Simulations of many people’s decisions are used in public health and safety as well as to support policymaking.These simulations rely on creditable models of individual decision-making. An obvious approach is to develop a list of plausibleactions and to then evaluate the benefits of each in the current situation to make the decision. However, such evaluations canbe implausible, e.g., zero-intelligence traders in economics, or impracticable because the approach is computationally intensivefor large-scale simulations. As a result, a commonly used approach is to select randomly from the plausible actions. Withoutdata on how people would actually chose, a random number from a uniform distribution over the plausible options is often usedto represent the unknown cognition. However, we claim that substituting a uniform random distribution for how people makedecisions is making very strong claims about the process and we will present data demonstrating it is simply wrong.