142/s1793005709001246.
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Cognitive experiments indicate the presence of discontinuities in brain dynamics during high-level cognitive processing. Non-linear dynamic theory of brains pioneered by Freeman explains the experimental findings through the theory of metastability and edge-of-criticality in cognitive systems, which are key properties associated with robust operation and fast and reliable decision making. Recently, neuropercolation has been proposed to model such critical behavior. Neuropercolation is a family of probabilistic models based on the mathematical theory of bootstrap percolations on lattices and random graphs and motivated by structural and dynamical properties of neural populations in the cortex. Neuropercolation exhibits phase transitions and it provides a novel mathematical tool for studying spatio-temporal dynamics of multi-stable systems. The present work reviews the theory of cognitive phase transitions based on neuropercolation models and outlines the implications to decision making in brains and in artificial designs.
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López, Ernesto Cervantes, and Eduardo Méndez Segura. "General Equilibrium Models in the Economy and in the Field of Development Policy." International Journal of Economics, Business and Management Research 08, no. 03 (2024): 129–38. http://dx.doi.org/10.51505/ijebmr.2024.8309.
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Economics is heavily reliant on mathematics, and with the advent of computers we can now make better decisions based on vast amounts of data. This article provides an overview of the mathematical structure underlying decision-making, which is motivated by elements of optimisation theory, which are currently supported by algorithms developed in equilibrium models in economics. We show a motivational overview of the theory of general equilibrium models and their advantage today. Although the approach of a computable general equilibrium model is a complicated task, it undoubtedly allows better decision making, especially in governmental decisions and little has been done in some developing countries.
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Chen, Yifan, Zhong Zheng, Xiaoya Wang, Ziqi Meng, and Jiayao Li. "Automatic pricing and replenishment decision-making for vegetable products based on optimization models." Theoretical and Natural Science 34, no. 1 (May 10, 2024): 284–90. http://dx.doi.org/10.54254/2753-8818/34/20241181.
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Addressing the replenishment and pricing issues of vegetable products is crucial for ensuring product quality and freshness, optimizing sales combinations, refining pricing strategies, and enhancing operational efficiency. Through scientific data analysis and decision-making, supermarkets can better meet consumer demands, enhance competitiveness, and achieve sustainable development. This paper discusses the complex issues of procurement and pricing of fresh vegetable products in current supermarkets. It employs methods such as hierarchical clustering analysis, Topsis evaluation, and optimization models to construct data models, establishing multiple models to address replenishment and pricing decision-making from various perspectives. The research indicates, firstly, the paper categorizes vegetable products into four clusters, explores complementary and substitute products within them, and discovers that reasonable sales combinations among different types of single products can mutually promote sales, leading to higher economic benefits for supermarkets. Secondly, the paper derives a mathematical model describing the relationship between total profit, total sales volume of individual products, and pricing. This model provides valuable recommendations for supermarkets replenishment and pricing decisions, ensuring practical implementation of pricing and replenishment plans. Thirdly, the paper establishes a model for maximizing profits under constant replenishment quantities, assisting supermarkets in formulating more scientific replenishment plans for individual products within a limited number of available items. By judiciously applying the innovative mathematical models presented in this paper, supermarkets can obtain reliable market analysis and make corresponding replenishment and pricing decisions.
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Salutina, T. Y., M. F. Gumerov, A. R. Kaberova, and G. P. Platunina. "DECISION-MAKING IN PRICE POLICY MANAGEMENT IN THE SPEECH ANALYTICS MARKET." Beneficium, no. 2 (2023): 20–27. http://dx.doi.org/10.34680/beneficium.2023.2(47).20-27.
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The article is devoted to adapting general mathematical models of the software markets for describing the mar-ket of a specific product. These are the technologies of artificial intellect in speech analytics. The purpose of this study is to create a modeling instrumentation for pricing the technologies of speech analytics in companies which enter this mar-ket. The purpose also includes recommendations provided with the price policy. The object of the study is the Russian market of speech analytics technologies. The subject of the study are the prices of this product in companies which enter the market being explored. In this studying the authors use classical methods of economical and mathematical modeling the markets with different competitive levels (monopoly, duopoly, oligopoly, monopolistic competition). The results of the study are the foundations of prices for the companies which enter the speech analytics market. These prices are based on three kinds of economical mathematical models: regression, rating and marginal indicators. All three kinds of models lead to one recommendation. A company entering the speech analytics market, when setting a price, should focus less on the quality of speech recognition (this factor has little effect on the price in all three models), and more on the availability of additional options: the more of them, the higher the company’s ability to set the price at a level closer to the market leaders.
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Коротких, И. Н., М. В. Фролов, В. Н. Самодай, М. Д. Михайлова, Л. И. Садова, and О. Л. Бельских. "MATHEMATICAL, SIMULATION AND LOGICAL MODELING OF DECISION-MAKING PROCESSES IN GYNECOLOGY." СИСТЕМНЫЙ АНАЛИЗ И УПРАВЛЕНИЕ В БИОМЕДИЦИНСКИХ СИСТЕМАХ 22, no. 4 (December 20, 2023): 113–18. http://dx.doi.org/10.36622/vstu.2023.22.4.018.
