3,218 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Do price trajectory data increase the efficiency of market impact estimation?
Market impact is an important problem faced by large institutional investor
and active market participant. In this paper, we rigorously investigate whether
price trajectory data from the metaorder increases the efficiency of
estimation, from an asymptotic view of statistical estimation. We show that,
for popular market impact models, estimation methods based on partial price
trajectory data, especially those containing early trade prices, can outperform
established estimation methods (e.g., VWAP-based) asymptotically. We discuss
theoretical and empirical implications of such phenomenon, and how they could
be readily incorporated into practice
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics
We study a game between autobidding algorithms that compete in an online
advertising platform. Each autobidder is tasked with maximizing its
advertiser's total value over multiple rounds of a repeated auction, subject to
budget and/or return-on-investment constraints. We propose a gradient-based
learning algorithm that is guaranteed to satisfy all constraints and achieves
vanishing individual regret. Our algorithm uses only bandit feedback and can be
used with the first- or second-price auction, as well as with any
"intermediate" auction format. Our main result is that when these autobidders
play against each other, the resulting expected liquid welfare over all rounds
is at least half of the expected optimal liquid welfare achieved by any
allocation. This holds whether or not the bidding dynamics converges to an
equilibrium and regardless of the correlation structure between advertiser
valuations
Canada\u27s Evergreen Playground: A History of Snow in Vancouver
The City of Vancouver is not as snowy as the rest of Canada; rain, not snow, is its defining weather feature. But snow is a common seasonal occurrence, having fallen there nearly every winter since the 1850s. This dissertation places snow at the centre of the City of Vancouver’s history. It demonstrates how cultural and natural factors influenced human experiences and relationships with snow on the coast between the 1850s and 2000s. Following Vancouver’s incorporation, commercial and civic boosters constructed – and settlers adopted – what I call an evergreen mentality. Snow was reconceptualized as a rare and infrequent phenomenon. The evergreen mentality was not completely false, but it was not entirely true, either. This mindset has framed human relationships with snow in Vancouver ever since. While this idea was consistent, how coastal residents experienced snow evolved in response to societal developments (such as the rise of the automobile and the adoption of new snow-clearing technologies) and regional climate change.
I show that the history of snow in Vancouver cannot be fully understood without incorporating the southern Coast Mountains. Snow was a connecting force between the coastal metropolis and mountainous hinterland. Settlers drew snowmelt to the urban environment for its energy potential and life-sustaining properties; snow drew settlers to the mountains for recreation and economic opportunities. Mountain snow became a valuable resource for coastal residents throughout the twentieth century. Human relationships with snow in the mountains were shaped, as they were in the city, by seasonal expectations, societal circumstances, and shifting climate conditions.
In charting a history of snow in Vancouver and the southern Coast Mountains, this dissertation clears a new path in Canadian environmental historiography by bringing snow to the historiographical forefront. It does so in an urban space not known for snow, broadening the existing geography of snow historiography. In uncovering snow’s impact on year-round activities, this work also expands the field’s temporal boundaries. Through this work, one sees how snow helped to make Canada’s Evergreen Playground
Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing
Key challenges in running a retail business include how to select products to
present to consumers (the assortment problem), and how to price products (the
pricing problem) to maximize revenue or profit. Instead of considering these
problems in isolation, we propose a joint approach to assortment-pricing based
on contextual bandits. Our model is doubly high-dimensional, in that both
context vectors and actions are allowed to take values in high-dimensional
spaces. In order to circumvent the curse of dimensionality, we propose a simple
yet flexible model that captures the interactions between covariates and
actions via a (near) low-rank representation matrix. The resulting class of
models is reasonably expressive while remaining interpretable through latent
factors, and includes various structured linear bandit and pricing models as
particular cases. We propose a computationally tractable procedure that
combines an exploration/exploitation protocol with an efficient low-rank matrix
estimator, and we prove bounds on its regret. Simulation results show that this
method has lower regret than state-of-the-art methods applied to various
standard bandit and pricing models. Real-world case studies on the
assortment-pricing problem, from an industry-leading instant noodles company to
an emerging beauty start-up, underscore the gains achievable using our method.
In each case, we show at least three-fold gains in revenue or profit by our
bandit method, as well as the interpretability of the latent factor models that
are learned
Maximizing Miner Revenue in Transaction Fee Mechanism Design
Transaction fee mechanism design is a new decentralized mechanism design problem where users bid for space on the blockchain. Several recent works showed that the transaction fee mechanism design fundamentally departs from classical mechanism design. They then systematically explored the mathematical landscape of this new decentralized mechanism design problem in two settings: in the plain setting where no cryptography is employed, and in a cryptography-assisted setting where the rules of the mechanism are enforced by a multi-party computation protocol. Unfortunately, in both settings, prior works showed that if we want the mechanism to incentivize honest behavior for both users as well as miners (possibly colluding with users), then the miner revenue has to be zero. Although adopting a relaxed, approximate notion of incentive compatibility gets around this zero miner-revenue limitation, the scaling of the miner revenue is nonetheless poor.
