Andrew Caplin, Daniel Martin, Philip Marx, Anastasiia Morozova, Leshan Xu. Testing Capacity-Constrained Learning. 2026. Revise & Resubmit at Experimental Economics.

We introduce a general test of capacity-constrained learning models. Learning has capacity constraints when the set of possible ways to learn is exogenously fixed, as in the widely used fixed-capacity versions of rational inattention (Sims 2003) and efficient coding (Woodford 2012). With such models, changes in incentives do not alter the extent of attention, only how individuals decide to allocate their scarce attention. We show that choice data are consistent with capacity-constrained learning if and only if they satisfy a No Improving (Action or Attention) Switches (NIS) condition. Based on existing experiments in which the incentives for being correct are varied, we find strong evidence that participants fail NIS for a wide range of standard perceptual tasks: identifying the proportion of ball colors, recognizing shapes, and counting the number of balls. We further show that violations of NIS occur systematically in response to higher incentives, suggesting that incentives often expand attention beyond what capacity-constrained models allow. However, we find that this is not true for all existing perceptual tasks in the literature, which offers insights into settings where we do or do not expect incentives to impact the extent of attention.

Anastasiia Morozova. Rational Signals, Biased Ears: Power and Social Learning Inefficiencies.

In a novel lab-in-the-field experiment within a real company, I leverage endogenous hierarchy to assess the overweighting/underweighting of others' signals relative to their hierarchical distance, organizational layer, professional prestige, and social distance. I hypothesize that power, defined as hierarchical distance, distorts belief updating: individuals discount signals from subordinates and overweight those from superiors. The design allows me to distinguish heuristic overweighting from belief-based ability assessments and from strategic deference when guesses are visible. Using the results from the paired experiment, I predict information aggregation quality within an organizational network against Bayesian and DeGroot (naive) benchmarks, subject to informational asymmetries that model relevant workplace scenarios in the aligned-incentives environment, and identify welfare implications of these heuristics as well as scenarios in which they can be efficiency-improving.

Austin Brooksby, Anastasiia Morozova. The When, What and Why of AI Use in Online Preference Elicitation Experiments.

Anastasiia Morozova, Alexey Upravitelev. Complexity Aversion and Herding in Financial Markets.