1,089 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Spectrum auctions: designing markets to benefit the public, industry and the economy
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
Current issues of the management of socio-economic systems in terms of globalization challenges
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
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
Online Resource Sharing via Dynamic Max-Min Fairness: Efficiency, Robustness and Non-Stationarity
We study the allocation of shared resources over multiple rounds among
competing agents, via a dynamic max-min fair (DMMF) mechanism: the good in each
round is allocated to the requesting agent with the least number of allocations
received to date. Previous work has shown that when an agent has i.i.d. values
across rounds, then in the worst case, she can never get more than a constant
strictly less than fraction of her ideal utility -- her highest achievable
utility given her nominal share of resources. Moreover, an agent can achieve at
least half her utility under carefully designed `pseudo-market' mechanisms,
even though other agents may act in an arbitrary (possibly adversarial and
collusive) manner.
We show that this robustness guarantee also holds under the much simpler DMMF
mechanism. More significantly, under mild assumptions on the value
distribution, we show that DMMF in fact allows each agent to realize a fraction of her ideal utility, despite arbitrary behavior by other
agents. We achieve this by characterizing the utility achieved under a richer
space of strategies, wherein an agent can tune how aggressive to be in
requesting the item. Our new strategies also allow us to handle settings where
an agent's values are correlated across rounds, thereby allowing an adversary
to predict and block her future values. We prove that again by tuning one's
aggressiveness, an agent can guarantee fraction of her ideal
utility, where is a parameter that quantifies dependence
across rounds (with indicating full independence and lower values
indicating more correlation). Finally, we extend our efficiency results to the
case of reusable resources, where an agent might need to hold the item over
multiple rounds to receive utility
Vulnerabilities of Single-Round Incentive Compatibility in Auto-bidding: Theory and Evidence from ROI-Constrained Online Advertising Markets
Most of the work in auction design literature assumes that bidders behave
rationally based on the information available for every individual auction, and
the revelation principle enables designers to restrict their efforts to
incentive compatible (IC) mechanisms. However, in today's online advertising
markets, one of the most important real-life applications of auction design,
the data and computational power required to bid optimally are only available
to the auction designer, and an advertiser can only participate by setting
performance objectives and constraints for its proxy auto-bidder provided by
the platform.
The prevalence of auto-bidding necessitates a review of auction theory. In
this paper, we examine properties of auto-bidding markets through the lens of
ROI-constrained value-maximizing campaigns, which are widely adopted in many
global-scale online advertising platforms. Through theoretical analysis and
empirical experiments on both synthetic and realistic data, we find that second
price auction exhibits many undesirable properties (equilibrium multiplicity,
computational hardness, exploitability by bidders and auctioneers, instability
of bidders' utilities, and interference in A/B testing) and loses its dominant
theoretical advantages in single-item scenarios. Some of these phenomena have
been identified in literature (for budget-constrained auto-bidders) and widely
observed in practice, and we show that they are actually deeply rooted in the
property of (single-round) incentive compatibility. Although many complex
designs have been proposed in literature, first and second price auctions
remain popular in industry. We hope that our work could bring new perspectives
to the community and benefit practitioners to attain a better grasp of
real-world markets
京都大学経済研究所年次報告書 2022年度
本資料は、教員個人の履歴、業績を除き、特に断りがない限り、2022年4月から2023年3月までの活動についてまとめたものです。1 研究所の概要 [1]1-1.沿革 [1]1-2.年表、歴代所長、名誉教授 [2]1-3.第一期中期計画の概要 [6]1-4.第一期中期目標期間に係る京都大学経済研究所の研究に関する現況分析結果について [9]1-5.第二期中期計画の概要 [11]1-6.第二期中期目標期間に係る京都大学経済研究所の研究に関する現況分析結果について [13]1-7.第三期中期計画の概要 [16]1-8.第三期中期目標期間に係る京都大学経済研究所の研究に関する現況分析結果について [17]1-9.第四期中期計画の概要 [19]2 研究体制 [20]2-1.組織 [20]2-2.職員 [21]2-3.予算 [28]2-4.図書 [30]2-5.公的統計オンサイト利用施設 [30]2-6.敷地と建物 [31]2-7.東京分室 [31]3 研究活動 [32]3-1.研究の概要 [32]3-2.研究活動 [40]3-3.研究成果 [66]4 人材養成 [68]4-1.大学院教育への協力 [68]4-2.大学院生受入状況 [68]4-3.研究生受入状況 [68]4-4.ポスト・ドクター(PD)受入状況 [68]4-5.日本学術振興会外国人特別研究員受入状況 [68]5 国際交流 [69]5-1.研究者の受入 [69]5-2.国際シンポジウム [75]6 社会貢献 [76]6-1.学外委員等 [76]6-2.一般市民対象シンポジウム等の開催 [77]6-3.創立60周年記念式典並びに記念講演会の開催 [78]7 参考資料 [79]7-1.過去の大規模プロジェクト [79]7-2.研究成果(所員による論文・著書被引用数より) --社会科学系附置研究所比較-- [82]7-3.共同利用 [84]教員の履歴・業績・研究概要 [87
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