The paper examines prerequisites and assumptions of the classical Bass innovation diffusion model with the aim of applying it in modeling of relevant stochastic processes related to the pandemic. The Bass model has proven its versatility and applicability to various environments. A thorough mathematical substantiation of the model properties is presented based on theories of evolutionary equations and stochastic processes for its further development, as well as search for uncertainty parameters and observable variables. The paper provides realistic estimation results of the Bass model parameters for vaccination in Ukraine andBelarus on weekly data of the first half of 2021. Similar studies are suggested for other countries, as well as regions and districts of Ukraine.
According to the data of 2017–2018, when capital investments of the Ukraineʼs districts were made public, two clusters were identified – production leaders of Luhansk region. According to the economic efficiency of industry and services (comparable agricultural data were not measured by the State Statistics Service), the stable leaders of Luhansk region are adjacent city of Rubizhne, Kreminsky and Novoaydarky districts, as well as adjacent Starobilsky, Belovodsky, Popasnyansky districts, where in the second and third quarters of 2018 indicators have improved. One of the clusters includes all higher education institutions of Luhansk region, except for institutions of Severodonestk and Lysyschansk, where wages are higher than the average in the regionʼs districts. Another cluster is a border cluster based on Novopskovsky district, as well as adjacent border districts – Markivsky and Milovsky, where in the abovementioned second and third quarters of 2018 indicators have deteriorated. It should be noted that the quarterly industry data for Novopskovsky and Milovsky districts were generated based on their quarterly population data and the quarterly industry aggregate data; the quarterly capital investments data for Milovsky district were generated in a similar way. After generation of the computing data for all the fifteen districts available, estimates of the Cobb–Douglas function parameters and regression residuals for the six quarters of 2017–2018 were found. For the contemporary Luhansk region, the labor production factor is much more important than the capital production factor, which emphasizes the role of human capital and related intangible assets. The results obtained lead to assumption that economic efficiency in the large cities of Luhanshchyna in general is lower than in the clusters identified. It means that Luhansk region needs new industrialization based on modern information and communication technologies, intangible assets, and human capital. Such industrialization and digitalization needs a comprehensive international cooperation. The export conclusions for Luhansk region as of 2017 do not differ from the conclusion as of 2011: it is promising to start exporting by product groups of footwear, leather goods, essential oils, meat, fish, watercraft.
Introduction. Optimization can be applied in developing profitability management tools for a cloud service broker working according to a certain business model. On behalf of the managing telecommunications holding company (telecommunications operator), this broker integrates, aggregates and configures software and data storage services of third-party Internet software vendors. Such a broker receives only fixed commissions from this company, based on the subscription fee, but does not pay royalties to an Internet software vendor and does not receive payments from the sale of service packages. The purpose. The cloud broker faces the problem of limited human resources required to carry out the relevant legal, technical and economic activities. In addition, the broker faces the problem of uncertainty in sales, service prices, the share of resource use, or the risk of losing operational and financial goals. Results. To run a broker?s business efficiently, one needs to find services and their bundles that increase profitability and reduce financial risk by solving certain optimization problems. Information on such services is needed to support negotiations on fixed and variable commissions, as well as to prioritize services and their packages to be provided. Thus, for the cloud services broker, both profitability management tools and services portfolio development tools are useful. In general, a cloud service broker is an organization that negotiates the relationships between cloud service clients and Internet software vendors. Cloud broker can be created on the basis of different business models regarding the type of service (platform, infrastructure, software), type of clients (enterprise, household), functions performed (identity management, accounting, billing, location, etc.), the degree of rebranding, measures of aggregation of services and other criteria. Conclusions. Different cloud brokers have different attitudes to choice of important solutions for their businesses. Solutions can relate to pricing, capacity planning and utilization in combination with service quality, security, scalability and other issues. Keywords: optimization, portfolio, uncertainty, Boolean variables, revenue generation.
