title: Social foraging with partial (public) information.
abstract: Group foragers can utilize public information to better estimate patch quality and arrive at more efficient patch-departure rules. However, acquiring such information may come at a cost; e.g. reduced search efficiency. We present a Bayesian group-foraging model in which social foragers do not require full awareness of their companions' foraging success; only of their number. In our model, patch departure is based on direct estimates of the number of remaining items. This is achieved by considering all likely combinations of initial patch-quality and group foraging-success; given the individual forager's experience within the patch. Slower rates of information-acquisition by our 'partially-aware' foragers lead them to over-utilize poor patches; more than fully-aware foragers. However, our model suggests that the ensuing loss in long-term intake-rates can be matched by a relatively low cost to the acquisition of full public information. In other words, we suggest that group-size offers sufficient information for optimal patch utilization by social foragers. We suggest, also, that our model is applicable to other situations where resources undergo 'background depletion', which is coincident but independent of the consumer's own utilization.
authors.affiliation: Department of Life-Sciences, Ben-Gurion University, Eilat Campus, Eilat, Israel; The Interuniversity Institute for Marine Sciences, PO Box 469, Eilat, Israel. Electronic address: ofrimann@gmail.com.
authors.forename: Ofri
authors.initials: O
authors.lastname: Mann
authors.affiliation: Department of Life-Sciences, Ben-Gurion University, Eilat Campus, Eilat, Israel; The Interuniversity Institute for Marine Sciences, PO Box 469, Eilat, Israel. Electronic address: mkiflawi@bgu.ac.il.
authors.forename: Moshe
authors.initials: M
authors.lastname: Kiflawi
mesh_terms: D022126:Access to Information; D001364:Awareness; D001499:Bayes Theorem; D003258:Consumer Behavior / Q000706:statistics & numerical data*; D017753:Ecosystem; D005247:Feeding Behavior; D016247:Information Storage and Retrieval / Q000706:statistics & numerical data*; D019359:Knowledge; D008962:Models, Theoretical; D012919:Social Behavior
keywords: Bayesian foraging; Group foraging; Information use; Patch quality estimation; Public information