To Spend or to Gain: Online Learning in Repeated Karma Auctions
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Date
2025-05
Publication Type
Conference Paper
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yes
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Abstract
Recent years have seen a surge of artificial currency-based mechanisms in contexts where monetary instruments are deemed unfair or inappropriate, e.g., in allocating food donations to food banks, course seats to students, and, more recently, even for traffic congestion management. Yet the applicability of these mechanisms remains limited in repeated auction settings, as it is challenging for users to learn how to bid an artificial currency that has no value outside the auctions. Indeed, users must jointly learn the value of the currency in addition to how to spend it optimally. Moreover, in the prominent class of karma mechanisms, in which artificial karma payments are redistributed to users at each time step, users do not only spend karma to obtain public resources but also gain karma for yielding them. For this novel class of karma auctions, we propose an adaptive karma pacing strategy that learns to bid optimally, and show that this strategy a) is asymptotically optimal for a single user bidding against competing bids drawn from a stationary distribution; b) leads to convergent learning dynamics when all users adopt it; and c) constitutes an approximate Nash equilibrium as the number of users grows. Our results require a novel analysis in comparison to adaptive pacing strategies in monetary auctions, since we depart from the classical assumption that the currency has known value outside the auctions, and consider that the currency is both spent and gained through the redistribution of payments.
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Publication status
published
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Editor
Book title
AAMAS '25: Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems
Journal / series
Volume
Pages / Article No.
289 - 297
Publisher
International Foundation for Autonomous Agents and Multiagent Systems
Event
24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Online learning; Artificial currency; Karma economy; Repeated auctions; Budget-constrained auctions; Adaptive pacing
Organisational unit
03604 - Wattenhofer, Roger / Wattenhofer, Roger
09574 - Frazzoli, Emilio / Frazzoli, Emilio
09478 - Dörfler, Florian / Dörfler, Florian
Notes
Funding
180545 - NCCR Automation (phase I) (SNF)
Related publications and datasets
Is variant form of: 10.48550/arXiv.2403.04057