Resource Allocation with Karma Mechanisms

[2406.14728] Resource Allocation with Karma Mechanisms Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Multiagent Systems arXiv:2406.14728 (cs) [Submitted on 20 Jun 2024] Title: Resource Allocation with Karma Mechanisms Authors: Kevin Riehl , Anastasios Kouvelas , Michail Makridis View a PDF of the paper titled Resource Allocation with Karma Mechanisms, by Kevin Riehl and Anastasios Kouvelas and Michail Makridis View PDF HTML (experimental) Abstract: Monetary markets serve as established resource allocation mechanisms, typically achieving efficient solutions with limited information. However, they are susceptible to market failures, particularly under the presence of public goods, externalities, or inequality of economic power. Moreover, in many resource allocating contexts, money faces social, ethical, and legal constraints. Consequently, research increasingly explores artificial currencies and non-monetary markets, with Karma emerging as a notable concept. Karma, a non-tradeable, resource-inherent currency for prosumer resources, operates on the principles of contribution and consumption of specific resources. It embodies fairness, near incentive compatibility, Pareto-efficiency, robustness to population heterogeneity, and can incentivize a reduction in resource scarcity. The literature on Karma is scattered across disciplines, varies in scope, and lacks of conceptual clarity and coherence. Thus, this study undertakes a comprehensive review of the Karma mechanism, systematically comparing its resource allocation applications and elucidating overlooked mechanism design elements. Through a systematic mapping study, this review situates Karma within its literature context, offers a structured design parameter framework, and develops a road-map for future research directions. Subjects: Multiagent Systems (cs.MA) Cite as: arXiv:2406.14728 [cs.MA] (or arXiv:2406.14728v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2406.14728 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Economies 2024 Related DOI : https://doi.org/10.3390/economies12080211 Focus to learn more DOI(s) linking to related resources Submission history From: Kevin Riehl [ view email ] [v1] Thu, 20 Jun 2024 20:48:01 UTC (9,281 KB) Full-text links: Access Paper: View a PDF of the paper titled Resource Allocation with Karma Mechanisms, by Kevin Riehl and Anastasios Kouvelas and Michail Makridis View PDF HTML (experimental) TeX Source view license Current browse context: cs.MA < prev | next > new | recent | 2024-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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