Top of page
Skip to main content
Main content

Michal ArbillyLecturer | QTM & Biology

Biography

Dr. Arbilly is a lecturer at Emory University’s Departments of Biology and Quantitative Theory and Methods. 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

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