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Second-order Inductive Inference: an axiomatic approach

Patrick H. O'Callaghan

arXiv 5 Apr 2019 · Theoretical Economics

arXiv:1904.02934 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Consider a predictor who ranks eventualities on the basis of past cases: for instance a search engine ranking webpages given past searches. Resampling past cases leads to different rankings and the extraction of deeper information. Yet a rich database, with sufficiently diverse rankings, is often beyond reach. Inexperience demands either "on the fly" learning-by-doing or prudence: the arrival of a novel case does not force (i) a revision of current rankings, (ii) dogmatism towards new rankings, or (iii) intransitivity. For this higher-order framework of inductive inference, we derive a suitably unique numerical representation of these rankings via a matrix on eventualities x cases and describe a robust test of prudence. Applications include: the success/failure of startups; the veracity of fake news; and novel conditions for the existence of a yield curve that is robustly arbitrage-free.

Citation extraction

29
references
35
in-text mentions
29
distinct cited
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9,718
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appendix boundary found by appendix_command · 29% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Argenziano, R. and Gilboa, I (2019) Second-order induction in prediction problems0.73732100%
2Enkavi, A. Z., Weber, B., Zweyer, I., Wagner, J., Elger, C. E., Webe… (2017) Evidence for hippocampal dependence of value-based decisions0.64441100%
3Bradbury, H. and Ross, K (1990) The effects of novelty and choice materials on the intransitivity of preferences of children and adults0.40511100%
4Cowles, A (1933) Can stock market forecasters forecast?0.40511100%
5Dobkin, D. P. and Reiss, S. P (1980) The complexity of linear programming0.40511100%
6Fama, E. F. and French, K. R (2010) Luck versus skill in the cross-section of mutual fund returns0.40511100%
7Fama, E. F. and MacBeth, J. D (1973) Risk, return, and equilibrium: Empirical tests0.40511100%
8Grant, S., Kline, J., Meneghel, I., Quiggin, J., and Tourky, R (2015) A theory for robust experiments for choice under uncertainty0.40511100%
9Grant, S. and Quiggin, J (2015) A preference model for choice subject to surprise0.40511100%
10Harvey, C. R. and Liu, Y (2015) Lucky factors0.40511100%

Showing the top 10 of 29 scored citations.