Optimization of Social Security Systems Under Uncertainty

Ermolieva, T.Y. (2002). Optimization of Social Security Systems Under Uncertainty. IIASA Interim Report. IIASA, Laxenburg, Austria: IR-02-077

[thumbnail of IR-02-077.pdf]

Download (127kB) | Preview


The aim of this paper is to develop optimization-based approaches for modeling multi-agent and multi-regional social security systems under demographic and economic uncertainties. Conceptually, the proposed model deals with the production and consumption processes coevolving with "birth-and-death" processes of the participating agents. Uncertainties concern fertility, life expectancy, migration and such economic and health variables as rate of return, incomes and disability rates. The goal is to satisfy a reasonable and secure consumption of agents. There is considerable similarity between the decisions involved in the optimization of social security systems and the production planning processes: in both cases "savings" are taken in periods of low demand and "dissavings" when the demand turns high. The significant difference of our problem is that decisions on savings and dissavings may have large-scale effects on the whole economy, in particular, they effect returns on savings through investments and capital formation. The model tracks incomes and expenditures of agents, their savings and dissavings, as well as intergenerational and interregional transfers of wealth. Robust management strategies are defined by using such risk indicators as ruin, shortfall and Conditional-Value-at-Risk (CVaR). The adaptive Monte Carlo optimization procedure is proposed to derive optimal decisions. Numerical experiments and possible applications to catastrophic risk management are discussed.

Item Type: Monograph (IIASA Interim Report)
Research Programs: Social Security Reform (SSR)
Young Scientists Summer Program (YSSP)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:14
Last Modified: 27 Aug 2021 17:17
URI: https://pure.iiasa.ac.at/6711

Actions (login required)

View Item View Item