2 Search Results for "Kahng, Anson"

Document
DOI: 10.4230/LIPIcs.ITCS.2024.51
An Axiomatic Characterization of CFMMs and Equivalence to Prediction Markets

Authors: Rafael Frongillo, Maneesha Papireddygari, and Bo Waggoner

Published in: LIPIcs, Volume 287, 15th Innovations in Theoretical Computer Science Conference (ITCS 2024)

Abstract
Constant-function market makers (CFMMs), such as Uniswap, are automated exchanges offering trades among a set of assets. We study their technical relationship to another class of automated market makers, cost-function prediction markets. We first introduce axioms for market makers and show that CFMMs with concave potential functions characterize "good" market makers according to these axioms. We then show that every such CFMM on n assets is equivalent to a cost-function prediction market for events with n outcomes. Our construction directly converts a CFMM into a prediction market, and vice versa. Using this equivalence, we give another construction which can produce any 1-homogenous, increasing, and concave CFMM, as are typically used in practice, from a cost function. Conceptually, our results show that desirable market-making axioms are equivalent to desirable information-elicitation axioms, i.e., markets are good at facilitating trade if and only if they are good at revealing beliefs. For example, we show that every CFMM implicitly defines a proper scoring rule for eliciting beliefs; the scoring rule for Uniswap is unusual, but known. From a technical standpoint, our results show how tools for prediction markets and CFMMs can interoperate. We illustrate this interoperability by showing how liquidity strategies from both literatures transfer to the other, yielding new market designs.
Cite as

Rafael Frongillo, Maneesha Papireddygari, and Bo Waggoner. An Axiomatic Characterization of CFMMs and Equivalence to Prediction Markets. In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 51:1-51:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{frongillo_et_al:LIPIcs.ITCS.2024.51,
  author =	{Frongillo, Rafael and Papireddygari, Maneesha and Waggoner, Bo},
  title =	{{An Axiomatic Characterization of CFMMs and Equivalence to Prediction Markets}},
  booktitle =	{15th Innovations in Theoretical Computer Science Conference (ITCS 2024)},
  pages =	{51:1--51:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-309-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{287},
  editor =	{Guruswami, Venkatesan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2024.51},
  URN =		{urn:nbn:de:0030-drops-195795},
  doi =		{10.4230/LIPIcs.ITCS.2024.51},
  annote =	{Keywords: Convex analysis, Equivalence result, Axiomatic characterization, Market Makers, Prediction markets, Scoring rules, Cost-functions}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2020.65
Computation-Aware Data Aggregation

Authors: Bernhard Haeupler, D. Ellis Hershkowitz, Anson Kahng, and Ariel D. Procaccia

Published in: LIPIcs, Volume 151, 11th Innovations in Theoretical Computer Science Conference (ITCS 2020)

Abstract
Data aggregation is a fundamental primitive in distributed computing wherein a network computes a function of every nodes' input. However, while compute time is non-negligible in modern systems, standard models of distributed computing do not take compute time into account. Rather, most distributed models of computation only explicitly consider communication time. In this paper, we introduce a model of distributed computation that considers both computation and communication so as to give a theoretical treatment of data aggregation. We study both the structure of and how to compute the fastest data aggregation schedule in this model. As our first result, we give a polynomial-time algorithm that computes the optimal schedule when the input network is a complete graph. Moreover, since one may want to aggregate data over a pre-existing network, we also study data aggregation scheduling on arbitrary graphs. We demonstrate that this problem on arbitrary graphs is hard to approximate within a multiplicative 1.5 factor. Finally, we give an O(log n ⋅ log(OPT/t_m))-approximation algorithm for this problem on arbitrary graphs, where n is the number of nodes and OPT is the length of the optimal schedule.
Cite as

Bernhard Haeupler, D. Ellis Hershkowitz, Anson Kahng, and Ariel D. Procaccia. Computation-Aware Data Aggregation. In 11th Innovations in Theoretical Computer Science Conference (ITCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 151, pp. 65:1-65:38, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{haeupler_et_al:LIPIcs.ITCS.2020.65,
  author =	{Haeupler, Bernhard and Hershkowitz, D. Ellis and Kahng, Anson and Procaccia, Ariel D.},
  title =	{{Computation-Aware Data Aggregation}},
  booktitle =	{11th Innovations in Theoretical Computer Science Conference (ITCS 2020)},
  pages =	{65:1--65:38},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-134-4},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{151},
  editor =	{Vidick, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2020.65},
  URN =		{urn:nbn:de:0030-drops-117506},
  doi =		{10.4230/LIPIcs.ITCS.2020.65},
  annote =	{Keywords: Data aggregation, distributed algorithm scheduling, approximation algorithms}
}
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