864 research outputs found

    Current issues of the management of socio-economic systems in terms of globalization challenges

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    The authors of the scientific monograph have come to the conclusion that the management of socio-economic systems in the terms of global challenges requires the use of mechanisms to ensure security, optimise the use of resource potential, increase competitiveness, and provide state support to economic entities. Basic research focuses on assessment of economic entities in the terms of global challenges, analysis of the financial system, migration flows, logistics and product exports, territorial development. The research results have been implemented in the different decision-making models in the context of global challenges, strategic planning, financial and food security, education management, information technology and innovation. The results of the study can be used in the developing of directions, programmes and strategies for sustainable development of economic entities and regions, increasing the competitiveness of products and services, decision-making at the level of ministries and agencies that regulate the processes of managing socio-economic systems. The results can also be used by students and young scientists in the educational process and conducting scientific research on the management of socio-economic systems in the terms of global challenges

    Towards a human-centric data economy

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    Spurred by widespread adoption of artificial intelligence and machine learning, “data” is becoming a key production factor, comparable in importance to capital, land, or labour in an increasingly digital economy. In spite of an ever-growing demand for third-party data in the B2B market, firms are generally reluctant to share their information. This is due to the unique characteristics of “data” as an economic good (a freely replicable, non-depletable asset holding a highly combinatorial and context-specific value), which moves digital companies to hoard and protect their “valuable” data assets, and to integrate across the whole value chain seeking to monopolise the provision of innovative services built upon them. As a result, most of those valuable assets still remain unexploited in corporate silos nowadays. This situation is shaping the so-called data economy around a number of champions, and it is hampering the benefits of a global data exchange on a large scale. Some analysts have estimated the potential value of the data economy in US$2.5 trillion globally by 2025. Not surprisingly, unlocking the value of data has become a central policy of the European Union, which also estimated the size of the data economy in 827C billion for the EU27 in the same period. Within the scope of the European Data Strategy, the European Commission is also steering relevant initiatives aimed to identify relevant cross-industry use cases involving different verticals, and to enable sovereign data exchanges to realise them. Among individuals, the massive collection and exploitation of personal data by digital firms in exchange of services, often with little or no consent, has raised a general concern about privacy and data protection. Apart from spurring recent legislative developments in this direction, this concern has raised some voices warning against the unsustainability of the existing digital economics (few digital champions, potential negative impact on employment, growing inequality), some of which propose that people are paid for their data in a sort of worldwide data labour market as a potential solution to this dilemma [114, 115, 155]. From a technical perspective, we are far from having the required technology and algorithms that will enable such a human-centric data economy. Even its scope is still blurry, and the question about the value of data, at least, controversial. Research works from different disciplines have studied the data value chain, different approaches to the value of data, how to price data assets, and novel data marketplace designs. At the same time, complex legal and ethical issues with respect to the data economy have risen around privacy, data protection, and ethical AI practices. In this dissertation, we start by exploring the data value chain and how entities trade data assets over the Internet. We carry out what is, to the best of our understanding, the most thorough survey of commercial data marketplaces. In this work, we have catalogued and characterised ten different business models, including those of personal information management systems, companies born in the wake of recent data protection regulations and aiming at empowering end users to take control of their data. We have also identified the challenges faced by different types of entities, and what kind of solutions and technology they are using to provide their services. Then we present a first of its kind measurement study that sheds light on the prices of data in the market using a novel methodology. We study how ten commercial data marketplaces categorise and classify data assets, and which categories of data command higher prices. We also develop classifiers for comparing data products across different marketplaces, and we study the characteristics of the most valuable data assets and the features that specific vendors use to set the price of their data products. Based on this information and adding data products offered by other 33 data providers, we develop a regression analysis for revealing features that correlate with prices of data products. As a result, we also implement the basic building blocks of a novel data pricing tool capable of providing a hint of the market price of a new data product using as inputs just its metadata. This tool would provide more transparency on the prices of data products in the market, which will help in pricing data assets and in avoiding the inherent price fluctuation of nascent markets. Next we turn to topics related to data marketplace design. Particularly, we study how buyers can select and purchase suitable data for their tasks without requiring a priori access to such data in order to make a purchase decision, and how marketplaces can distribute payoffs for a data transaction combining data of different sources among the corresponding providers, be they individuals or firms. The difficulty of both problems is further exacerbated in a human-centric data economy where buyers have to choose among data of thousands of individuals, and where marketplaces have to distribute payoffs to thousands of people contributing personal data to a specific transaction. Regarding the selection process, we compare different purchase strategies depending on the level of information available to data buyers at the time of making decisions. A first methodological contribution of our work is proposing a data evaluation stage prior to datasets being selected and purchased by buyers in a marketplace. We show that buyers can significantly improve the performance of the purchasing process just by being provided with a measurement of the performance of their models when trained by the marketplace with individual eligible datasets. We design purchase strategies that exploit such functionality and we call the resulting algorithm Try Before You Buy, and our work demonstrates over synthetic and real datasets that it can lead to near-optimal data purchasing with only O(N) instead of the exponential execution time - O(2N) - needed to calculate the optimal purchase. With regards to the payoff distribution problem, we focus on computing the relative value of spatio-temporal datasets combined in marketplaces for predicting transportation demand and travel time in metropolitan areas. Using large datasets of taxi rides from Chicago, Porto and New York we show that the value of data is different for each individual, and cannot be approximated by its volume. Our results reveal that even more complex approaches based on the “leave-one-out” value, are inaccurate. Instead, more complex and acknowledged notions of value from economics and game theory, such as the Shapley value, need to be employed if one wishes to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms. However, the Shapley value entails serious computational challenges. Its exact calculation requires repetitively training and evaluating every combination of data sources and hence O(N!) or O(2N) computational time, which is unfeasible for complex models or thousands of individuals. Moreover, our work paves the way to new methods of measuring the value of spatio-temporal data. We identify heuristics such as entropy or similarity to the average that show a significant correlation with the Shapley value and therefore can be used to overcome the significant computational challenges posed by Shapley approximation algorithms in this specific context. We conclude with a number of open issues and propose further research directions that leverage the contributions and findings of this dissertation. These include monitoring data transactions to better measure data markets, and complementing market data with actual transaction prices to build a more accurate data pricing tool. A human-centric data economy would also require that the contributions of thousands of individuals to machine learning tasks are calculated daily. For that to be feasible, we need to further optimise the efficiency of data purchasing and payoff calculation processes in data marketplaces. In that direction, we also point to some alternatives to repetitively training and evaluating a model to select data based on Try Before You Buy and approximate the Shapley value. Finally, we discuss the challenges and potential technologies that help with building a federation of standardised data marketplaces. The data economy will develop fast in the upcoming years, and researchers from different disciplines will work together to unlock the value of data and make the most out of it. Maybe the proposal of getting paid for our data and our contribution to the data economy finally flies, or maybe it is other proposals such as the robot tax that are finally used to balance the power between individuals and tech firms in the digital economy. Still, we hope our work sheds light on the value of data, and contributes to making the price of data more transparent and, eventually, to moving towards a human-centric data economy.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Georgios Smaragdakis.- Secretario: Ángel Cuevas Rumín.- Vocal: Pablo Rodríguez Rodrígue

