Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/311906 
Year of Publication: 
2022
Citation: 
[Journal:] Theory and Decision [ISSN:] 1573-7187 [Volume:] 93 [Issue:] 4 [Publisher:] Springer US [Place:] New York, NY [Year:] 2022 [Pages:] 663-690
Publisher: 
Springer US, New York, NY
Abstract: 
We introduce a new class of real-valued monotones in preordered spaces, injective monotones. We show that the class of preorders for which they exist lies in between the class of preorders with strict monotones and preorders with countable multi-utilities, improving upon the known classification of preordered spaces through real-valued monotones. We extend several well-known results for strict monotones (Richter–Peleg functions) to injective monotones, we provide a construction of injective monotones from countable multi-utilities, and relate injective monotones to classic results concerning Debreu denseness and order separability. Along the way, we connect our results to Shannon entropy and the uncertainty preorder, obtaining new insights into how they are related. In particular, we show how injective monotones can be used to generalize some appealing properties of Jaynes' maximum entropy principle, which is considered a basis for statistical inference and serves as a justification for many regularization techniques that appear throughout machine learning and decision theory.
Subjects: 
Multi-utility representation
Richter–Peleg function
Majorization
Uncertainty preorder
Maximum entropy
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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