Confidence in Belief, Weight of Evidence and Uncertainty Reporting

Brian Hill
Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, PMLR 103:235-245, 2019.

Abstract

One way of putting a popular objection to the Bayesian account of belief and decision is that it does not allow a role for something akin to the Keynesian concept of ‘weight of evidence’ in choice. This paper argues that a recently-defended approach, which refines the credal-set representation of beliefs to give pride of place to an agent’s confidence in her beliefs, can do so fruitfully. Motivated by the use of confidence by the IPCC and US Defense Intelligence Agency in their assessments of uncertainty, the paper then considers the consequences of the proposed approach for uncertainty reporting. On the one hand, when connected to decision, the model affords a clear separation of the belief and value factors: an important quality in policy making contexts where these are the responsibility of different actors. On the other hand, the issue of inter-agent confidence calibration is discussed, and a calibration scale is proposed and defended, on the basis of weight-of-evidence version of David Lewis’s Principal Principle.

Cite this Paper

BibTeX
@InProceedings{pmlr-v103-hill19a, title = {Confidence in Belief, Weight of Evidence and Uncertainty Reporting}, author = {Hill, Brian}, booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications}, pages = {235--245}, year = {2019}, editor = {De Bock, Jasper and de Campos, Cassio P. and de Cooman, Gert and Quaeghebeur, Erik and Wheeler, Gregory}, volume = {103}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v103/hill19a/hill19a.pdf}, url = {https://proceedings.mlr.press/v103/hill19a.html}, abstract = {One way of putting a popular objection to the Bayesian account of belief and decision is that it does not allow a role for something akin to the Keynesian concept of ‘weight of evidence’ in choice. This paper argues that a recently-defended approach, which refines the credal-set representation of beliefs to give pride of place to an agent’s confidence in her beliefs, can do so fruitfully. Motivated by the use of confidence by the IPCC and US Defense Intelligence Agency in their assessments of uncertainty, the paper then considers the consequences of the proposed approach for uncertainty reporting. On the one hand, when connected to decision, the model affords a clear separation of the belief and value factors: an important quality in policy making contexts where these are the responsibility of different actors. On the other hand, the issue of inter-agent confidence calibration is discussed, and a calibration scale is proposed and defended, on the basis of weight-of-evidence version of David Lewis’s Principal Principle.} }
Endnote
%0 Conference Paper %T Confidence in Belief, Weight of Evidence and Uncertainty Reporting %A Brian Hill %B Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications %C Proceedings of Machine Learning Research %D 2019 %E Jasper De Bock %E Cassio P. de Campos %E Gert de Cooman %E Erik Quaeghebeur %E Gregory Wheeler %F pmlr-v103-hill19a %I PMLR %P 235--245 %U https://proceedings.mlr.press/v103/hill19a.html %V 103 %X One way of putting a popular objection to the Bayesian account of belief and decision is that it does not allow a role for something akin to the Keynesian concept of ‘weight of evidence’ in choice. This paper argues that a recently-defended approach, which refines the credal-set representation of beliefs to give pride of place to an agent’s confidence in her beliefs, can do so fruitfully. Motivated by the use of confidence by the IPCC and US Defense Intelligence Agency in their assessments of uncertainty, the paper then considers the consequences of the proposed approach for uncertainty reporting. On the one hand, when connected to decision, the model affords a clear separation of the belief and value factors: an important quality in policy making contexts where these are the responsibility of different actors. On the other hand, the issue of inter-agent confidence calibration is discussed, and a calibration scale is proposed and defended, on the basis of weight-of-evidence version of David Lewis’s Principal Principle.
APA
Hill, B.. (2019). Confidence in Belief, Weight of Evidence and Uncertainty Reporting. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, in Proceedings of Machine Learning Research 103:235-245 Available from https://proceedings.mlr.press/v103/hill19a.html.

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