Proving the Incompatibility of Efficiency and Strategyproofness via SMT Solving
April 19, 2016 ยท Declared Dead ยท ๐ International Joint Conference on Artificial Intelligence
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Florian Brandl, Felix Brandt, Manuel Eberl, Christian Geist
arXiv ID
1604.05692
Category
cs.GT: Game Theory
Cross-listed
cs.AI,
cs.LO
Citations
51
Venue
International Joint Conference on Artificial Intelligence
Last Checked
3 months ago
Abstract
Two important requirements when aggregating the preferences of multiple agents are that the outcome should be economically efficient and the aggregation mechanism should not be manipulable. In this paper, we provide a computer-aided proof of a sweeping impossibility using these two conditions for randomized aggregation mechanisms. More precisely, we show that every efficient aggregation mechanism can be manipulated for all expected utility representations of the agents' preferences. This settles an open problem and strengthens a number of existing theorems, including statements that were shown within the special domain of assignment. Our proof is obtained by formulating the claim as a satisfiability problem over predicates from real-valued arithmetic, which is then checked using an SMT (satisfiability modulo theories) solver. In order to verify the correctness of the result, a minimal unsatisfiable set of constraints returned by the SMT solver was translated back into a proof in higher-order logic, which was automatically verified by an interactive theorem prover. To the best of our knowledge, this is the first application of SMT solvers in computational social choice.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Game Theory
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
A Motivational Game-Theoretic Approach for Peer-to-Peer Energy Trading in the Smart Grid
R.I.P.
๐ป
Ghosted
Computing Resource Allocation in Three-Tier IoT Fog Networks: a Joint Optimization Approach Combining Stackelberg Game and Matching
R.I.P.
๐ป
Ghosted
Fast Convergence of Regularized Learning in Games
R.I.P.
๐ป
Ghosted
Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks
R.I.P.
๐ป
Ghosted
Blockchain Mining Games
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted