and Bayesian Models simulations are sufficient for causally explanatory and predictive claims • We will argue… – Neither of these suffice as wholly explanatory models… – but, do well for prediction… – And are replicatively and predicatively valid
a well- defined distribution of food – Establishes knowledge of food location • Food is moved to new areas – Animal no longer sure of qualiy of food patch • Bayesian model is supposed to predict behavior of animals – Prior information → Future Expectations
Java, C++) • Model a network of individual agents interacting with each other • Individual attributes produce group-level behavior, emphasis on heterogeneity • Variety of algorithms can be employed for each ―agent‖ (animal, person, etc.)
EPICURE • Tests Ideal Free Distribution (IFD) model Hypothesis: Given a distribution of food, animals should distribute themselves proportionally to the location of food (Fretwell & Lucas, 1970) Reality: Animals tend to undermatch—less than proportional amount of animals end up in food-rich areas (Godin & Keenleyside, 1984; Gillis & Kramer, 1984; Baum & Kraft, 1998)
data from previous studies • Potentially Predictively Valid – Makes novel predictions, but has not yet been formally tested in non-ABM setting • Not Structurally Valid – Does not purport to model an underlying physiological (causal) process
underdetermination – SV imposes strict constraints on the casual relationships programmed in the model by adherence to theoretical laws which limit the structure of the model. – The limitation imposed requires that the organizational structure of the model accurately represent the organizational structure of the system its modeling
not be present in biology • SV models sometimes still don’t produce law- like regularities on grounds that: – Entities are not governed by a single regularity; – Entities are too complex to adequately model using a handful of (sometimes competing) independent behavioral rules, and; – Given the complexity of the real system, the models are incapable to imitating behavior
An Empirical View. Thomas Valone, Oikos (2006). Bayesian Foraging with Only Two Patch Types. Ola Olsson, Oikos (2006). Bayes’ Theorem and its Applications in Animal Behavior. John M. McNamera, Richard F. Green, and Ola Olsson, Oikos (2006). EPICURE: EPICURE: Spatial and Knowledge Limitations in Group Foraging. Michael E. Roberts and Robert L. Goldstone, Adaptive Behavior (2006).