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В статье рассматриваются методы интеллектуальной поддержки принимаемых решений в процессе диагностирования и выбора лечения. Применяются методы имитационного эксперимента для организации и алгоритмизации диалогового режима в ускоренном и реальном масштабе времени на основе адаптивных алгоритмов, математических и логических моделей, использующих наравне с объективной текущей информацией априорную информацию, поступающую от ЛВ, и экспертную информацию. Таким образом, располагая прогностическими моделями однородных компонент, ЛВ вместе с исследователем операций имеет возможность "проиграть" на моделях методом имитационного эксперимента возможные варианты лечения, сравнивать их, предполагать исход лечения. При этом отработка вариантов лечения может проводиться либо только по информации от ЛВ, либо по информации от ЛВ с использованием адаптивного подхода. На эффективность выбора тактики лечения оказывает также влияние использования рациональной организации имитационного эксперимента, так как его целью является обучение ЛВ на моделях и получение дополнительной информации о процессе перед началом лечения, что очень существенно при принятии решений в условиях неполной априорной информации. При организации имитационного эксперимента имеют место три уровня, представляющих собой в совокупности итеративный многоуровневый процесс прогнозирования исхода лечения The article discusses methods of intellectual decision support in the process of diagnosis and treatment selection. Simulation experiment methods are used to organize and algorithmize the dialogue mode in accelerated and real time based on adaptive algorithms, mathematical and logical models that use, along with objective current information, a priori information coming from the LP and expert information. Thus, having prognostic models of homogeneous components, the LP, together with the surgery researcher, has the opportunity to “play out” possible treatment options on models using a simulation experiment, compare them, and predict the outcome of treatment. In this case, development of treatment options can be carried out either only based on information from the PL, or based on information from the PL using an adaptive approach. The effectiveness of the choice of treatment tactics is also influenced by the use of a rational organization of a simulation experiment, since its goal is to train the drug using models and obtain additional information about the process before starting treatment, which is very important when making decisions in conditions of incomplete a priori information. When organizing a simulation experiment, there are three levels, which together represent an iterative multi-level process of predicting the outcome of treatment
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Veronskaya, M. V., and E. D. Trushkovskaya. "The main tasks and mechanisms of the decision-making process for the distribution of budget resources of the city." Вестник гражданских инженеров 18, no. 5 (2021): 160–66. http://dx.doi.org/10.23968/1999-5571-2021-18-5-160-166.
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The paper considers the relevant for the present time task of making rational decisions at forming the expenditure part of the budget, when there are enough financial opportunities not only for critical areas of financing, but also for ensuring a decent standard of living for citizens. The main mechanisms of decision-making regarding the allocation of expenditures, which involves the use of mathematical optimization models, are considered. It is investigated where these models are appropriate to use, and for which tasks they are not suitable. The information support of the entire decision-making process necessary for the application of optimization methods is analyzed.
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Ivanov, Aleksandr K., Aleksandr L. Savkin, Dmitriy M. Yastrebov, and Sergey S. Shumilov. "DECISION-MAKING BASED ON THE RECEPTOR-LIGAND INTERACTION METHODS." Автоматизация процессов управления 3, no. 65 (2021): 4–18. http://dx.doi.org/10.35752/1991-2927-2021-3-65-4-18.
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The article deals with the mathematical models of decision-making in the management organization systems. The models are based on the receptor-ligand interaction methods studied in the biokinetics. The article describes an interaction between initial data coming to the organization system and knowledge database generated on the basis of operation experience. The combination of matching pairs creates decisions for resolving problems and achieving the goals. The interaction may take one or more stages. For one stage the models were developed in the form of differential equations when the starting amount of data is not equal to the one in knowledge database. When the amounts are equal, the model is represented as Riccati equation in a special form solving by an indefinite coefficient. The article considers cases when information recourses of initial data and the ones of knowledge database are not uniquely defined. The similar models are developed for two stages of interaction. The article gives the examples of calculation of information recourses amount for a decision in case of different ratios of initial data/ knowledge database.
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Kumratova, A. M., E. V. Popova, V. V. Aleshchenko, A. A. Bykov, and A. K. Bashieva. "Decision tree as a tool for implementing a scenario approach for multi-level predictive models." IOP Conference Series: Earth and Environmental Science 839, no. 3 (September 1, 2021): 032050. http://dx.doi.org/10.1088/1755-1315/839/3/032050.
