Stochastic Processes in Modeling Life Expectancy in Japan

Abstract

Purpose: The aim of the study was to assess the stochastic processes in modeling life expectancy in Japan.

Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.

Findings: The study indicated that these models account for various unpredictable factors that affect life expectancy, such as genetic differences, lifestyle choices, environmental influences, and healthcare accessibility. By utilizing stochastic processes, researchers can create more accurate and dynamic representations of life expectancy that reflect real-world complexities. These models often employ techniques like Markov chains and Poisson processes to predict the probability of survival and the occurrence of death at different ages. The findings indicate that stochastic models can provide more precise life expectancy estimates compared to traditional deterministic models, which often rely on fixed assumptions and averages. Consequently, these models are crucial for actuaries, public health officials, and policymakers who require reliable life expectancy predictions to design effective health interventions, insurance policies, and retirement plans. Furthermore, stochastic modeling allows for the simulation of various scenarios and the assessment of potential impacts of changing conditions, offering a robust framework for understanding and managing the uncertainties associated with human longevity.

Implications to Theory, Practice and Policy: Markov chain theory, renewal theory and semi-markov process theory may be used to anchor future studies on assessing the stochastic processes in modeling life expectancy in Japan. Enhancing the quality and collection of health data is crucial for the effective application and refinement of stochastic models. Developing targeted health policies that address socio-economic and geographic disparities in life expectancy is crucial. Utilizing insights from hidden Markov models can help design interventions specifically for disadvantaged groups, thereby improving their health outcomes and reducing life expectancy disparities.