Change search
ReferencesLink to record
Permanent link

Direct link
On collective bandit behaviour
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The collective decision process of Gambusia affinis (the mosquitofish) is investigated from the standpoint of online machine learning algorithms. A new algorithm, the Collaborative Exp3 algorithm, is derived from the adversarial bandits framework to model how groups of fish make collective decisions leading to consensus. Thanks to maximum likelihood estimation, parameters are tuned and comparisons between data and algorithm performances are addressed. This work provides promising results in the scope of recovering information transfer within fish groups as well as to understand the individual mechanisms involved in the collective decision process. It is the first published approach to connect online machine learning algorithms with data, hence bridging a gap between theory and biological practice.

Place, publisher, year, edition, pages
IT, 14 046
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-230982OAI: diva2:742548
Educational program
Master Programme in Computational Science
Available from: 2014-09-02 Created: 2014-09-02 Last updated: 2014-09-02Bibliographically approved

Open Access in DiVA

fulltext(1061 kB)125 downloads
File information
File name FULLTEXT01.pdfFile size 1061 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Department of Information Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 125 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 386 hits
ReferencesLink to record
Permanent link

Direct link