Data, knowledge, and common sensereasoning

Authors

  • Ali Ghodsi Royal Institute of Technology Author
  • Bernardo Huberman Core Innovation en CableLabs, Stanford University Author
  • Fang Wu HP Labs Author

DOI:

https://doi.org/10.59471/raia201844

Abstract

We explore the use of pari-mutuel markets in a peer-to-peer setting to generate a wide diversity of content offerings while responding adaptively to customer demand. Files are served and paid for through a parimutuel market similar to that used for betting in horse races and in lotteries.
Our simulations are based on rational agents, which all act according to a set of simple rules. The results show that a favorite-longshot bias occurs, where agents tend to bet on longshots rather than favorites when following simple expected utility maximization. Furthermore, we have confirmed that the long-tail does sustain even when the agents only have a limited view of all files
to pick from. If the limited view consists of random subsets of all files, the long tail is enhanced.
If the limited view consists of the top most popular items, the long tail slightly decreases. We have also explored the effect of bounded rationality. Our results show that the system is robust in presence of a large fraction of providers that have bounded rationality. If the providers with bounded rationality pick random items, the long tail is enhanced. Conversely, if the providers with bounded rationality only pick their favorite, the long tail slightly decreases

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Published

2018-05-29

How to Cite

1.
Ghodsi A, Huberman B, Wu F. Data, knowledge, and common sensereasoning. Revista Abierta de Informática Aplicada [Internet]. 2018 May 29 [cited 2025 Mar. 10];2(1):23-32. Available from: https://raia.revistasuai.ar/index.php/raia/article/view/44