A Nonlinear Copula-Graph Agent-Based Framework for Cross-Generational Risk Modeling in Pension Systems
A Nonlinear Copula-Graph Agent-Based Framework for Cross-Generational Risk Modeling in Pension Systems

Author(s): Radostin Vazov
Subject(s): Politics / Political Sciences, Politics, Economy, Business Economy / Management, Welfare systems, Financial Markets, Socio-Economic Research
Published by: Университет за национално и световно стопанство (УНСС)
Keywords: pension systems; pension fund; Generational Agent Network; Non-Parametric Copula Layer; risk management
Summary/Abstract: We propose a novel framework for modeling cross-generational risks in pension systems by integrating agent-based dynamics with non-parametric copulas and graph-based causal inference. Traditional pension models often rely on linear correlation assumptions, which fail to capture complex interdependencies between generations, such as asymmetric tail risks and demographic cascades. The proposed method introduces a Generational Agent Network (GAN), where every cohort is depicted as an independent agent possessing attributes that denote participation levels, longevity, and intergenerational support ratios. These agents communicate via a Non-Parametric Copula Layer (NCL) that models nonlinear relationships without rigid parametric constraints, which supports the identification of emerging risks such as concurrent longevity shocks. Additionally, a Graph Causality Engine (GCE) detects cross-generational causal relationships, illustrating the nonlinear impact of Baby Boomers’ retirement decisions on the economic pressures faced by later generations. The framework converts traditional actuarial inputs into agent states by employing deep learning methods, subsequently feeding risk metrics back into actuarial valuation to dynamically modify reserve requirements. The model, deployed on a heterogeneous computing framework, integrates GPU-accelerated copula computation with decentralized graph analysis to perform real-time policy simulations. Our method resolves key deficiencies in pension risk assessment by directly including intergenerational linkages and complex nonlinear interactions, thus establishing a stronger basis for extended financial strategy. The findings illustrate the framework’s capacity to identify concealed risks and guide flexible funding approaches, which holds substantial relevance for policymakers and pension fund administrators.

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