Optimal resource allocation and technology adoption in dynamic systems

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The goal of my research is to develop a series of frameworks for decision making in the context of dynamic systems, specifically systems driven by population trends, behavior change, or technological advancement. In this program of research, I will develop operations research models to address three challenging features of dynamic decision making: 1) value of information analysis in dynamic systems; 2) technology adoption models incorporating the expectation of technological change; and 3) multiple-decision-maker frameworks to develop an understanding of technology adoption in fragmented decision systems. The operations research models and analytical methods I will develop to investigate these management questions are readily adapted and generalized to responsive decision making across public and private sector problems. Each of these streams of research is inspired by a real-world decision problem. As a result, in addition to methodologic advancements, my work has the potential to have substantial policy impact. Value of information in dynamic systems: This stream of work is inspired by the difficult problem of when to perform epidemiologic monitoring of an infectious disease in order to proactively respond to an “outbreak”. Traditional approaches to identifying optimal prevention and treatment strategies rely on deterministic models overlooking the full consequences of the stochastic process. Methodologically, this stream of work will integrate Bayesian approaches to value-of-information, infectious disease modeling using stochastic non-linear dynamic systems, and optimal control theory. Technology adoption with the expectation of technological change: I approach this problem by investigating how optimal technology-consumption decisions are influenced by the existence of reserve-use technology alternatives, the ability to influence the long-term value of a technology through managed use, and the ability to influence the rate new technologies “arrive” through research and development subsidies. This problem is encountered in many areas, including management of natural resources, information technology infrastructure, and of the global crisis of antimicrobial resistance. Technology adoption in fragmented decision systems: Fragmented decision making in the provision of preventive services covered by insurance may cause suboptimal investment in technologies with long-term benefit because neither decision maker bears all the long-term consequences nor obtains all the benefits of investment. Correcting for the inefficiency caused by system fragmentation may be possible with contracts between decision makers, specifically a payment from the second insurance provider to the first. In this stream of research, I extend this framework to include realistic elements that may affect the ability of contracts to overcome fragmented decision making such as differences in ability to negotiate prices with technology manufacturers and information asymmetry.