When Learners Shape Rewards: Learning Dynamics in Trading Populations

Market simulation is valuable for understanding collective market behavior and evaluating trading strategies under interaction. Existing work emphasizes how traders interact or how well individual agents perform. Less clear is how collective behavior and the benefits of learning change as more participants continually revise their decision rules. We introduce FactorMarket Lab (FML), coupling factor-based stock predictors with a multi-stock order-book market. Market interaction generates new training examples that, once their labels become available, enter subsequent model updates. Traders share an initial model within each family but independently sample available data and update separate copies. Historical experiments demonstrate learning benefits, and interactive measurements show behavioral differentiation from a shared start. We vary peer learning density and update frequency, comparing the same focal cohort with and without learning. Across the tested populations, behavioral differentiation varies with learning density and update timing, with distinct responses across models. The gain from a trader's own updates also depends on how its peers learn: more widespread or frequent learning is not uniformly better. In mixed-model populations, conditions favoring collective outcomes need not maximize focal learning gains. These findings motivate evaluating continual learning as a population process, not only as an individual capability.