5,612 research outputs found

    An indigenous perspective on institutions for sustainable business in China

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    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

    Advertiser Learning in Direct Advertising Markets

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    Direct buy advertisers procure advertising inventory at fixed rates from publishers and ad networks. Such advertisers face the complex task of choosing ads amongst myriad new publisher sites. We offer evidence that advertisers do not excel at making these choices. Instead, they try many sites before settling on a favored set, consistent with advertiser learning. We subsequently model advertiser demand for publisher inventory wherein advertisers learn about advertising efficacy across publishers' sites. Results suggest that advertisers spend considerable resources advertising on sites they eventually abandon -- in part because their prior beliefs about advertising efficacy on those sites are too optimistic. The median advertiser's expected CTR at a new site is 0.23%, five times higher than the true median CTR of 0.045%. We consider how pooling advertiser information remediates this problem. Specifically, we show that ads with similar visual elements garner similar CTRs, enabling advertisers to better predict ad performance at new sites. Counterfactual analyses indicate that gains from pooling advertiser information are substantial: over six months, we estimate a median advertiser welfare gain of \$2,756 (a 15.5% increase) and a median publisher revenue gain of \$9,618 (a 63.9% increase)

    Financial and Economic Review 22.

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    Power system adequacy: on two-area models and the capacity procurement decision process

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    In this work, we explore methodological extensions to modelling practices in power system adequacy for single-area and two-area systems. Specifically, we build on top of some of the practices currently in use in Great Britain (GB) by National Grid, framing this in the context of the current technological transition in which renewable capacity is gradually replacing a considerable share of fossil-fuel-based capacity. We explore two-area extensions of the methodology currently used in GB to quantify risk in single-area models. By doing this, we also explore the impact of shortfall-sharing policies and wind capacity on risk indices and on the value of interconnection. Furthermore, we propose a model based on the statistical theory of extreme values to characterise statistical dependence across systems in both net demand (defined as power demand minus renewable generation) and capacity surpluses/deficits (defined as power supply minus demand), looking at how statistical dependence strength influences post-interconnection risk and the capacity value of interconnection. Lastly, we analyse the risk profile of a single-area system as reliance on wind capacity grows, looking at risk beyond the standard set of risk indices, which are based on long-term averages. In doing this, we look at trends which are overlooked by the latter, yet are of considerable importance for decision-makers. Moreover, we incorporate a measure of the decision-maker's degree of risk aversion into the current capacity procurement methodology in GB, and look at the impact of this and other parameters on the amount of procured capacity. We find that shortfall-sharing policies can have a sizeable impact on the interconnector's valuation in terms of security of supply, specially for systems that are significantly smaller than their neighbours. Moreover, this valuation also depends strongly on the risk indices chosen to measure it. We also find that the smoothing effect of parametric extreme value models on tail regions can have a material effect on practical adequacy calculations for post-interconnection risks, and that assumed independence between conventional generation fleets makes capacity shortfall co-occurrences only weakly dependent (in a precisely defined sense) across areas despite much stronger statistical dependence between system net demands. Lastly, as more wind capacity is installed, we find multiple relevant changes in the (single-area) system's risk profile that are not expressed by the standard risk indices: in particular, we find a substantial increase in the frequency of severe events, extreme year-to-year variability of outturn, and a progression to a system with fewer days of potentially much larger shortfalls. Moreover, we show that a high reliance on wind introduces a substantial amount of uncertainty into the calculations due to the limited number of available historic years, which cannot account for the wide range of possible weather conditions the system could experience in the future. Lastly, we also find that the a higher reliance on wind generation also impact the capacity procurement decision process, potentially making the amount of procured capacity considerably more sensitive to parameters such as the value of lost load

    The impact of active and passive investment on market efficiency: a simulation study

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    We create a simulated financial market and examine the effect of different levels of active and passive investment on fundamental market efficiency. In our simulated market, active, passive, and random investors interact with each other through issuing orders. Active and passive investors select their portfolio weights by optimizing Markowitz-based utility functions. We find that higher fractions of active investment within a market lead to an increased fundamental market efficiency. The marginal increase in fundamental market efficiency per additional active investor is lower in markets with higher levels of active investment. Furthermore, we find that a large fraction of passive investors within a market may facilitate technical price bubbles, resulting in market failure. By examining the effect of specific parameters on market outcomes, we find that that lower transaction costs, lower individual forecasting errors of active investors, and less restrictive portfolio constraints tend to increase fundamental market efficiency in the market

    The Politics of Platformization: Amsterdam Dialogues on Platform Theory

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    What is platformization and why is it a relevant category in the contemporary political landscape? How is it related to cybernetics and the history of computation? This book tries to answer such questions by engaging in multidisciplinary dialogues about the first ten years of the emerging fields of platform studies and platform theory. It deploys a narrative and playful approach that makes use of anecdotes, personal histories, etymologies, and futurable speculations to investigate both the fragmented genealogy that led to platformization and the organizational and economic trends that guide nowadays platform sociotechnical imaginaries

    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

    Wait for others?:Social and intertemporal preferences in allocation of healthcare resources

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