Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Ineffectual Regulation When An Official of A Regulatory Agency Seems to Regulate Effectually Without Actually Doing So

Version 1 : Received: 9 September 2020 / Approved: 10 September 2020 / Online: 10 September 2020 (05:56:54 CEST)

How to cite: Oldberg, T. Ineffectual Regulation When An Official of A Regulatory Agency Seems to Regulate Effectually Without Actually Doing So. Preprints 2020, 2020090223 (doi: 10.20944/preprints202009.0223.v1). Oldberg, T. Ineffectual Regulation When An Official of A Regulatory Agency Seems to Regulate Effectually Without Actually Doing So. Preprints 2020, 2020090223 (doi: 10.20944/preprints202009.0223.v1).

Abstract

Occasionally, officials of the world’s regulatory agencies embark upon attempts at bringing previously unregulated physical systems under regulation by them. Each such attempt raises an epistemological issue. At issue is whether enough information about the outcomes of the events of the future, given the outcomes of the events of the present, will be in the hands of a would-be regulator for this regulator to regulate effectually. If present, this information is provided by runs of a model of the physical system that is slated for regulation. Ideally, this model makes an argument that draws its conclusion from the evidence presented to it. If so, this argument is of the form of a predictive inference. However, the process by which an argument draws its conclusion from the evidence may go awry. This happens, for example, when the axiom of probability theory called unit measure is falsified by a conclusion that is drawn from the evidence by this argument.A method is derived from first principles for determination of whether unit measure is satisfied or falsified by an argument made by a model, given that this argument may attach unusual meanings to statistical terms. This method is used in a study of whether unit measure is satisfied or falsified by the arguments that are made by a pair of models. Both models are in active use by regulatory agencies around the world. Under neither argument do runs of the model provide an official of a regulatory agency with the information gain aka mutual information that he or she would need to regulate effectually. Under both arguments, attachment of unusual meanings to statistical terms creates the illusion that such an official can and does regulate effectually.

Subject Areas

logic semantics; statistical terms; regulation; model; nil information gain

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.