Arun G. Chandrasekhar, Paul Goldsmith-Pinkham, Tyler H. McCormick, Samuel Thau, Jerry Wei
arXiv 8 Mar 2024 · Econometrics · 2 citations (OpenAlex)
arXiv:2403.05704 · PDF · DOI · OpenAlex · Extracted main text
Network diffusion models are used to study things like disease transmission, information spread, and technology adoption. However, small amounts of mismeasurement are extremely likely in the networks constructed to operationalize these models. We show that estimates of diffusions are highly non-robust to this measurement error. First, we show that even when measurement error is vanishingly small, such that the share of missed links is close to zero, forecasts about the extent of diffusion will greatly underestimate the truth. Second, a small mismeasurement in the identity of the initial seed generates a large shift in the locations of expected diffusion path. We show that both of these results still hold when the vanishing measurement error is only local in nature. Such non-robustness in forecasting exists even under conditions where the basic reproductive number is consistently estimable. Possible solutions, such as estimating the measurement error or implementing widespread detection efforts, still face difficulties because the number of missed links are so small. Finally, we conduct Monte Carlo simulations on simulated networks, and real networks from three settings: travel data from the COVID-19 pandemic in the western US, a mobile phone marketing campaign in rural India, and in an insurance experiment in China.
appendix boundary found by appendix_command · 43% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Watts, Duncan J and Strogatz, Steven H (1998) Collective dynamics of ‘small-world’ networks | 0.965 | 10 | 5 | 90% |
| 2 | Beaman, Lori and BenYishay, Ariel and Magruder, Jeremy and Mobarak,… (2021) Can network theory-based targeting increase technology adoption? | 0.874 | 6 | 2 | 100% |
| 3 | Hoff, Peter D. and Raftery, Adrian E. and Handcock, Mark S (2002) Latent Space Approaches to Social Network Analysis | 0.737 | 3 | 2 | 100% |
| 4 | Kang, Yuhao and Gao, Song and Liang, Yunlei and Li, Mingxiao and Kru… (2020) Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic | 0.644 | 3 | 2 | 67% |
| 5 | Acemoglu, Daron and Chernozhukov, Victor and Werning, Iván and Whins… (2021) Optimal targeted lockdowns in a multigroup SIR model | 0.644 | 2 | 2 | 100% |
| 6 | Chandrasekhar, A. and Lewis, R (2010) Econometrics of sampled networks self | 0.644 | 2 | 2 | 100% |
| 7 | Fajgelbaum, Pablo D and Khandelwal, Amit and Kim, Wookun and Mantova… (2021) Optimal lockdown in a commuting network | 0.644 | 2 | 2 | 100% |
| 8 | Shapiro, Michael and Delgado-Eckert, Edgar (2012) Finding the probability of infection in an SIR network is NP-Hard | 0.644 | 2 | 2 | 100% |
| 9 | Contreras, Daniel and Martineau, Sébastien and Tassion, Vincent (2024) Supercritical percolation on graphs of polynomial growth | 0.511 | 2 | 1 | 100% |
| 10 | Griffith, Alan (2022) Name your friends, but only five? the importance of censoring in peer effects estimates using social network data | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Team Networks with Partially Observed Links | 0.405 | 1 | 1 |