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Michal ArbillyAssociate Teaching Professor | Data & Decision Sciences & Biology

Biography

Dr. Arbilly is a lecturer at Emory University’s Departments of Biology and Data & Decision Sciences. Her research interests are at the intersection of animal behavior, cognition, and evolution, and she is especially interested in how group-living shaped the evolution of learning and decision-making processes. She studies these questions using computer models – a powerful tool for looking into the complexities of cognitive processes, social dynamics, and the interaction between them.

Education

  • Ph.D, Zoology, Tel Aviv University, Tel Aviv-Yafo, Israel, 2011
  • MSc, Genetics, The Hebrew University of Jerusalem, Jerusalem, 2005
  • B.Sc., Life Sciences and Psychology, The Hebrew University of Jerusalem, Jerusalem, 2003

Research

Animal behavior, Cognition, and Evolution

 

Publications

Arbilly, M (2018) High-magnitude innovators as keystone individuals in the evolution of culture. Philosophical Transactions of the Royal Society B, Biological Sciences 373: 20170053.
 
Arbilly, M, Lotem A (2017) Constructive anthropomorphism: a functional evolutionary approach to the study of human-like cognitive abilities in animals. Proceedings of the Royal Society B, Biological Sciences 284: 20171616.
 
Arbilly, M, Laland KN (2017) The magnitude of innovation and its evolution in social animals. Proceedings of the Royal Society B, Biological Sciences 284: 20162385.
 
Burton-Chellew M, Kacelnik A, Arbilly, M, dos Santos M, Mathot KM, McNamara JM, Mengel F, van der Weele J, Vollan B (2017) The ecological and economic conditions of exploitation strategies. In: Investors and Exploiters in Ecology and Economics: Principles and Applications. Ed. by LA Giraldeau, P Heeb, M Kosfeld. Vol. 21. Strüngmann Forum Reports. Series editor J Lupp. Cambridge, MA: MIT Press.
 
Arbilly, M (2015) Understanding the evolution of learning by explicitly modeling learning mechanisms. Current Zoology 61: 341–349. 

Teaching

  • DATASCI 100: Introduction to Statistical Inference
  • DATASCI 385: Evolutionary Game Theory