2010/06/30 by Denis Boyer, Peter D. Walsh · 116 citations
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · Psychology · #Animal Vocal Communication and Behavior #Artificial intelligence #Biology #Computer science #Data science #Diffusion and Search Dynamics #Ecology #Foraging #Machine learning #Primate Behavior and Ecology #cond-mat.dis-nn #q-bio.PE
paper · pdf · doi:10.1098/rsta.2010.0275
published in Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences 368(1933), 5645-5659 (Royal Society) · 14 pages, 4 figures, improved discussion
arxiv created 2010/10/12 · openalex publication_date 2010/11/15 · arxiv updated 2015/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Thanks to recent technological advances, it is now possible to track with an unprecedented precision and for long periods of time the movement patterns of many living organisms in their habitat. The increasing amount of data available on single trajectories offers the possibility of understanding how animals move and of testing basic movement models. Random walks have long represented the main description for micro-organisms and have also been useful to understand the foraging behaviour of large animals. Nevertheless, most vertebrates, in particular humans and other primates, rely on sophisticated cognitive tools such as spatial maps, episodic memory and travel cost discounting. These properties call for other modelling approaches of mobility patterns. We propose a foraging framework where a learning mobile agent uses a combination of memory-based and random steps. We investigate how advantageous it is to use memory for exploiting resources in heterogeneous and changing environments. An adequate balance of determinism and random exploration is found to maximize the foraging efficiency and to generate trajectories with an intricate spatio-temporal order, where travel routes emerge without multi-step planning. Based on this approach, we propose some tools for analysing the non-random nature of mobility patterns in general.