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Abstract The paper presents a decision-making method for a quantitative income estimation depending on the intensity of the future tourist flow, as a complex indicator reflecting the level of the tourist market in a region or in a separate object (a hotel complex, sanatorium, tourist base, etc.). The authors proposed to use a three-level economic and mathematical model as a practical implementation of the hotel complex room stock management process. Each its level corresponds to a specific task. At the first level it is a pre-forecast study, substantiation and selection of forecasting models. At the second it is a forecast model and the quantitative value of the predicted indicator. At the third level it is a model tohelp a decision maker (DM) with decision making, i.e., a decision tree is applied as a tool. Thus, the authors present a complete system of models and methods of decision support. The results of pre-forecast analysis, development of predictive models, building, adaptation and implementation of top-level economic and mathematical models will help decision makers to make effective management decisions. There by the maneuver material resources, choose sales technologies and search for economic solutions, including in tourism recreational production activities.
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Sniedovich, Moshe. "The Art and Science of Modeling Decision-Making Under Severe Uncertainty." Decision Making in Manufacturing and Services 1, no. 2 (October 11, 2007): 111–36. http://dx.doi.org/10.7494/dmms.2007.1.2.111.
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For obvious reasons, models for decision-making under severe uncertainty are austere. Simply put, there is precious little to work with under these conditions. This fact highlights the great importance of utilizing in such cases the ingredients of the mathematical model to the fullest extent, which in turn brings under the spotlight the art of mathematical modeling. In this discussion we examine some of the subtle considerations that are called for in the mathematical modeling of decision-making under severe uncertainty in general, and worst-case analysis in particular. As a case study we discuss the lessons learnt on this front from the Info-Gap experience.
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Almarri, B., and A. A. Azzam. "Energy Saving via a Minimal Structure." Mathematical Problems in Engineering 2022 (June 15, 2022): 1–6. http://dx.doi.org/10.1155/2022/5450344.
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Not only mathematical statistics, differential equations, and mathematical models were used to analyze and reduce data, but a rough set model is also employed in medical, engineering, and other fields to analyze and reduce them. The goal of this paper is to introduce a minimal structure concept to produce new rough set models and show that it is suitable for analyzing most real-life problems, reduction of attributes, and decision making. We examine the effectiveness of the following method in the problem of electric power generators and decision making. We also offer a comparison of our method and Pawlak’s method. Finally, the variable precision model improves the accuracy of decision making.
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Akram, Muhammad, Danish Saleem, and Talal Al-Hawary. "Spherical Fuzzy Graphs with Application to Decision-Making." Mathematical and Computational Applications 25, no. 1 (January 20, 2020): 8. http://dx.doi.org/10.3390/mca25010008.
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In a network model, the evaluation information given by decision makers are occasionally of types: yes, abstain, no, and refusal. To deal with such problems, we use mathematical models based on picture fuzzy sets. The spherical fuzzy model is more versatile than the picture fuzzy model as it broadens the space of uncertain and vague information, due to its outstanding feature of vast space of participation of acceptable triplets. Graphs are a mathematical representation of networks. Thus to deal with many real-world phenomena represented by networks, spherical fuzzy graphs can be used to model different practical scenarios in a more flexible manner than picture fuzzy graphs. In this research article, we discuss two operations on spherical fuzzy graphs (SFGs), namely, symmetric difference and rejection; and develop some results regarding their degrees and total degrees. We describe certain concepts of irregular SFGs with several important properties. Further, we present an application of SFGs in decision making.
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Zaytsev, Sergey V. "CONSTRUCTION OF GAME-THEORETIC MODELS IN THEЧMODERN ECONOMY OF ENTERPRISES AND METHODS OF THEIR MEASUREMENTS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 6/2, no. 138 (2023): 178–87. http://dx.doi.org/10.36871/ek.up.p.r.2023.06.02.020.
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In the conditions of the modern economy, a fairly large number of game-theoretic models are allocated, depending on the scope of application, the amount and significance of resources, as well as the state of the external environment and the state of measurements. Measurements occupy a special place in the compilation and study of game-theoretic models. Game theory is an applied interdisciplinary science that creates decision-making models in various fields in order to develop optimal solutions to a conflict or problem situation with the most profitable use of resources or information. The relevance of the topic of this article is due to the growing role of the creative approach in the economy or the development of the so-called creative economy, when a decision-maker uses his experience and mathematical and instrumental models to make a decision or create a successful project in order to enter the market. The relevance of the research topic is due to the need to improve the quality of decisions through various intelligent methods and measuring instruments for more successful decisions, as well as in order to increase the overall efficiency of decision-making at enterprises. In addition, the relevance is due to the insufficient number of studies on the theoretical component of economic and mathematical models in the digital economy.
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Rius-Sorolla, G., J. Maheut, S. Estellés-Miguel, and J. P. Garcia-Sabater. "Coordination mechanisms with mathematical programming models for decentralized decision-making: a literature review." Central European Journal of Operations Research 28, no. 1 (November 8, 2018): 61–104. http://dx.doi.org/10.1007/s10100-018-0594-z.
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Bertsimas, Dimitris, John Silberholz, and Thomas Trikalinos. "Optimal healthcare decision making under multiple mathematical models: application in prostate cancer screening." Health Care Management Science 21, no. 1 (September 17, 2016): 105–18. http://dx.doi.org/10.1007/s10729-016-9381-3.
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