A testbed to enable comparisons between competing approaches for computational social choice
| Autor: | John A. Doucette, Robin Cohen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Management science Computer science media_common.quotation_subject Testbed Rank (computer programming) General Engineering Context (language use) Machine learning computer.software_genre Preference Field (computer science) Group decision-making Analytics Voting Artificial intelligence business computer media_common |
| Zdroj: | Big Data and Information Analytics. 1:309-340 |
| ISSN: | 2380-6966 |
| DOI: | 10.3934/bdia.2016013 |
| Popis: | Within artificial intelligence, the field of computational social choice studies the application of AI techniques to the problem of group decision making, especially through systems where each agent submits a vote taking the form of a total ordering over the alternatives (a preference). Reaching a reasonable decision becomes more difficult when some agents are unwilling or unable to rank all the alternatives, and appropriate voting systems must be devised to handle the resulting incomplete preference information. In this paper, we present a detailed testbed which can be used to perform information analytics in this domain. We illustrate the testbed in action for the context of determining a winner or putting candidates into ranked order, using data from realworld elections, and demonstrate how to use the results of the testbed to produce effective comparisons between competing algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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