Home // International Journal On Advances in Software, volume 15, numbers 1 and 2, 2022 // View article
Authors:
Takamasa Kikuchi
Hiroshi Takahashi
Keywords: social simulation; knowledge extraction; financial retirement planning; experience mapping techniques
Abstract:
Asset formation for the retirement generation is a common issue around the world and has been widely discussed in various countries. We begin this paper by surveying the research on asset formation and life planning. Then, we show a data-driven life planning support framework based on social simulation. Based on the data and simulation results, this framework is intended to run simulations based on customer attribute data and to evaluate and validate measures for customers’ retirement assets. The social simulation model is constructed based on finance theory. Machine learning methods are used for the analysis of customer features and evaluation of the policy measures. Moreover, the simulation results are represented by experience mapping techniques. The following are the key findings: our framework 1) allows for effective discussion of measures to avoid the depletion of retirement assets and 2) allows simulation results to be widely interpreted and shared not only by model developers and analysts but also by decision-makers and frontline personnel.
Pages: 54 to 64
Copyright: Copyright (c) to authors, 2022. Used with permission.
Publication date: June 30, 2022
Published in: journal
ISSN: 1942-2628