Combining human and machine intelligence for making predictions

An extensive literature in psychology, economics, statistics, operations research and management science has dealt with comparing forecasts based on human-expert judgment vs. (statistical) models in a variety of scenarios, mostly finding advantage of the latter, yet acknowledging the necessity of the former. Although computers can use vast amounts of data to make predictions that are often more accurate than those by human experts, humans are still more adept at processing unstructured information and at recognizing unusual circumstances and their consequences. Can we combine predictions from humans and machines to get predictions that are better than either could do alone? We used prediction markets to combine predictions from groups of people and artificial intelligence agents. We found that the combined predictions were both more accurate and more robust in comparison to those made by groups of only people, or only machines. This combined approach may be especially useful in situations where patterns are difficult to discern, where data are difficult to codify, or where sudden changes occur unexpectedly.

Department dc:contributor.department

Sloan School of Management.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2013

Author dc:creator

• Nagar, Yiftach

Advisor dc:contributor.advisor

• Thomas W. Malone.

• Sloan School of Management.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/82272

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/82272