Animals must make estimates about possible resources in order to choose the resource which will save them time and energy while conferring high energetic content. In order to make the most optimal decision, foragers must use various parameters to come up with an accurate estimate for each possible alternative. Learning rules allow us the possibility of analyzing which parameters animals may be using in order to make the best decision. We use compare known learning rules (i.e. Linear Operator Rule, Relative Payoff Sum Rule, Perfect Memory) and experimental data extracted from bumblebees (Bombus impatiens) subjected to a two armed bandit scenario in order to find what learning rule best describes their foraging choices in a changing environment. Our findings suggest that bumblebees seem to be using parameters consistent with the Linear Operator Rule and the Relative Payoff Rule. More importantly, our results suggest that there is great variance in learning rule use between individuals
