Decision-theoretic models have become increasingly popular as a basis for solving agent and multiagent problems, due to their ability to quantify the complex uncertainty and preferences that pervade most nontrivial domains. However, this quantitative nature also complicates the problem of constructing models that accurately represent an existing agent or multiagent system, leading to the common question, "Where do the numbers come from?" In this work, we present a method for exploiting knowledge about the qualitative structure of a problem domain to automatically derive the correct quantitative values that would generate an observed pattern of agent behavior. In particular, we propose the use of piecewise linear functions to represent probability distributions and utility functions with a structure that we can then exploit to more efficiently compute value functions. More importantly, we have designed algorithms that can (for example) take a sequence of actions and automatically generate a reward function that would generate that behavior within our agent model. This algorithm allows us to efficiently fit an agent or multiagent model to observed behavior. We illustrate the application of this framework with examples in multiagent modeling and social simulation, using decision-theoretic models drawn from the alphabet soup of existing research (e.g., MDPs, POMDPs, Dec-POMDPs, Com-MTDPs).