Approximate Mechanism Design without Money
By Ariel D. Procaccia, Moshe Tennenholtz. TEAC, 2013
428 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Management Science and Operations Research, Marketing
View Paper: https://www.semanticscholar.org/paper/20282279ccd9f427008776d05b52d3c1f8d2a915
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- David F. Manlove
- Kamesh Munagala
- Brandon Fain
- Wade Hann-Caruthers
- Sumit Goel
- Ariel D. Procaccia
- Vincent Conitzer
- Nisarg Shah
In Management Science and Operations Research
- David F. Manlove
- Kamesh Munagala
- Brandon Fain
- Wade Hann-Caruthers
- Sumit Goel
- Ashish Goel
- Felix Brandt
- Ariel D. Procaccia
In Marketing
Citations in the corpus
- Coordinate-wise Median: Not Bad, Not Bad, Pretty Good (2020, background): “In the class of deterministic strategyproof mechanisms for locating a facility, the median mechanism has been shown to be optimal under various objectives and domains [Procaccia and Tennenholtz [27], Feigenbaum et al.”
- Algorithmics of Matching Under Preferences (2013, methodology): “To mitigate this, an alternative is to design a strategy-proof mechanism that provides an approximate solution, following Procaccia and Tennenholtz [484].”
- The Core of the Participatory Budgeting Problem (2016, background): “The problem of truthful allocation of public goods without payments is considered in the context of the facility location problem in [27]; however, the setting is unrelated to ours and the authors are concerned with the social welfare or the total dis-utility, not the core.”
- Handbook of Computational Social Choice (2016, background): “, auction theory), and topics such as approximate mechanism design without money (Procaccia and Tennenholtz, 2013) or incentive compatible machine learning (Dekel et al.”