arXiv 11 Feb 2023 · Econometrics
arXiv:2302.05747 · PDF · DOI · OpenAlex · Extracted main text
Designing individualized allocation of treatments so as to maximize the equilibrium welfare of interacting agents has many policy-relevant applications. Focusing on sequential decision games of interacting agents, this paper develops a method to obtain optimal treatment assignment rules that maximize a social welfare criterion by evaluating stationary distributions of outcomes. Stationary distributions in sequential decision games are given by Gibbs distributions, which are difficult to optimize with respect to a treatment allocation due to analytical and computational complexity. We apply a variational approximation to the stationary distribution and optimize the approximated equilibrium welfare with respect to treatment allocation using a greedy optimization algorithm. We characterize the performance of the variational approximation, deriving a performance guarantee for the greedy optimization algorithm via a welfare regret bound. We implement our proposed method in simulation exercises and an empirical application using the Indian microfinance data (Banerjee et al., 2013), and show it delivers significant welfare gains.
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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 | Abhijit Banerjee and Arun G. Chandrasekhar and Esther Duflo and Matt… (2013) The Diffusion of Microfinance | 1.000 | 13 | 4 | 100% |
| 2 | Wainwright, Martin J and Jordan, Michael I and others (2008) Graphical models, exponential families, and variational inference | 1.000 | 8 | 3 | 100% |
| 3 | Snijders, Tom AB and others (2002) Markov chain Monte Carlo estimation of exponential random graph models | 0.928 | 4 | 3 | 100% |
| 4 | Mele, Angelo (2017) A structural model of dense network formation | 0.874 | 8 | 2 | 100% |
| 5 | Monderer, Dov and Shapley, Lloyd S (1996) Potential games | 0.874 | 5 | 2 | 100% |
| 6 | Chatterjee, Sourav and Dembo, Amir (2016) Nonlinear large deviations | 0.843 | 3 | 3 | 100% |
| 7 | Nakajima, Ryo (2007) Measuring peer effects on youth smoking behaviour | 0.811 | 4 | 2 | 100% |
| 8 | Ballester, Coralio and Calvó-Armengol, Antoni and Zenou, Yves (2006) Who's who in networks. Wanted: The key player | 0.737 | 3 | 2 | 100% |
| 9 | Akbarpour, Mohammad and Malladi, Suraj and Saberi, Amin (2025) Just a Few Seeds More: The Value of Network Data for Diffusion | 0.644 | 2 | 2 | 100% |
| 10 | Badev, Anton (2021) Nash equilibria on (un) stable networks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 77 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Robust Network Targeting with Multiple Nash Equilibria | 0.874 | 5 | 2 |
| 2 | Statistical Inference of Optimal Allocations 1: Regularities and their Implications | 0.405 | 1 | 1 |