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.
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.
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.
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