Prediction

Prediction is fundamental to successful decision-making (Budaev et al. 2019) and, often, to modeling adaptation in an ABM. (Railsback and Harvey 2020 extensively discuss the use of simple predictions in modeling difficult decisions by model agents.) Some ABMs use “explicit prediction”: the agents’ adaptive behaviors or learning are based on explicit estimates of future conditions (future values of both agent and environment state variables) and future consequences of decisions. For these ABMs, explain how agents predict future conditions and decision consequences. What internal models of future conditions or decision consequences do agents use to make predictions for decision-making? Models that do not include explicit prediction often include “implicit prediction”: hidden or implied assumptions about the future consequences of decisions. A classic example of implicit prediction is that following a gradient of increasing food scent will lead an agent to food. Describe: • How the models of adaptive behavior use either explicit or implicit prediction. • The rationale for how prediction is represented: is the model designed to represent how the agents actually make predictions? Or is prediction modeled as it is simply because it produces useful behavior?

• The adaptive behavior of households is based on the implicit prediction that moving when the objective measure is below the tolerance threshold is likely to eventually result in the household occupying a location where the tolerance threshold is met permanently
• The utility measure estimates utility over a time horizon by using the explicit prediction that profit P and failure risk F will remain constant over the time horizon. This assumption is accurate in this model because the patches’ P and F values are static.
• In this version we modify the algorithm for predicting future reproduction and survival to include differences between day and night. We do this simply by keeping track of whether each of the future hours evaluated (Step 2 of the algorithm described in Section 5.4) is in day or night, and using memory of habitat conditions during the previous night (or day) to predict future conditions. A Daphnia deciding what to do during a daytime hour uses the prediction that future daytime hours until the time horizon will all have the same growth and survival conditions as the patch it is evaluating, and that future nighttime hours will have conditions it “remembers” from the patch and α value the Daphnia actually used during the most recent night hour. At night, a Daphnia predicts future day conditions from the patch and α value it used in the most recent day hour.