Market intermediaries coordinate the actions of buyers and sellers. Digital platforms, including the case Platform as a Service (PaaS), take the roles of market intermediaries with some novel ones. In a multi-intermediary world, consumers and suppliers continue to incur search costs due to reacting to multiple intermediaries. Consumers and suppliers discount future net gains due to monetization of search time costs. Consumers have different levels of willingness to pay, suppliers have different opportunity costs, and intermediary firms have different transaction costs. These firms set both bid prices and ask prices. Consumers look for firms that offer a lower purchase price, and suppliers look for firms that offer a higher sale price. Due to such heterogeneity and search costs, the market equilibrium is a distribution of sale prices and a distribution of purchase prices. This equilibrium depends on the discount rate of consumers and suppliers, for whom a higher discount rate stands for a decrease in activity (the number of active consumers and suppliers), while a higher discount rate means an increase in the activity of intermediary firms (the number of active firms): a higher discount rate increases the costs of time-consuming search for consumers and suppliers. Intermediary firms then raise their purchase prices and lower their sale prices because consumers and suppliers are willing to pay a premium to avoid further search, thus increasing the returns to intermediation for firms and stimulating growth in the number of intermediary firms active at the market equilibrium. Thus, the discount rate determines the search costs. When this rate falls to zero, the search costs are eliminated and the relationships between the size of the bid-ask spread and transaction costs are revealed. Then the Walras equilibrium will be the limiting case of the intermediated market when transaction costs fall, and the supply and demand model can be considered an ideal case compatible with the market under consideration at the presence of search costs and price-setting firms. The cloud technologies are saving the general search costs. The two basic cases of providers for such technologies are monopoly and competition.
Introduction. Outbreaks of infectious diseases and the COVID-19 pandemic in particular pose a serious public health challenge. The other side of the challenge is always opportunity, and today such opportunities are information technology, decision making systems, best practices of proactive management and control based on modern methods of data analysis (data driven decision making) and modeling. The article reviews the prospects for the use of publicly available software in modeling epidemiological trends. Strengths and weaknesses, main characteristics and possible aspects of application are considered. The purpose of the article is to review publicly available health software. Give situations in which one or another approach will be useful. Segment and determine the effectiveness of the underlying models. Note the prospects of high-performance computing to model the spread of epidemics. Results. Although deterministic models are ready for practical use without specific additional settings, they lose comparing to other groups in terms of their functionality. To obtain evaluation results from stochastic and agentoriented models, you first need to specify the epidemic model, which requires deeper knowledge in the field of epidemiology, a good understanding of the statistical basis and the basic assumptions on which the model is based. Among the considered software, EMOD (Epidemiological MODelling software) from the Institute of Disease Modeling is a leader in functionality. Conclusions. There is a free access to a relatively wide set of software, which was originally developed by antiepidemiological institutions for internal use in decision-making, however was later opened to the public. In general, these programs have been adapted to increase their practical application. Got narrowed focus on potential issues. The possibility of adaptive use was provided. We can note the sufficient informativeness and convenience of using the software of the group of deterministic methods. Also, such models have a rather narrow functional focus. Stochastic models provide more functionality, but lose some of their ease of use. We have the maximum functionality from agentoriented models, although for their most effective use you need to have the appropriate skills to write program code. Keywords: epidemiological software, deterministic modeling, stochastic modeling, agentoriented mode-ling, high performance computing, decision making systems.
Introduction. Health care is characterized by the fact that it belongs to the major state functions and the main kinds of economic activity at the same time, as well as the fact that in contemporary conditions it provides dual-use products – use for both conventional and defense against the latest biothreats. In the course of reforming this state function in Ukraine, the main financing is provided through the National Health Service of Ukraine, where management changes relatively frequently. The purpose. Protection against biohazards, health care, health insurance requires systemic resilience and integrated management based on modern information and communication technologies. Such technologies for social insurance have been successfully developed and implemented by the V.M.Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine. Results. A specific example of Zaporizhia region shows which health facilities are without proper government support and how to anticipate and manage distributed networks on big data. In all the above issues of protection against biohazards, health care, health insurance, government institutions cannot make rational decisions without comprehensive and accurate assessment of future gains (and losses) caused by the implementation of a particular project, as well as a comparison of such gains with the present value of costs associated with this project. It is important for decision makers to measure gains and costs in the same units applying the known principle «Who canʼt measure cannot manage». Since project costs are usually measured in monetary terms, it makes sense to measure all gains in monetary terms as well. Different approaches to economic assessment of health status compare the benefits from a medical intervention with the costs of that intervention. Conclusions. Gains from medical intervention can be measured in physical units on a one-dimensional scale, monetary units, units of cardinal utility function, reflecting the multidimensional concept of health via the scalar index or key performance indicator. Nowadays multiple dimensions mentioned are gradually developing into big data for each node and link of the health care grid. Keywords: biothreats, system resilience, social insurance, health insurance, big data, distributed networks.
