Merge pull request #22 from StanfordASL/djalota/some_changes

added aamas reference

_bibliography/ASL_Bib.bib

@@ -3339,7 +3339,7 @@ @inproceedings{JansonHuEtAl2018
 @inproceedings{JalotaTsaoEtAl2023,
   author    = {Jalota, D. and Tsao, M. and Pavone, M. and Ye, Y.},
   title     = {Catch Me If You Can: Combatting Fraud in Artificial Currency Based Government Benefits Programs},
-  booktitle = proc_WINE,
+  booktitle = proc_AAMAS,
   year      = {2023},
   abstract  = {Artificial currencies have grown in popularity in many real-world resource allocation settings. In particular, they have gained traction in government benefits programs, e.g., food assistance or transit benefits programs, that provide support to eligible users in the population, e.g., through subsidized food or public transit. However, such programs are prone to two common fraud mechanisms: (i) \emph{misreporting fraud}, wherein users can misreport their private attributes to gain access to more artificial currency (credits) than they are entitled to, and (ii) \emph{black market fraud}, wherein users may seek to sell some of their credits in exchange for \emph{real} money. In this work, we develop mechanisms to address these two sources of fraud in artificial currency based government benefits programs. To address misreporting fraud, we propose an audit mechanism that induces a two-stage game between an administrator and users, wherein the administrator running the benefits program can audit users at some cost and levy fines against them for misreporting their information. For this audit game, we first investigate the conditions on the administrator’s budget to establish the existence of equilibria and present a linear programming approach to compute these equilibria under both the signaling game and Bayesian persuasion formulations. We then show that the decrease in misreporting fraud corresponding to our audit mechanism far outweighs the spending of the administrator to run it by establishing that its total costs are lower than that of the status quo with no audits. To highlight the practical viability of our audit mechanism in mitigating misreporting fraud, we present a case study on Washington D.C.'s federal transit benefits program where the proposed audit mechanism even demonstrates several orders of magnitude improvement in total cost compared to a no-audit strategy for some parameter ranges.},
   address   = {Shanghai, China},