No-Regret Learning from Partially Observed Data in Repeated Auctions
Loading...
Author / Producer
Date
2020-11
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
Data
Abstract
We study a general class of repeated auctions, such as the ones found in electricity markets, as multi-agent games between the bidders. In such a repeated setting, bidders can adapt their strategies online using no-regret algorithms based on the data observed in the previous auction rounds. Well-studied no-regret algorithms depend on the feedback information available at every round, and can be mainly distinguished as bandit (or payoff-based), and full-information. However, the information structure found in auctions lies in between these two models, since participants can often obtain partial observations of their utilities under different strategies. To this end, we modify existing bandit algorithms to exploit such additional information. Specifically, we utilize the feedback information that bidders can obtain when their bids are not accepted, and build a more accurate estimator of the utility vector. This results in improved regret guarantees compared to standard bandit algorithms. Moreover, we propose a heuristic method for auction settings where the proposed algorithm is not directly applicable. Finally, we demonstrate our findings on case studies based on realistic electricity market models.
Permanent link
Publication status
published
External links
Book title
21st IFAC World Congress
Journal / series
Volume
53 (2)
Pages / Article No.
14 - 19
Publisher
Elsevier
Event
1st Virtual IFAC World Congress (IFAC-V 2020)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
auctions; game theory; no-regret; coarse-correlated equilibrium; electricity markets
Organisational unit
09578 - Kamgarpour, Maryam (ehemalig) / Kamgarpour, Maryam (former)
Notes
Due to the Coronavirus (COVID-19) the 21st IFAC World Congress 2020 became the 1st Virtual IFAC World Congress (IFAC-V 2020).
Funding
678945 - Control of Large-scale Stochastic Hybrid Systems for Stability of Power Grid with Renewable Energy (EC)