    Regulating competition in the digital network industry: A proposal for progressive ecosystem regulation

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    The digital sector is a cornerstone of the modern economy, and regulating digital enterprises can be considered the new frontier for regulators and competition authorities. To capture and address the competitive dynamics of digital markets we need to rethink our (competition) laws and regulatory strategies. The thesis develops new approaches to regulating digital markets by viewing them as part of a network industry. By combining insights from our experiences with existing regulation in telecommunications with insights from economics literature and management theory, the thesis concludes by proposing a new regulatory framework called ‘progressive ecosystem regulation’. The thesis is divided in three parts and has three key findings or contributions. The first part explains why digital platforms such as Google’s search engine, Meta’s social media platforms and Amazon’s Marketplace are prone to monopolization. Here, the thesis develops a theory of ‘digital natural monopoly’, which explains why competition in digital platform markets is likely to lead to concentration by its very nature.The second part of the thesis puts forward that competition in digital markets persists, even if there is monopoly in a market. Here, the thesis develops a conceptual framework for competition between digital ecosystems, which consists of group of actors and products. Digital enterprises compete to carve out a part of the digital network industry where they can exert control, and their strong position in a platform market can be used offensively or defensively to steer competition between ecosystems. The thesis then sets out four phases of ecosystem competition, which helps to explain when competition in the digital network industry is healthy and when it is likely to become problematic.The third and final part of the thesis brings together these findings and draws lessons from our experiences of regulating the network industry for telecommunications. Based on the insights developed in the thesis it puts forward a proposal for ‘progressive ecosystem regulation’. The purpose of this regulation is to protect and empower entrants from large digital ecosystems so that they can develop new products and innovate disruptively. This regulatory framework would create three regulatory pools: a heavily regulated, lightly regulated and entrant pool. The layered regulatory framework allows regulators to adjust who receives protection under the regulation and who faces the burdens relatively quickly, so that the regulatory framework reflects the fast pace of innovation and changing nature of digital markets. With this proposal, the thesis challenges and enriches our existing notions on regulation and specifically how we should regulate digital markets