In this paper, we show that if we make a mildly stronger reasonable-world assumption than prior works, we can circumvent the known limitations on miner revenue, and design auctions that generate optimal miner revenue. We also systematically explore the mathematical landscape of transaction fee mechanism design under the new reasonable-world and demonstrate how such assumptions can alter the feasibility and infeasibility landscape
Modelação e Negociação de Flexibilidade em Comunidades de Energia Renovável
The progressive replacement of traditional generation resources with intermittent
resources has reduced the available supply-side flexibility and increased the need to unlock
flexibility on the demand-side. At the same time, the rising electricity consumption in
residential buildings requires an analysis of the potential flexibility of the loads within them
to contribute to the operation needs of electrical grids. Lastly, regulations governing self consumption have allowed end consumers to form energy communities based on local
electricity markets. This is an additional incentive to define strategies for trading available
flexibility at local level, in separate but simultaneously integrated structures within
wholesale electricity markets.
The proposed dissertation work focuses on studying the flexibility of energy
production and consumption by prosumers within a Renewable Energy Community (REC).
The objective is to investigate how residential flexibility can be determined, modeled, and
aggregated for trading in a local market created for this purpose. The work to be developed
will present a two-stage model that determines residential technical flexibility and
establishes a local market only for its transaction.
In the first stage, the optimal scheduling of domestic devices (flexible units or FUs)
for each prosumer is determined, serving as a baseline for comparison, along with the
technical limits of flexibility (maximum and minimum possible consumption profiles) for
each FU.
In the second stage, a market model is established only for flexibility exchanges. The
technical flexibility determined in the first stage is offered to the Community Manager (CM)
as flexibility offer, with an associated price. This entity acts as an aggregator and
simultaneously as the operator of the local market. At this level, the Distribution System
Operator (DSO) submits its flexibility requirements for the next day to the CM, who is
responsible for executing the clearing process. The pricing of the flexibility offered by
prosumers in the market is based on the base energy tariff they are subject to, which
corresponds to the cost of their optimal scheduling obtained in the first stage, without
considering this flexibility. Therefore, offering flexibility becomes an incentive to reduce prosumers energy costs or increase their utility, complementing their mere participation in
energy markets.
A case study based on a renewable energy community with a strong penetration of
emerging technologies is used to validate and demonstrate the relevance of the proposed
approach in terms of determining and activating residential FU flexibility. The obtained
results show that participation in the local flexibility market leads to a reduction in prosumers
energy costs, around 4.5%, in average. It can be an incentive for prosumers to join RECs
that would not only have local energy trading structures but also mechanisms for negotiating
and sharing flexibility. In addition, it was evidenced that the impact of electric vehicle
chargers and battery energy storage systems on the total flexibility offered and accepted in
the market is much greater than that the impact of other small loads studied. This not only
constitutes an incentive for the study of the operational flexibility of these resources but also
for investments in these emerging technologies.A substituição progressiva dos recursos de geração tradicionais por recursos
intermitentes tem reduzido a flexibilidade disponível do lado da oferta e aumentado a
necessidade de desbloqueá-la do lado da procura. Ao mesmo tempo, o aumento do consumo
de eletricidade nos edifícios residenciais obriga a que seja analisada a flexibilidade potencial
das cargas que o constituem, de modo a contribuir para as necessidades de operação das
redes elétricas. Por último, a regulamentação do autoconsumo, tem permitido aos
consumidores finais constituir comunidades energéticas baseadas em mercados locais de
eletricidade. Isto torna ainda mais importante a definição de estratégias para comercializar a
flexibilidade disponível a esse nível, em estruturas de mercado local separadas, mas
simultaneamente integradas nos mercados grossistas de eletricidade.
O trabalho proposto para dissertação assenta no estudo da flexibilidade da produção
e consumo de energia por parte dos prosumidores de uma Comunidade de Energia
Renovável. O objetivo é estudar como a flexibilidade residencial pode ser determinada,
modelada e agregada de modo a ser transacionada num mercado local criado para esse fim.
Assim, o trabalho a ser desenvolvido apresentará um modelo de dois estágios que determina
a flexibilidade técnica residencial e cria um mercado local exclusivo para transaciona-la.