The interaction of the government financial system, the state banking system, and the investment system of renewable photo-voltaic (PV) power generation equipment can lead to sustainable strategies of these three parties (including government subsi-dies and bank loans) in the distributed state PV-market depending on its level of development. However, the instability of power output, caused by the variability and changing nature of renewable energy sources, poses challenges for large-scale power dispatch. In addition, the development of the PV-industry has been constrained by a long period of return on investment in solar photovoltaics and the need for large initial investments. With the rapid development of the sharing economy, the provision of financial support and the sharing of investment risks among investors in the PV-energy have become key means of promoting the PV-industry. State incentive policy was considered an effective approach to significant promotion of PV-systems. Government subsidies reduce the need for large initial investments, and market mechanisms, such as feed-in tariffs and tax rebates, increase return on investment and reduce payback periods. In addition, bank loans are considered another major source of external financing for the development of the PV-industry. Third-party financing with appropriate risk-sharing is considered an effective approach to promote the use of photovoltaic technologies. As government subsidies put pressure on the state budget and bank loans require banks to take significant credit risks, there are clear barriers to governments and banks supporting the develop-ment of the PV-industry. By 2022, the issues of computing such targeted government subsidies and bank loans with limited credit risks, which maximize incentives for the diffusion of PV-technologies, remain underdeveloped. The current important issues for suggested numerical studying and modeling are: can government subsidies and bank loans significantly contribute to the diffusion of PV-installations at various levels of the PV-market development; what evolutionarily stable states will be formed at different levels of the PV-market development; how the volume of government subsidies, the share of bank loans, the capacity of PV-installations by investors will affect the evolutionary trajectories of the all PV-market parameters and the transformation of various evolutionarily stable states. To do this, numerical modeling is performed to study the dynamic evolutionary trajectories at different levels of the PV-market development.
Critical infrastructure of interdependent modern sectors is increasingly relying on cyber systems and cyber infrastructures, which are characterized by growing risks of their cyber components, including cyberphysical subsystems. Therefore, cybersecurity is important for the protection of critical infrastructure. The search for cost-effective ways to increase or improve the security of cyber infrastructure is based on optimization models and methods of cyber infrastructure stability, safety, and reliability. These models and methods have different fields of application and different directions, not necessarily focused on the cyber infrastructure resilience. The growing role of information and communication technologies has influenced the concept of security and the nature of war. Many critical infrastructures (airports, hospitals, oil pipelines) have become potentially vulnerable to organized cyber attacks. Today, the implementation of the major state function of defense and security largely depends on the successful use of information and communication technologies as modern competitive (final and intermediate) dual-use products used by different people for different purposes. Game theory is increasingly used to assess strategic interactions between attackers and defenders in cyberspace. Game research and modeling combinations are combined to study the security of cyberspace. In cyberspace, the arsenal of weapons is built by finding more vulnerabilities in the defense of the target. Vulnerability is a weakness in the security procedures of the system, the design of the system or its implementation, as well as in the organization of internal control, which may be used by the source of the threat. The dynamic nature of vulnerabilities means that they are constantly changing over time. Detecting a vulnerability by a defender reduces the effectiveness of the attacker’s cyber weapon, which exploits the vulnerability, and increases the target protection. Game theory has been applied to many issues, including resource allocation, network security, and human cooperation. In cyberspace, there is often a placement game where the attacker and the defender decide where to allocate their respective resources. Defender’s resources can be security infrastructure (firewalls), finance, training. For example, a network administrator might look for a resource allocation that minimizes the risk of (cyber) attacks and at the same time protects against cyberattacks. The attacker has limited resources and is at risk of being tracked down and punished. The problem of resource allocation in cyberspace can be formulated as a game-theoretic problem, taking into account the concept of common knowledge and the problem of uncertain observability.
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