A tunable algorithm for collective decision-making. Export

@article{citeulike:927761, abstract = {Complex biological systems are increasingly understood in terms of the algorithms that guide the behavior of system components and the information pathways that link them. Much attention has been given to robust algorithms, or those that allow a system to maintain its functions in the face of internal or external perturbations. At the same time, environmental variation imposes a complementary need for algorithm versatility, or the ability to alter system function adaptively as external circumstances change. An important goal of systems biology is thus the identification of biological algorithms that can meet multiple challenges rather than being narrowly specified to particular problems. Here we show that emigrating colonies of the ant Temnothorax curvispinosus tune the parameters of a single decision algorithm to respond adaptively to two distinct problems: rapid abandonment of their old nest in a crisis and deliberative selection of the best available new home when their old nest is still intact. The algorithm uses a stepwise commitment scheme and a quorum rule to integrate information gathered by numerous individual ants visiting several candidate homes. By varying the rates at which they search for and accept these candidates, the ants yield a colony-level response that adaptively emphasizes either speed or accuracy. We propose such general but tunable algorithms as a design feature of complex systems, each algorithm providing elegant solutions to a wide range of problems.}, address = {*School of Life Sciences, Arizona State University, P.O. Box 874501, Tempe, AZ 85287-4501.}, author = {Pratt, S. C. and Sumpter, D. J.}, citeulike-article-id = {927761}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0604801103}, citeulike-linkout-1 = {http://www.pnas.org/content/103/43/15906.full.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/103/43/15906.full.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/17038502}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=17038502}, day = {24}, doi = {10.1073/pnas.0604801103}, issn = {0027-8424}, journal = {Proc Natl Acad Sci U S A}, keywords = {ants, collective\_behaviour, decision, information, model, scaling, speed-accuracy}, month = {October}, number = {43}, pages = {15906--15910}, posted-at = {2006-11-03 19:12:15}, priority = {4}, title = {A tunable algorithm for collective decision-making.}, url = {http://dx.doi.org/10.1073/pnas.0604801103}, volume = {103}, year = {2006} }

TY - JOUR ID - citeulike:927761 L3 - citeulike-article-id:927761 N2 - Complex biological systems are increasingly understood in terms of the algorithms that guide the behavior of system components and the information pathways that link them. Much attention has been given to robust algorithms, or those that allow a system to maintain its functions in the face of internal or external perturbations. At the same time, environmental variation imposes a complementary need for algorithm versatility, or the ability to alter system function adaptively as external circumstances change. An important goal of systems biology is thus the identification of biological algorithms that can meet multiple challenges rather than being narrowly specified to particular problems. Here we show that emigrating colonies of the ant Temnothorax curvispinosus tune the parameters of a single decision algorithm to respond adaptively to two distinct problems: rapid abandonment of their old nest in a crisis and deliberative selection of the best available new home when their old nest is still intact. The algorithm uses a stepwise commitment scheme and a quorum rule to integrate information gathered by numerous individual ants visiting several candidate homes. By varying the rates at which they search for and accept these candidates, the ants yield a colony-level response that adaptively emphasizes either speed or accuracy. We propose such general but tunable algorithms as a design feature of complex systems, each algorithm providing elegant solutions to a wide range of problems. IS - 43 JF - Proc Natl Acad Sci U S A SN - 0027-8424 AD - *School of Life Sciences, Arizona State University, P.O. Box 874501, Tempe, AZ 85287-4501. EP - 15910 TI - A tunable algorithm for collective decision-making. VL - 103 SP - 15906 KW - ants KW - collective_behaviour KW - decision KW - information KW - model KW - scaling KW - speed-accuracy AU - Pratt, SC AU - Sumpter, DJ PY - 2006/10/24/ UR - http://dx.doi.org/10.1073/pnas.0604801103 ER -