Complexity science and its primary tool, agent-based simulation modeling, are potent new ways to understand and help manage health systems. Syed Mehmud and Alan Mills will show you how to use agent-based simulation models, together with results from complexity science and behavioral economics, to address problems related to health insurance exchanges. In this session, you will learn: • How to identify exchange problems that are amenable to agent-based simulation modeling • How to use agent-based simulation modeling, together with results from complexity science and behavioral economics, to address exchange problems.

One of the first projects undertaken by Claim Analytics was to create a model to predict return to work for group insurance claimants with long term disabilities. This report, sponsored in part by the Society of Actuaries Health Section , is a case study on creating such a model.

This paper proposes a method for valuing American options using a Monte Carlo simulation approach. Our approach can be used to price the reset feature found in some equity-linked insurance contracts. We also show how to modify the stochastic mesh model of Broadie and Glasserman to incorporate quasi-Monte Carlo in the simulation and thus improve the efficiency. From NAAJ, July 2001, Volume 5, Number 3.

This paper discusses a generalization of the classical risk theory process, in which the Poisson claims process is replaced by a nonhomogeneous Poisson process, with the intensity being a periodic function. Results are obtained by simulation.

This paper discusses the use of a special type of quasi–Monte Carlo simulation called the lattice rule method to value options whose payoffs depend on several stochastic variables. It provides an overview of lattice rules, discusses implementation, and demonstrate their efficiency relative to standard Monte Carlo and classical quasi–Monte Carlo methods. It also demonstrates the improvement in the estimation of partial derivatives of these prices the so-called Greeks.

The purpose of this paper is to explore pension plan dynamics— – the behavior of a defined- benefit pension plan over time in an uncertain environment. We proceed by simulating the behavior of a model plan in a stochastically varying economic environment. While both the model and its environment are simplified, the model displays interesting behavior suggesting policy considerations that should be included in the design and funding of these plans.

The purpose of this paper is to explore pension plan metrics— – measuring the behavior of a defined- benefit pension plan in an uncertain environment. Our goal is to measure and evaluate the quantitative impact of competing policy choices. We proceed by investigating some basic pension metrics, evaluating the characteristics of each using a simulated pension plan. We find that some typical metrics— – such as the mean and standard deviation— – are useful only in certain circumstances and that other less traditional measurements are sometimes better suited to evaluating policy choices.

The purpose of this paper is to explore a methodology for choosing among techniques for managing contribution volatility in defined- benefit pension plans. A frequently used – and as often criticized – method for managing contribution volatility is to base actuarial cost on smoothed assets. Our goal in this paper is to develop a methodology for measuring and evaluating the quantitative impact of competing asset smoothing policies. We proceed by investigating a series of basic pension metrics, evaluating the characteristics of each using a simulated pension plan. We then use normalized and weighted combinations of these metrics to determine an optimal smoothing policy.

Presentation from the 2009 Health Spring Meeting focused on reserving for health benefits - nice detail versus other presentations.

Abstract of Research Project on Interest Rate Risk