Algorithmic Contract Design for Crowdsourced Ranking: Warm-up: Agents With Known Equal Disutility
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https://hackernoon.com/algorithmic-contract-design-for-crowdsourced-ranking-warm-up-agents-with-known-equal-disutility
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https://hackernoon.com/algorithmic-contract-design-for-crowdsourced-ranking-warm-up-agents-with-known-equal-disutility
Algorithmic Contract Design for Crowdsourced Ranking: A Deeper Look at Our Experiments
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https://hackernoon.com/algorithmic-contract-design-for-crowdsourced-ranking-a-deeper-look-at-our-experiments
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https://hackernoon.com/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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https://hackernoon.com/additional-information-about-the-algorithmic-contract-design-experiments-what-you-should-know
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https://hackernoon.com/additional-information-about-the-algorithmic-contract-design-experiments-what-you-should-know
Hackernoon
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.