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On collective bandit behaviour
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi.
2014 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
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.

sted, utgiver, år, opplag, sider
2014.
Serie
IT, 14 046
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-230982OAI: oai:DiVA.org:uu-230982DiVA: diva2:742548
Utdanningsprogram
Master Programme in Computational Science
Veileder
Examiner
Tilgjengelig fra: 2014-09-02 Laget: 2014-09-02 Sist oppdatert: 2014-09-02bibliografisk kontrollert

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