Policy Learning with Competing Agents

References

• Acemoglu D, Jensen MK (2010) Robust comparative statics in large static games. 49th IEEE Conf. Decision Control (IEEE, Piscataway, NJ), 3133–3139.Google Scholar
• Acemoglu D, Jensen MK (2015) Robust comparative statics in large dynamic economies. J. Political Econom. 123(3):587–640.Crossref, Google Scholar
• Ahmadi S, Beyhaghi H, Blum A, Naggita K (2022) On classification of strategic agents who can both game and improve. Preprint, submitted February 28, https://arxiv.org/abs/2203.00124.Google Scholar
• Athey S, Wager S (2021) Policy learning with observational data. Econometrica 89(1):133–161.Crossref, Google Scholar
• Bhattacharya D, Dupas P (2012) Inferring welfare maximizing treatment assignment under budget constraints. J. Econometrics 167(1):168–196.Crossref, Google Scholar
• Björkegren D, Blumenstock JE, Knight S (2020) Manipulation-proof machine learning. Preprint, submitted April 8, https://arxiv.org/abs/2004.03865.Google Scholar
• Blake T, Coey D (2014) Why marketplace experimentation is harder than it seems: The role of test-control interference. Proc. 15th ACM Conf. Econom. Comput. (ACM, New York), 567–582.Google Scholar
• Bound J, Hershbein B, Long BT (2009) Playing the admissions game: Student reactions to increasing college competition. J. Econom. Perspect. 23(4):119–146.Crossref, Google Scholar
• Brückner M, Kanzow C, Scheffer T (2012) Static prediction games for adversarial learning problems. J. Machine Learn. Res. 13(1):2617–2654.Google Scholar
• Chen Y, Liu Y, Podimata C (2020) Learning strategy-aware linear classifiers. Adv. Neural Inform. Processing Systems 33:15265–15276.Google Scholar
• Corchón LC (1994) Comparative statics for aggregative games the strong concavity case. Math. Soc. Sci. 28(3):151–165.Crossref, Google Scholar
• Cournot A (1982) Researches into the Mathematical Principles of the Theory of Wealth (Routledge, New York).Google Scholar
• Dalvi N, Domingos P, Sumit M, Verma SD (2004) Adversarial classification. Proc. 10th Internat. Conf. Knowledge Discovery Data Mining (ACM Press, New York), 99–108.Google Scholar
• Dong J, Roth A, Schutzman Z, Waggoner B, Wu ZS (2018) Strategic classification from revealed preferences. Proc. 2018 ACM Conf. Econom. Comput. (ACM, New York), 55–70.Google Scholar
• Frankel A, Kartik N (2019a) Improving information from manipulable data. J. Eur. Econom. Assoc. 20(1):79–115.Crossref, Google Scholar
• Frankel A, Kartik N (2019b) Muddled information. J. Political Econom. 127(4):1739–1776.Crossref, Google Scholar
• Hardt M, Megiddo N, Papadimitriou C, Wootters M (2016) Strategic classification. Proc. 2016 ACM Conf. Innovations Theoret. Comput. Sci. (ACM, New York), 111–122.Google Scholar
• Heckman JJ, Lochner L, Taber C (1998) General-equilibrium treatment effects: A study of tuition policy. Amer. Econom. Rev. 88(2):381–386.Google Scholar
• Hu Y, Li S, Wager S (2022) Average direct and indirect causal effects under interference. Biometrika 109(4):1165–1172.Crossref, Google Scholar
• Ingels SJ (1994) National Education Longitudinal Study of 1988: Second Follow-up: Student Component Data File User’s Manual (US Department of Education, Office of Educational Research and Improvement, Washington, DC).Google Scholar
• Jagadeesan M, Mendler-Dünner C, Hardt M (2021) Alternative microfoundations for strategic classification. Internat. Conf. Machine Learn. (PMLR, New York), 4687–4697.Google Scholar
• Johari R, Li H, Liskovich I, Weintraub GY (2022) Experimental design in two-sided platforms: An analysis of bias. Management Sci. 68(10):7069–7089.Link, Google Scholar
• Kallus N, Zhou A (2021) Minimax-optimal policy learning under unobserved confounding. Management Sci. 67(5):2870–2890.Link, Google Scholar
• Kandori M, Mailath GJ, Rob R (1993) Learning, mutation, and long run equilibria in games. Econometrica 61(1):29–56.Crossref, Google Scholar
• Kitagawa T, Tetenov A (2018) Who should be treated? Empirical welfare maximization methods for treatment choice. Econometrica 86(2):591–616.Crossref, Google Scholar
• Kleinberg J, Raghavan M (2020) How do classifiers induce agents to invest effort strategically? ACM Trans. Econom. Comput. 8(4):1–23.Crossref, Google Scholar
• Kleinberg J, Lakkaraju H, Leskovec J, Ludwig J, Mullainathan S (2018) Human decisions and machine predictions. Quart. J. Econom. 133(1):237–293.Crossref, Google Scholar
• Levanon S, Rosenfeld N (2022) Generalized strategic classification and the case of aligned incentives. Internat. Conf. Machine Learn. (PMLR, New York), 12593–12618.Google Scholar
• Li S, Wager S (2022) Random graph asymptotics for treatment effect estimation under network interference. Ann. Statist. 50(4):2334–2358.Crossref, Google Scholar
• Liu LT, Garg N, Borgs C (2022) Strategic ranking. Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 2489–2518.Google Scholar
• Manski CF (2004) Statistical treatment rules for heterogeneous populations. Econometrica 72(4):1221–1246.Crossref, Google Scholar
• Miller JP, Perdomo JC, Zrnic T (2021) Outside the echo chamber: Optimizing the performative risk. Internat. Conf. Machine Learn. (PMLR, New York), 7710–7720.Google Scholar
• Monderer D, Shapley LS (1996) Fictitious play property for games with identical interests. J. Econom. Theory 68(1):258–265.Crossref, Google Scholar
• Munro E (2024) Treatment allocation with strategic agents. Management Sci. 71(1):123–145.Google Scholar
• Munro E, Wager S, Xu K (2021) Treatment effects in market equilibrium. Preprint, submitted September 23, https://arxiv.org/abs/2109.11647.Google Scholar
• Perdomo J, Zrnic T, Mendler-Dünner C, Hardt M (2020) Performative prediction. Internat. Conf. Machine Learn. (PMLR, New York), 7599–7609.Google Scholar
• Rosinger KO, Sarita Ford K, Choi J (2021) The role of selective college admissions criteria in interrupting or reproducing racial and economic inequities. J. Higher Ed. 92(1):31–55.Crossref, Google Scholar
• Santelices MV, Horn C, Catalán X (2019) Institution-level admissions initiatives in Chile: Enhancing equity in higher education? Stud. Higher Ed. 44(4):733–761.Crossref, Google Scholar
• Sävje F, Aronow P, Hudgens M (2021) Average treatment effects in the presence of unknown interference. Ann. Statist. 49(2):673–701.Crossref, Google Scholar
• Thayer K, Ko AJ (2017) Barriers faced by coding bootcamp students. Proc. 2017 ACM Conf. Internat. Comput. Ed. Res. (ACM, New York), 245–253.Google Scholar
• Wager S, Xu K (2021) Experimenting in equilibrium. Management Sci. 67(11):6694–6715.Link, Google Scholar