The cumulative effects (CE) model explains free-operant choice by the ratio of total numbers of responses and reinforcements, a probability-like variable. Williams (1994) argues that the model is vulnerable to experiments that disprove melioration, a local probability model. The authors note critical differences between the nonlocal CE model and local probability models that allow the CE model to handle some data with which they are incompatible. All models are simplifications of reality; hence, a model's failures are as revealing as its successes. Williams suggests that simple models may need to be abandoned in favor of a "representational" account. The authors point out that representations must be both acquired and acted on. Acquisition requires processing of responses and reinforcers; action requires decision rules. Models are simply testable suggestions for what these rules and processes might be.