Psychological and Behavioral Modeling: A Structured Review of Methods, Datasets, Applications, and Open Challenges

Psychological and behavioral modeling turns cognition, emotion, and motivation into computable quantities that can be related to observable behavior. The field has moved from theory-driven rules to statistical machine learning, deep learning, large language models (LLMs), and foundation models of cognition, but methods are fragmented, results are seldom comparable, and the link between opaque predictors and psychological theory is unsettled. This structured narrative review, reported with a PRISMA-style selection flow and an explicit evidence-tier coding, frames the field as a multilevel system. It organizes research into five modeling classes (psychological states, behavioral capabilities, individual–environment interaction, individual dynamics, and group psychology), defined by unit of analysis, target, and direction of inference, and it positions system dynamics, in the stock–flow–feedback sense, as one model family within that scheme rather than as a synonym for dynamical-systems modeling. We score the computational readiness of the theories that supply model ontologies on two rubric-based axes, formal specification and measurement maturity, and compare model families by assumptions, data needs, interpretability, and failure modes. Four claims are defended, each with stated boundary conditions and counterexamples: theory readiness, not algorithmic capability, determines where theory-informed learning is feasible; the dominant use of models shifts from individual-level estimation to collective-level simulation without a parallel shift in validation; multilevel data, not model capacity, constrain cross-scale modeling; and readiness claims must be indexed to a graded evidence base. We catalogue datasets, including small-group, dyadic, and longitudinal-network resources, review applications in mental health, education, decision support, and emergency management with their evidentiary status made explicit, treat military use as an ethically bounded case, and turn five recurrent bottlenecks into research-practice recommendations paired, where possible, with testable predictions.