Michael Strevens (NYU) gives a talk at the 6th Munich-Sydney-Tilburg Conference on "Models and Decisions" (10-12 April, 2013) titled "Idealization, Prediction, Difference-Making". Abstract: Every model leaves out or distorts some factors that are causally connected to its target phenomena – the phenomena that it seeks to predict or explain. If we want to make predictions, and we want to base decisions on those predictions, what is it safe to omit or to simplify, and what ought a causal model to capture fully and correctly? A schematic answer: the factors that matter are those that make a difference to the target phenomena. There are several ways to understand the notion of difference-making. Which are the most useful to the forecaster, to the decision-maker? This paper advances a view.