{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/collective-learning-from-individual","title":"Collective learning from individual experiences and information transfer during group foraging","arxiv_id":"1901.07465","date":"2019-01-22","proceeding":null,"authors":[],"abstract":"Living in groups brings benefits to many animals, such as a protection\nagainst predators and an improved capacity for sensing and making decisions\nwhile searching for resources in uncertain environments. A body of studies has\nshown how collective behaviors within animal groups on the move can be useful\nfor pooling information about the current state of the environment. The effects\nof interactions on collective motion have been mostly studied in models of\nagents with no memory. Thus, whether coordinated behaviors can emerge from\nindividuals with memory and different foraging experiences is still poorly\nunderstood. By means of an agent based model, we quantify how individual memory\nand information fluxes can contribute to improving the foraging success of a\ngroup in complex environments. In this context, we define collective learning\nas a coordinated change of behavior within a group resulting from individual\nexperiences and information transfer. We show that an initially scattered\npopulation of foragers visiting dispersed resources can gradually achieve\ncohesion and become selectively localized in space around the most salient\nresource sites. Coordination is lost when memory or information transfer among\nindividuals is suppressed. The present modelling framework provides predictions\nfor empirical studies of collective learning and could also find applications\nin swarm robotics and motivate new search algorithms based on reinforcement.","url_abs":"http://arxiv.org/abs/1901.07465v1","url_pdf":"http://arxiv.org/pdf/1901.07465v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"collective-learning-from-individual","repo_url":"https://github.com/AndreaFalconCortes/CollectiveLearning-1D-JAVA-Script-","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}