Description:

Paul Goldsmith-Pinkham (Yale University): Non-robustness of diffusion estimates on networks with measurement error

Abstract: Network diffusion models are used to study disease transmission, information spread, technology adoption, and other socio-economic processes. We show that estimates of these diffusions are highly non-robust to mismeasurement. First, even when the network is measured perfectly, small and local mismeasurement in the initial seed generates a large shift in the locations of the expected diffusion. Second, if instead the initial seed is known, small and arbitrarily structured measurement error in links, with the share of missed links close to zero, causes diffusion forecasts to be significant under-estimates. Such failures exist even when the basic reproductive number is consistently estimable. We explore difficulties implementing possible solutions, such as estimating the measurement error or implementing widespread detection efforts. Finally, we conduct simulations on synthetic and real networks from three settings: travel data from the COVID-19 pandemic, a mobile phone marketing campaign in rural India, and an insurance experiment in China.