  Undesired bias afflicts both human and algorithmic decision making, and may
be especially prevalent when information processing trade-offs incentivize the
use of heuristics. One primary example is \textit{statistical discrimination}
-- selecting social partners based not on their underlying attributes, but on
readily perceptible characteristics that covary with their suitability for the
task at hand. We present a theoretical model to examine how information
processing influences statistical discrimination and test its predictions using
multi-agent reinforcement learning with various agent architectures in a
partner choice-based social dilemma. As predicted, statistical discrimination
emerges in agent policies as a function of both the bias in the training
population and of agent architecture. All agents showed substantial statistical
discrimination, defaulting to using the readily available correlates instead of
the outcome relevant features. We show that less discrimination emerges with
agents that use recurrent neural networks, and when their training environment
has less bias. However, all agent algorithms we tried still exhibited
substantial bias after learning in biased training populations.
