2,245 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Spectrum auctions: designing markets to benefit the public, industry and the economy

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    Access to the radio spectrum is vital for modern digital communication. It is an essential component for smartphone capabilities, the Cloud, the Internet of Things, autonomous vehicles, and multiple other new technologies. Governments use spectrum auctions to decide which companies should use what parts of the radio spectrum. Successful auctions can fuel rapid innovation in products and services, unlock substantial economic benefits, build comparative advantage across all regions, and create billions of dollars of government revenues. Poor auction strategies can leave bandwidth unsold and delay innovation, sell national assets to firms too cheaply, or create uncompetitive markets with high mobile prices and patchy coverage that stifles economic growth. Corporate bidders regularly complain that auctions raise their costs, while government critics argue that insufficient revenues are raised. The cross-national record shows many examples of both highly successful auctions and miserable failures. Drawing on experience from the UK and other countries, senior regulator Geoffrey Myers explains how to optimise the regulatory design of auctions, from initial planning to final implementation. Spectrum Auctions offers unrivalled expertise for regulators and economists engaged in practical auction design or company executives planning bidding strategies. For applied economists, teachers, and advanced students this book provides unrivalled insights in market design and public management. Providing clear analytical frameworks, case studies of auctions, and stage-by-stage advice, it is essential reading for anyone interested in designing public-interested and successful spectrum auctions

    Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses

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    In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside units (RSU), and virtual simulators can maintain the vehicular MR Metaverse via digital simulations for sharing data and making driving decisions collaboratively. However, large-scale traffic and driving simulation via realistic data collection and fusion from the physical world for online prediction and offline training in autonomous driving systems are difficult and costly. In this paper, we propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations for improving driving safety and traffic efficiency. First, we propose a multi-task DT offloading model for the reliable execution of heterogeneous DT tasks with different requirements at RSUs. Then, based on the preferences of AV's DTs and collected realistic data, virtual simulators can synthesize unlimited conditioned driving and traffic datasets to further improve robustness. Finally, we propose a multi-task enhanced auction-based mechanism to provide fine-grained incentives for RSUs in providing resources for autonomous driving. The property analysis and experimental results demonstrate that the proposed mechanism and architecture are strategy-proof and effective, respectively

    The theory of wasting assets with reference to the regulation and pricing of gold in the South African gold mining industry

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    The main aim of this thesis is to present and critically analyse the theory of wasting assets with regard to extractive mineral industries in general and to the pricing and regulation of gold . ii'" particular. Furthermore, to consider the contention that the. price of minerals {such as gold) has been "too low11 in the current generation and that market forces have· led to a 11 too rapid11 depletion of these · resources. The argument that H is the government's duty to intervene and extend the lives of the mines is· also queried •. A detailed examination of the South African· gold mining taxation formula attempts to show how this type of· government intervention (in the for in of .a sliding scale taxation formula) results in uneconomic act ions by mine owners and non-optimal extraction patterns of the resource The contention is put forward that, given certain considerations, market ibrces should lead to the most optimal use of an exhaustible resource where property rights exist and are def inable0 Unlike common property resources, such as the fisheries, where market .forces fail to make the most optimal use of the resource, government intervention is unjustified The scope of the paper is intended to cover both the underlying theory of wasting assets and to relate this theory to gold in part icu1 ar., The determinants of the gold price will be considered as well as the effects of government intervention via· the gold mining taxation formula on the South· African gold mining industry. Hence, the thesis is divided into two sections: "Theoretical 11 and "Gold and Gold Mining". With regard to the method of paper - Literature. from as far back ,· as 1931 regarding .the theory of wasting assets, was collected and .analysed. The information for the section on 11 Gold and Gold Mining" was collected from the various organisations involved in the industry, notably the Chamber of Mines _and the Mineral Engineering Department . . 9f the University of the Witwatersrand. Information regarding the Gold Mining Taxation and Lease Formulae was obtained from the various Government Reports that have been printed since the introduction of the Mining Taxation. Act No. 6 in 191

    A review of the housing market-clearing process in integrated land-use and transport models

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    The land-use/transport interaction (LUTI) modeling framework has become the current state of best practice for analyzing the interdependency between the land-use and transportation systems. This paper presents a comprehensive review of the housing market-clearing mechanisms used in operational LUTI models. Market clearing is a critical component of modeling housing markets, but a systematic review and critique of the current state of the art have not previously been undertaken. In the review paper, the theoretical foundations for modeling household location choice are reviewed, including bid-rent and random utility theories. Five LUTI models are discussed in detail: two equilibrium models, MUSSA and RELU-TRAN, and three dynamic disequilibrium models, UrbanSim, ILUTE, and SimMobility. The discussion focuses on the following key points: the assumptions embedded in the models, the aggregation level of households and locations, computational cost and operationalization of the models. One of the challenges is that there are rarely any empirical studies that compare the performance of equilibrium and dynamic models in the same study context. Future research is recommended to empirically investigate the pros and cons of the two modeling approaches and compare the model performances for their representativeness of real-world behavior, computational efficiencies, and abilities for policy analysis. More sophisticated studies about the impacts of agents’ behavior on the housing market-clearing process are also recommended

