Kimon Drakopoulos is an Assistant Professor of Data Sciences and Operations at USC Marshall School of Business, where he researches complex networked systems, control of contagion, information design and information economics. During the Summer of 2020 he served as the Chief Data Scientist of the Greek National COVID-19 Taskforce and Data Science and Operations Advisor to the Greek Prime Minister. He completed his Ph.D. in the Laboratory for Information and Decision Systems at MIT, focusing on the analysis and control of epidemics within networks. His current research revolves around controlling contagion, epidemic or informational as well as the use of information as a lever to improve operational outcomes in the context of testing allocation, fake news propagation and belief polarization.
| Name of Speaker | Kimon Drakopoulos |
| Schedule | 22 October 2021, 10am – 11.30am
(60 min talk + 30 min Q&A) |
| Link to Register | https://nus-sg.zoom.us/meeting/register/tZEqdeqqqzwiEtTecjPommMjKlLsJKr4UArz |
| Title | Why Perfect Tests May Not be Worth Waiting For: Information as a Commodity |
| Abstract | Information products provide agents with additional information that is used to update their actions. In many situations, access to such products can be quite limited. For instance, in epidemics, there tends to be a limited supply of medical testing kits or tests. These tests are information products because their output of a positive or a negative answer informs individuals and authorities on the underlying state and the appropriate course of action. In this paper, using an analytical model, we show how the accuracy of a test in detecting the underlying state affects the demand for the information product differentially across heterogeneous agents. Correspondingly, the test accuracy can serve as a rationing device to ensure that the limited supply of information products is appropriately allocated to the heterogeneous agents. When test availability is low and the social planner is unable to allocate tests in a targeted manner to the agents, we find that moderately good tests can outperform perfect tests in terms of social outcome. On the policy side, we use a numerical study of an evolving epidemic to confirm our theoretically derived insight that in the early stages of an epidemic with low test availability, releasing a moderately good test can be an optimal strategy. The work is forthcoming in Management Science (FastTrack). |
