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Automated Market Makers in Cryptoeconomic Systems: A Taxonomy and Archetypes

Authors: Daniel Kirste; Niclas Kannengießer; Ricky Lamberty; Ali Sunyaev;

Automated Market Makers in Cryptoeconomic Systems: A Taxonomy and Archetypes

Abstract

Proper design of automated market makers (AMMs) is crucial for decentralized token exchanges in cryptoeconomic systems. At the intersection of software engineering and economics, AMM design is, however, complex and, if done inappropriately, can lead to financial risks and inefficiencies. To address this, we developed an AMM taxonomy for systematically comparing AMM designs and propose three AMM archetypes that meet key requirements for token issuance and exchange. This work bridges software engineering and economic disciplines, providing guidance for designing AMMs tailored to diverse use cases and fostering sustainable cryptoeconomic systems.

Country
Germany
Keywords

FOS: Economics and business, FOS: Computer and information sciences, info:eu-repo/classification/ddc/330, Quantitative Finance - Trading and Market Microstructure, Computer Science - Cryptography and Security, ddc:330, Economics, Cryptography and Security (cs.CR), Trading and Market Microstructure (q-fin.TR)

  • BIP!
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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    8
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Average
Top 10%
Green