    Understanding How the Flash Clashes are Affected in an Asymmetric Informational Market with Agent-based Modelling

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    This thesis explores the impact of flash crashes on the dynamics of financial markets with asymmetric information. We built, implemented, and analysed an agent-based model of an extended information-sequential trading framework inspired by the models of Das and Glosten-Milgrom, where an exogenous fake shock is added into the system to disturb the actions of some traders where there is informational asymmetry. The key modelled agents include fundamental traders, who place orders at preferred prices; zero-intelligence traders, who place orders randomly; a market maker, who provides liquidity; and an exchange matching all orders under continuous auctions or batch auctions. To this end, by Monte-Carlo methods, we implement the model and examine the dynamics of the market under information asymmetry in the following aspects: the market structure, market risk, the network topology of agents and market mechanisms. Our results demonstrate that, an uninformed fundamental trader (UFT) in a messy network is highly likely to suffer a major loss due to the significant price crash in a strongly UFT-dominated market (the informed traders only account for less than 20%), in which case the market efficiency is also negatively affected; Applying batch auctions helps reallocate the profits among the agents to reduce the information advantage between informed and uninformed traders, but it has limited effect on mitigating flash crashes; Building an information-sharing connection between agents is effective to reducing flash crashes and narrows the information advantage gap between informed and uninformed traders, but a complete network with full information exposure could mislead uninformed traders to make biased decisions. These findings generated by an agent-based simulation model give us insights into real-world financial markets under asymmetric information, and the framework proposed in this thesis can be extended for future studies of asymmetric-information markets

    Online reverse auctions research in marketing versus SCM: A review and future directions

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    An online reverse auction (ORA) is a dynamic procurement mechanism that allows suppliers to compete in real time via a platform to gain a buyer’s business. The ORA is a technological tool introduced in the late 1990s, gaining proponents and detractors among practitioners and academics. Remarkably, while practitioner interestin ORAs has grown, related marketing and supply chain management (SCM) research has declined. This contradiction between theory and practice suggests the need to conduct a systematic review to provide readers with a state-of-the-art understanding of ORAs and recommend fruitful avenues for further research. We focus on the marketing literature and contrast the findings with SCM literature, in such an analysis practical relevance is stressed. Our study offers three main contributions: (1) integration of the cumulative marketing knowledge on ORAs in the 2002–2020 period, (2) development of a three-layer framework of the ORA domain (i.e., conceptualization, ORA as a process, and research setting), and (3) construction of a new research agenda to deal with scholarly challenges and emerging trends.Xunta de Galicia | Ref. GPC ED431B 2022/10Universidade de Vigo/CISU

    Learning in Repeated Multi-Unit Pay-As-Bid Auctions

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    Motivated by Carbon Emissions Trading Schemes, Treasury Auctions, and Procurement Auctions, which all involve the auctioning of homogeneous multiple units, we consider the problem of learning how to bid in repeated multi-unit pay-as-bid auctions. In each of these auctions, a large number of (identical) items are to be allocated to the largest submitted bids, where the price of each of the winning bids is equal to the bid itself. The problem of learning how to bid in pay-as-bid auctions is challenging due to the combinatorial nature of the action space. We overcome this challenge by focusing on the offline setting, where the bidder optimizes their vector of bids while only having access to the past submitted bids by other bidders. We show that the optimal solution to the offline problem can be obtained using a polynomial time dynamic programming (DP) scheme. We leverage the structure of the DP scheme to design online learning algorithms with polynomial time and space complexity under full information and bandit feedback settings. We achieve an upper bound on regret of O(MTlogB)O(M\sqrt{T\log |\mathcal{B}|}) and O(MBTlogB)O(M\sqrt{|\mathcal{B}|T\log |\mathcal{B}|}) respectively, where MM is the number of units demanded by the bidder, TT is the total number of auctions, and B|\mathcal{B}| is the size of the discretized bid space. We accompany these results with a regret lower bound, which match the linear dependency in MM. Our numerical results suggest that when all agents behave according to our proposed no regret learning algorithms, the resulting market dynamics mainly converge to a welfare maximizing equilibrium where bidders submit uniform bids. Lastly, our experiments demonstrate that the pay-as-bid auction consistently generates significantly higher revenue compared to its popular alternative, the uniform price auction.Comment: 51 pages, 12 Figure

    Private Equity: Antecedents, Outcomes, Mediators, and Moderators

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    As private equity’s financial heft and influence on the business landscape has intensified, so too has scholarly interest in the phenomenon. We review recent progress in private equity research, with a focus on the private equity industry’s later-stage buyout segment. To synthesize and integrate current findings, we construct a framework that encompasses not only antecedents and outcomes of private equity’s activities, but also mediators and moderators of the relationships that drive these outcomes. Based upon the gaps and learning opportunities that are surfaced by this framework, we develop recommendations for future private equity research. The proposed research agenda is particularly germane to management scholars, whose theories and perspectives have thus far been productively, yet relatively sparingly, applied in private equity research

    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
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