Change author order

paper/references.bib

@@ -1,4 +1,17 @@
 
+@inproceedings{airiauPortioningUsingOrdinal2019,
+  title = {Portioning {{Using Ordinal Preferences}}: {{Fairness}} and {{Efficiency}}},
+  shorttitle = {Portioning {{Using Ordinal Preferences}}},
+  booktitle = {Proceedings of the 28th {{International Joint Conference}} on {{Artificial Intelligence}}},
+  author = {Airiau, St{\'e}phane and Aziz, Haris and Caragiannis, Ioannis and Kruger, Justin and Lang, J{\'e}r{\^o}me and Peters, Dominik},
+  year = {2019},
+  month = jul,
+  pages = {11--17},
+  publisher = {{International Joint Conference on Artificial Intelligence Organization}},
+  doi = {10.24963/ijcai.2019/2},
+  abstract = {A public divisible resource is to be divided among projects. We study rules that decide on a distribution of the budget when voters have ordinal preference rankings over projects. Examples of such portioning problems are participatory budgeting, time shares, and parliament elections. We introduce a family of rules for portioning, inspired by positional scoring rules. Rules in this family are given by a scoring vector (such as plurality or Borda) associating a positive value with each rank in a vote, and an aggregation function such as leximin or the Nash product. Our family contains well-studied rules, but most are new. We discuss computational and normative properties of our rules. We focus on fairness, and introduce the SD-core, a group fairness notion. Our Nash rules are in the SD-core, and the leximin rules satisfy individual fairness properties. Both are Pareto-efficient.}
+}
+
 @inproceedings{azizFairMixingCase2019,
   title = {Fair {{Mixing}}: The {{Case}} of {{Dichotomous Preferences}}},
   shorttitle = {Fair {{Mixing}}},
@@ -163,7 +176,7 @@
   month = jul,
   volume = {7},
   doi = {10.1145/3340230},
-  abstract = {We address the question of aggregating the preferences of voters in the context of participatory budgeting. We scrutinize the voting method currently used in practice, underline its drawbacks, and introduce a novel scheme tailored to this setting, which we call ``Knapsack Voting.'' We study its strategic properties\textemdash{}we show that it is strategy-proof under a natural model of utility (a dis-utility given by the {$\mathscr{l}$}1 distance between the outcome and the true preference of the voter) and ``partially'' strategy-proof under general additive utilities. We extend Knapsack Voting to more general settings with revenues, deficits, or surpluses and prove a similar strategy-proofness result. To further demonstrate the applicability of our scheme, we discuss its implementation on the digital voting platform that we have deployed in partnership with the local government bodies in many cities across the nation. From voting data thus collected, we present empirical evidence that Knapsack Voting works well in practice.},
+  abstract = {We address the question of aggregating the preferences of voters in the context of participatory budgeting. We scrutinize the voting method currently used in practice, underline its drawbacks, and introduce a novel scheme tailored to this setting, which we call ``Knapsack Voting.'' We study its strategic properties\textemdash we show that it is strategy-proof under a natural model of utility (a dis-utility given by the {$\mathscr{l}$}1 distance between the outcome and the true preference of the voter) and ``partially'' strategy-proof under general additive utilities. We extend Knapsack Voting to more general settings with revenues, deficits, or surpluses and prove a similar strategy-proofness result. To further demonstrate the applicability of our scheme, we discuss its implementation on the digital voting platform that we have deployed in partnership with the local government bodies in many cities across the nation. From voting data thus collected, we present empirical evidence that Knapsack Voting works well in practice.},
   journal = {ACM Transactions on Economics and Computation},
   number = {2}
 }
@@ -181,19 +194,6 @@
   number = {1}
 }
 
-@inproceedings{langPortioningUsingOrdinal2019,
-  title = {Portioning {{Using Ordinal Preferences}}: {{Fairness}} and {{Efficiency}}},
-  shorttitle = {Portioning {{Using Ordinal Preferences}}},
-  booktitle = {Proceedings of the 28th {{International Joint Conference}} on {{Artificial Intelligence}}},
-  author = {Lang, J{\'e}r{\^o}me and Airiau, St{\'e}phane and Aziz, Haris and Kruger, Justin and Caragiannis, Ioannis and Peters, Dominik},
-  year = {2019},
-  month = jul,
-  pages = {11--17},
-  publisher = {{International Joint Conference on Artificial Intelligence Organization}},
-  doi = {10.24963/ijcai.2019/2},
-  abstract = {A public divisible resource is to be divided among projects. We study rules that decide on a distribution of the budget when voters have ordinal preference rankings over projects. Examples of such portioning problems are participatory budgeting, time shares, and parliament elections. We introduce a family of rules for portioning, inspired by positional scoring rules. Rules in this family are given by a scoring vector (such as plurality or Borda) associating a positive value with each rank in a vote, and an aggregation function such as leximin or the Nash product. Our family contains well-studied rules, but most are new. We discuss computational and normative properties of our rules. We focus on fairness, and introduce the SD-core, a group fairness notion. Our Nash rules are in the SD-core, and the leximin rules satisfy individual fairness properties. Both are Pareto-efficient.}
-}
-
 @misc{participatorybudgetingprojectHowPBWorks,
   title = {How {{PB Works}} \textendash{} {{Participatory Budgeting Project}}},
   author = {Participatory Budgeting Project},

paper/termpaper.tex

@@ -162,7 +162,7 @@ A third possibility for preference elicitation is \emph{ranked orders}. In this
 scenario, voters specify a ranking over the available choices (projects) with
 the highest ranked choice receiving the biggest amount of the budget and the
 lowest ranked one the lowest amount of the budget.
-\textcite{langPortioningUsingOrdinal2019} study a scenario in which the input
+\textcite{airiauPortioningUsingOrdinal2019} study a scenario in which the input
 method is ranked orders and the projects that can be chosen are divisible. The
 problem of allocating the budget to a set of winning projects under these
 circumstances is referred to as \emph{portioning}. Depending on the desired