Algorithmic Contract Design for Crowdsourced Ranking: Warm-up: Agents With Known Equal Disutility
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Algorithmic Contract Design for Crowdsourced Ranking: What Was Left Out of Section 3
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Algorithmic Contract Design for Crowdsourced Ranking: What Was Left Out of Section 3 | HackerNoon
Find out what was taken out of Section 3 of our Algorithmic Contract Design.
Algorithmic Contract Design for Crowdsourced Ranking: Omitted Proofs From Section 2
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Algorithmic Contract Design for Crowdsourced Ranking: Omitted Proofs From Section 2 | HackerNoon
Find out what was missing from section 2 of our Algorithmic Contract Design Research paper.
Algorithmic Contract Design for Crowdsourced Ranking: Definitions You Should Know
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Algorithmic Contract Design for Crowdsourced Ranking: Definitions You Should Know | HackerNoon
This paper is available on arxiv under CC 4.0 license. It is available under the terms of the Creative Commons license.
Algorithmic Contract Design for Crowdsourced Ranking: A Deeper Look at Our Experiments
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Algorithmic Contract Design for Crowdsourced Ranking: A Deeper Look at Our Experiments | HackerNoon
In the following section, we present experiments that test our models and algorithm. We evaluate the impact of our algorithm on the principal’s utility
Additional Information About the Algorithmic Contract Design Experiments: What You Should Know
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Additional Information About the Algorithmic Contract Design Experiments: What You Should Know | HackerNoon
Algorithm 4 takes as input “comparisons”, which is a list of s sublists, each containing the returned results of the binary comparisons performed by an agent.