Accession Number : AD1007105


Title :   Coordination and Collective Decision Making


Descriptive Note : Technical Report,17 Aug 2011,15 May 2015


Corporate Author : Princeton University Princeton United States


Personal Author(s) : Levin,Simon A ; Leonard,Naomi E ; Couzin,Iain D


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1007105.pdf


Report Date : 21 Aug 2015


Pagination or Media Count : 32


Abstract : This project studied individual search behavior and coordination of large groups of individual agents, and how consensus is achieved indecision-making. Animal groups frequently display highly coordinated movements, and provide an excellent vehicle by which to understand general principles that underlie collective behavior. We studied collective movement in animal populations, and the relationship between individual decision rules and emergent collective patterns, especially when different individuals have conflicting information. We expanded classical search models, extending multi-armed bandit and other Bayesian approaches to information gathering. We investigated the role of communication topology in generic models of group motion and decision-making, with particular reference to optimizing performance metrics such as speed, accuracy and robustness of decisions to intrinsic or extrinsic sources of error. Central was the degree to which collective optimization can be approximated in situations where individuals operate for individual benefit rather than group success. We employ a comprehensive approach using mathematical models of collective foraging (self-propelled interacting particles) within an evolutionary framework, and tools from dynamical systems theory, statistical mechanics, adaptive dynamics and state-of-the-art computational hardware and algorithms. We used large-scale individual-based simulation and mathematical analysis under biologically realistic assumptions, yet the results can help us elucidate fundamental biological principles that can be relevant to a wide range of scales and species from social bacteria to large mammals.


Descriptors :   decision making , information transfer , algorithms , BEHAVIORAL RESEARCH , BAYESIAN INFERENCE , mathematical models , COMPUTATIONAL BIOLOGY


Distribution Statement : APPROVED FOR PUBLIC RELEASE