Computational Social Science Doctoral Dissertation – Patients, Premiums, and Public Policy: Modeling Health Insurance Markets using Agent Computing – Kevin Comer
Notice and Invitation
Oral Defense of Doctoral Dissertation
Computational Social Science
Department of Computational and Data Sciences
College of Science
George Mason University
Bachelor of Science, University of Virginia, 2007
Master of Science, George Mason University, 2010
Patients, Premiums, and Public Policy: Modeling Health Insurance Markets using Agent Computing
Thursday, April 13, 2017, 10:00 a.m.
Exploratory Hall, Room 3301
All are invited to attend.
Robert Axtell, Chair
William G. Kennedy
This dissertation focuses on the assessment of adverse selection as a result of uncertainty and asymmetric information in a market of buyers and sellers. This dissertation seeks to provide two novel contributions to science – the development of a true-scale (one agent to one household) agent-based model of the individual health insurance market at the state level, and the assessment of the impacts of various policy implementations on the individual health insurance market. These impacts will cover not only the participation rates of individuals in the market, but also the price of coverage, the distribution of subscribers across simulated plans, and the expected net revenue of policy elements.
The first chapter is an agentized replication of the seminal model by Rothschild and Stiglitz, assessing the effect of asymmetric information on a stylized insurance market of buyers and sellers. The agent-based model replication is able to validate the findings of Rothschild and Stiglitz, that the heterogeneous nature of the propensity of risk across the buyer population, and the vested interest of the buyer to not disclose their probability of risk, leads to adverse selection and a failure in the insurance market. However, the assertion of the effectiveness of signaling that Rothschild and Stiglitz is assessed in this model, as well as the inclusion of subjective probability, or the uncertainty a perspective buyer might have over their own risk of accident.
The second chapter is the discussion of a baseline agent-based model representation of the individual health insurance market at the state level, representing one hundred thousand (100,000) buyer agents (termed “patients”) and ten (10) seller agents (termed “payers”). Using only empirical data for the income distribution and medical expenditure distribution, the baseline model is able to quantitatively reproduce large-scale behaviors seen in the health insurance market, most notably the price point elasticity of demand for health insurance, estimated at -0.6 by the RAND Health Insurance Experiment.
The fourth and penultimate chapter of this dissertation analyzes the six major components of the Patient Protection and Affordable Care Act or ACA, enacted on March 23, 2010, using modeled policy extensions from the baseline model described in the previous chapter. The coverage mandate leads to an increase in premium prices, while decreasing the number of subscribers that choose to participate in the individual health insurance market, which is indicative of adverse selection. While the individual mandate helps to mitigate the latter, the true mitigation policy is risk adjustment across the market. However, this leads to the consolidation of patients into fewer plans, and the departure of firms from the marketplace entirely.
A copy of Kevin’s dissertation is available for examination from Karen Underwood, Department of Computational and Data Sciences, 373 Research Hall. The dissertation is available to read only within the Department and cannot be taken out of the Department or copied.