Measurements of generalisation based on information geometry
MANNA '95 Proceedings of the first international conference on Mathematics of neural networks : models, algorithms and applications: models, algorithms and applications
Combining human and machine intelligence in large-scale crowdsourcing
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Crowd IQ: aggregating opinions to boost performance
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Task routing for prediction tasks
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Incentives for truthful reporting in crowdsourcing
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Exploiting user model diversity in forecast aggregation
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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In many applications, agents (whether human or computational) provide estimates that must be combined at a higher level. Recent research distinguishes two kinds of such estimates: interpreted and generated data. These two kinds of data require different kinds of aggregation processes, which behave differently from an information geometric perspective: interpreted estimates require methods such as voting that can leave the convex hull of the individual estimates, while the optimal aggregation for generated estimates lies within the convex hull and thus is accessible by methods such as weighted averages. We motivate our analysis in the context of a crowdsourced forecasting application, demonstrate the central insights theoretically, and show how these insights manifest them-selves in actual data.