Numa primeira fase, determina-se o escalonamento óptimo dos dispositivos
domésticos (unidades flexíveis ou UF) de cada prosumidor, o que constitui uma baseline de
comparação, bem como os limites técnicos de flexibilidade (perfis de consumo máximos e
mínimos possíveis) de cada UF.
Num segundo estágio, é estabelecido um modelo de mercado apenas para trocas de
flexibilidade. A flexibilidade técnica determinada no primeiro estágio é disponibilizada ao
Gestor de Comunidade (CM), enquanto oferta de flexibilidade, com um preço associado.
Esta entidade desempenha as funções de agregador e simultaneamente de operador do
mercado local. A este nível, o Operador do Sistema de Distribuição (ORD) submete os seus
requisitos de flexibilidade, para o dia seguinte, ao CM, que é responsável pelo executar o
clearing. A precificação da flexibilidade oferecida pelos prosumidores em mercado é feita
com base no valor da tarifa base de energia a que estão sujeitos, que corresponde ao custo do seu escalonamento ótimo, obtido no primeiro estágio, que não considera essa mesma
flexibilidade. Portanto, oferecer flexibilidade torna-se um incentivo para reduzir os custos
energéticos dos prosumidores ou aumentar a sua utilidade, o que complementa a sua mera
participação nos mercados de energia.
Um caso de estudo baseado numa comunidade de energia com forte penetração de
tecnologias emergentes é utilizado e valida a metodologia desenvolvida. Para além disso é
evidenciada a relevância da abordagem proposta em termos de determinação e ativação da
flexibilidade de UFs residenciais os impactos das mesmas no fecho de mercado. Os
resultados evidenciam que participação no mercado local de flexibilidade induz uma redução
dos custos energéticos dos prosumidores, na casa 4.5%, em média. O impacto dos
carregadores de veículos elétricos e dos sistemas de armazenamento de energia em baterias
na flexibilidade total oferecida e aceite em mercado é muito superior ao de outras pequenas
cargas estudadas. Tudo isto pode vir a resultar num incentivo ao investimento nos recursos
referidos, bem como à associação de prosumidores em comunidades de energia renovável,
onde para além de estruturas locais de comercialização de energia, existam outras que
permitam a negociação e partilha de flexibilidade
From disclosure to transparency - Essays on firms' voluntary disclosure in a transforming environment
This cumulative thesis is based on three articles.
In the first paper, I investigate firms' greenhouse gas emission disclosure strategies. The results show the potential existence of different disclosure equilibria, which implies different disclosure patterns in different industries. I further identify that disclosure mandates may have an adverse effect on firms' abatement incentives and even their total emissions.
In the second paper, I propose a model to investigate firms’ signaling decisions on the product level.
In the third paper, my coauthors and I investigate the potential and limits of privacy-preserving corporate blockchain applications for information provision. We show that blockchain technology can improve the information environment and outperform traditional institutions. However, we also characterize an adverse mixed-adoption equilibrium in which neither of the two channels realizes its full potential and information provision declines not only for individual firms but also in aggregate
Assessing prioritization measures for a private land conservation program in the U.S. Prairie Pothole Region
Private land conservation has become an important tool for protecting biodiversity and habitat, but methods for prioritizing and scheduling conservation on private land are still being developed. While return on investment methods have been suggested as a potential path forward, the different processes linking private landscapes to the socioeconomic systems in which they are embedded create unique challenges for scheduling conservation with this approach. We investigated a range of scheduling approaches within a return on investment framework for breeding waterfowl and broods in the Prairie Pothole Region of North Dakota, South Dakota, and Montana. Current conservation targeting for waterfowl in the region focuses mostly on the distribution and abundance of breeding waterfowl. We tested whether MaxGain approaches for waterfowl conservation differed from MinLoss approaches in terms of return on investment and which approach performed best in avoiding loss of waterfowl and broods separately. We also examined variation in results based upon the temporal scale of the abundance layers used for input and compared the region's current scheduling approach with results from our simulations. Our results suggested that MinLoss was the most efficient scheduling approach for both breeding waterfowl and broods and that using just breeding waterfowl to target areas for conservation programs might cause organizations to overlook important areas for broods, particularly over shorter timespans. The higher efficiency of MinLoss approaches in our simulations also indicated that incorporating probability of wetland drainage into decision-making improved the overall return on investment. We recommend that future conservation scheduling for easements in the region and for private land conservation in general include some form of return on investment or cost-effective analysis to make conservation more transparent
Learning in Repeated Multi-Unit Pay-As-Bid Auctions
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 and respectively, where is the number of units demanded by the
bidder, is the total number of auctions, and is the size of
the discretized bid space. We accompany these results with a regret lower
bound, which match the linear dependency in . 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
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