Abstract
The standard approach to solve prediction tasks is to apply inductive methods such as, e.g., the straight rule. Such methods are proven to be access-optimal in specific prediction settings, but not in all. Within the optimality-approach of meta-induction, success-based weighted prediction methods are proven to be access-optimal in all possible continuous prediction settings. However, meta-induction fails to be access-optimal in so-called demonic discrete prediction environments where the predicted value is inversely correlated with the true outcome. In this paper the problem of discrete prediction environments is addressed by embedding them into a synchronised prediction setting. In such a setting the task consists in providing a discrete and a continuous prediction. It is shown that synchronisation constraints exclude the possibility of demonic environments.
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Acknowledgements
This research was funded by the Austrian Academy of Sciences (OeAW, DOC grant) and the Deutsche Forschungsgemeinschaft (DFG priority program SPP 1516 “New Frameworks of Rationality”). I would like to thank two anonymous referees for very helpful comments. For valuable discussions on this topic I would like to thank Alexander Christian, Alexander Gebharter, and Gerhard Schurz.
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Feldbacher-Escamilla, C.J. Optimisation in a Synchronised Prediction Setting. J Gen Philos Sci 48, 419–437 (2017). https://doi.org/10.1007/s10838-017-9379-7
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DOI: https://doi.org/10.1007/s10838-017-9379-7