    Regulating competition in the digital network industry: A proposal for progressive ecosystem regulation

    Get PDF
    The digital sector is a cornerstone of the modern economy, and regulating digital enterprises can be considered the new frontier for regulators and competition authorities. To capture and address the competitive dynamics of digital markets we need to rethink our (competition) laws and regulatory strategies. The thesis develops new approaches to regulating digital markets by viewing them as part of a network industry. By combining insights from our experiences with existing regulation in telecommunications with insights from economics literature and management theory, the thesis concludes by proposing a new regulatory framework called ‘progressive ecosystem regulation’. The thesis is divided in three parts and has three key findings or contributions. The first part explains why digital platforms such as Google’s search engine, Meta’s social media platforms and Amazon’s Marketplace are prone to monopolization. Here, the thesis develops a theory of ‘digital natural monopoly’, which explains why competition in digital platform markets is likely to lead to concentration by its very nature.The second part of the thesis puts forward that competition in digital markets persists, even if there is monopoly in a market. Here, the thesis develops a conceptual framework for competition between digital ecosystems, which consists of group of actors and products. Digital enterprises compete to carve out a part of the digital network industry where they can exert control, and their strong position in a platform market can be used offensively or defensively to steer competition between ecosystems. The thesis then sets out four phases of ecosystem competition, which helps to explain when competition in the digital network industry is healthy and when it is likely to become problematic.The third and final part of the thesis brings together these findings and draws lessons from our experiences of regulating the network industry for telecommunications. Based on the insights developed in the thesis it puts forward a proposal for ‘progressive ecosystem regulation’. The purpose of this regulation is to protect and empower entrants from large digital ecosystems so that they can develop new products and innovate disruptively. This regulatory framework would create three regulatory pools: a heavily regulated, lightly regulated and entrant pool. The layered regulatory framework allows regulators to adjust who receives protection under the regulation and who faces the burdens relatively quickly, so that the regulatory framework reflects the fast pace of innovation and changing nature of digital markets. With this proposal, the thesis challenges and enriches our existing notions on regulation and specifically how we should regulate digital markets

    Epistemic Planning for Heterogeneous Robotic Systems

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    In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within these application spaces, communication is often unreliable, causing inefficiencies or outright failures to arise in most MRS algorithms. Many researchers tackle this problem by requiring all robots to either maintain communication using proximity constraints or assuming that all robots will execute a predetermined plan over long periods of disconnection. The latter method allows for higher levels of efficiency in a MRS, but failures and environmental uncertainties can have cascading effects across the system, especially when a mission objective is complex or time-sensitive. To solve this, we propose an epistemic planning framework that allows robots to reason about the system state, leverage heterogeneous system makeups, and optimize information dissemination to disconnected neighbors. Dynamic epistemic logic formalizes the propagation of belief states, and epistemic task allocation and gossip is accomplished via a mixed integer program using the belief states for utility predictions and planning. The proposed framework is validated using simulations and experiments with heterogeneous vehicles

    Fundamentals of Business

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    Fundamentals of Business, fourth edition (2023) is an open education resource intended to serve as a no-cost, faculty-customizable primary text for one-semester undergraduate introductory business courses. It covers the following topics in business: Teamwork; economics; ethics; entrepreneurship; business ownership, management, and leadership; organizational structures and operations management; human resources and motivating employees; managing in labor union contexts; marketing and pricing strategy; hospitality and tourism, accounting and finance, personal finances, and technology in business
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