Longitudinal surveys are a crucial component of behavioral research. Such surveys, however, face significant gaps in the data created by item and unit non-responses as well as semantic gaps resulting from questionnaires, assessed trends, and data collection methods evolving over time. Using 15 waves of vaccination surveys as a test-bed, we demonstrate how modern AI techniques can bridge both item and unit gaps, originating from non-response, and semantic gaps, originating from instrument evolution. We address these gaps through a two-component framework. We leverage LLM-generated semantic embeddings of survey questions to encode question meaning, enabling a Deep & Cross Network used for imputation to jointly model responses across item semantics, individual characteristics, and temporal dynamics. This structure directly addresses survey evolution by operating in learned semantic space. To overcome data scarcity, we use cluster-informed synthetic data generation via hierarchical prompting that produces synthetic responses preserving distributional properties and empirical cluster structure. Our approach achieves a strong improvement in semantic gap tasks and 80-90% synthetic data fidelity, providing practical solutions for evolving longitudinal studies.

Agent-based Models (ABMs) are valuable tools for policy analysis. ABMs help analysts explore the emergent consequences of regulatory and policy interventions in multi-agent decision-making settings. But the validity of inferences drawn from ABM explorations depends on the quality of the ABM agents' behavioral models. Prior approaches for specifying behaviors have limitations. This paper examines the value of reinforcement learning (RL) models as adaptive, high-performing, and behaviorally-valid models of agent decision-making in ABMs. We discuss the value of RL for modeling agents' utility-maximizing behaviors in policy-relevant ABMs. We address the problem of adapting RL algorithms to handle multi-agency in games by adapting and extending methods from recent literature. We evaluate examples of such RL-based ABM agents via experiments on two policy-relevant ABMs: a Minority Game ABM, and an ABM of Influenza Transmission. The RL behavioral models can outperform the default adaptive behavioral models. We also run analytic experiments on our RL-equipped ABMs: explorations of the effects of dynamic behavioral heterogeneity in a population, the impact of social network factors on adaptability, and the emergence of synchronization in a community. Our results suggest that the RL formalism can be an efficient abstraction for behavioral models in ABMs.

Previous modeling studies have identified the vaccination coverage level necessary for preventing influenza epidemics, but have not shown whether this critical coverage can be reached. Here we use computational modeling to determine, for the first time, whether the critical coverage for influenza can be achieved by voluntary vaccination. We construct a novel individual-level model of human cognition and behavior; individuals are characterized by two biological attributes (memory and adaptability) that they use when making vaccination decisions. We couple this model with a population-level model of influenza that includes vaccination dynamics. The coupled models allow individual-level decisions to influence influenza epidemiology and, conversely, influenza epidemiology to influence individual-level decisions. By including the effects of adaptive decision-making within an epidemic model, we can reproduce two essential characteristics of influenza epidemiology: annual variation in epidemic severity and sporadic occurrence of severe epidemics. We suggest that individual-level adaptive decision-making may be an important (previously overlooked) causal factor in driving influenza epidemiology. We find that severe epidemics cannot be prevented unless vaccination programs offer incentives. Frequency of severe epidemics could be reduced if programs provide, as an incentive to be vaccinated, several years of free vaccines to individuals who pay for one year of vaccination. Magnitude of epidemic amelioration will be determined by the number of years of free vaccination, an individuals' adaptability in decision-making, and their memory. This type of incentive program could control epidemics if individuals are very adaptable and have long-term memories. However, incentive-based programs that provide free vaccination for families could increase the frequency of severe epidemics. We conclude that incentive-based vaccination programs are necessary to control influenza, but some may be detrimental. Surprisingly, we find that individuals' memories and flexibility in adaptive decision-making can be extremely important factors in determining the success of influenza vaccination programs. Finally, we discuss the implication of our results for controlling pandemics.