A Myriad-Agent Framework for Insurer-Policyholder Behavioral Dynamics

Thesis Abstract

The study addresses the rising complexity of insurer–policyholder interactions in modern insurance markets, where individual decision-making and policyholder behavior interact with algorithmically mediated risk assessment and pricing processes. It identifies a gap in integrative models that capture heterogeneous agent behaviors and their emergent effects on premium dynamics, claim reporting, and policy retention within digitalized underwriting environments. The aim is to develop a Myriad-Agent Framework that unifies behavioral decision theory, bounded rationality, and multi-agent simulation to illuminate how policyholder preferences, risk perceptions, and insurer policies co-evolve under dynamic market conditions. Specific objectives are (1) to conceptualize a multi-agent model comprising heterogeneous policyholders, agents representing underwriters, adjusters, and mediating digital platforms, (2) to operationalize behavioral variables such as loss aversion, overconfidence, information asymmetry, and trust, (3) to calibrate the framework using empirical data from 1,000 insurance policy records and 200 semi-structured interviews with policyholders and practitioners, (4) to simulate scenarios incorporating machine learning-based pricing, personalized recommendations, and policy disclosures, and (5) to evaluate outcomes in terms of premium dispersion, policy lapse rates, claim filing latency, and customer lifetime value. The methodology employs a mixed-methods design anchored in agent-based modeling (ABM) and Bayesian inference. The population comprises individual policyholders across automotive, health, and property lines, with a purposive sub-sample of 400 policyholders for survey data and 40 industry practitioners for qualitative insights. Data collection instruments include a structured questionnaire validated for reliability (Cronbach’s alpha > 0.80) to measure behavioral constructs, policy documents and transaction histories extracted with consent for quantitative features, and semi-structured interview guides. Instrument validity is established through expert review and pilot testing, while reliability is confirmed via test–retest analysis. The ABM will be implemented in NetLogo and simulated over 2,000 iterations to explore emergent phenomena, with model parameters estimated and updated using a hierarchical Bayesian framework. Analytical techniques include regression analysis to identify determinants of policy retention and premature lapse, survival analysis for renewal probability, clustering to segment policyholder types, and ANOVA to compare outcomes across scenarios. The study will also apply thematic analysis to interview data to extract salient behavioral themes and policy design implications, and structural equation modeling to test hypothesized relationships among policyholder trust, perceived fairness, and engagement. Expected findings include (i) identification of critical behavioral drivers that amplify or dampen response to dynamic pricing and personalized recommendations; (ii) quantification of how heterogeneity in risk perception modulates reporting propensity and claim timing; (iii) demonstration of emergent macro-level patterns, such as clustering of high-retention cohorts under certain disclosure regimes; and (iv) evidence that digital mediation layers can either mitigate or exacerbate information asymmetry depending on design features. The study contributes to knowledge by proposing a rigorous, integrated theoretical and computational framework that links behavioral decision theory (prospect theory, bounded rationality) with institutional mechanisms (pricing algorithms, disclosure practices) in insurance. It advances methodological innovations in combining ABM with Bayesian calibration and mixed-methods validation, offering a transferable template for insurers aiming to predict policyholder responses to policy design changes. The main conclusion anticipated is that insurer–policyholder dynamics are emergent properties of interacting heterogeneous agents, where carefully engineered digital disclosures and transparent pricing feedback can align incentives, reduce adverse selection, and improve policy retention. Recommendations include design guidelines for transparent pricing interfaces, risk-communication strategies that reduce information asymmetry, and policy experimentation protocols that utilize ABM-derived insights to test the behavioral impact of new underwriting rules before large-scale deployment.