Collective Information
| Authors | |
|---|---|
| Publication date | 2020 |
| Book title | AAAI-20, IAAI-20, EAAI-20 proceedings |
| Book subtitle | Thirty-Fourth AAAI Conference on Artificial Intelligence, Thirty-Second Conference on Innovative Applications of Artificial Intelligence, The Tenth Symposium on Educational Advances in Artificial Intelligence : February 7–12th, 2020, New York Hilton Midtown, New York, New York, USA |
| ISBN |
|
| Series | Proceedings of the AAAI Conference on Artificial Intelligence |
| Event | 34th AAAI Conference on Artificial Intelligence, AAAI 2020 |
| Volume | Issue number | 9 |
| Pages (from-to) | 13520-13524 |
| Publisher | Palo Alto, California: AAAI Press |
| Organisations |
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| Abstract |
Many challenging problems of scientific, technological, and societal
significance require us to aggregate information supplied by multiple
agents into a single piece of information of the same type—the collective information
representing the stance of the group as a whole. Examples include
expressive forms of voting and democratic decision making (where
citizens supply information regarding their preferences), peer
evaluation (where participants supply information in the form of
assessments of their peers), and crowdsourcing (where volunteers supply
information by annotating data). In this position paper, I outline the
challenge of modelling, handling, and analysing all of these diverse
instances of collective information using a common methodology.
Addressing this challenge will facilitate a transfer of knowledge
between different application domains, thereby enabling progress in all
of them.
|
| Document type | Conference contribution |
| Language | English |
| Published at | https://doi.org/10.1609/aaai.v34i09.7074 |
| Downloads |
7074-Article Text-10303-1-10-20200526
(Final published version)
|
| Permalink to this page | |
