Higher-order decision theory

This paper investigates a surprising relationship between decision theory and proof theory. Using constructions originating in proof theory based on higher-order functions, so called quantifiers and selection functions, we show that these functionals model choice behavior of individual agents. Our f...

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Detalhes bibliográficos
Principais autores: Hedges, J, Oliva, P, Shprits, E, Winschel, V, Zahn, P
Formato: Conference item
Publicado em: Springer Verlag 2017
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author Hedges, J
Oliva, P
Shprits, E
Winschel, V
Zahn, P
author_facet Hedges, J
Oliva, P
Shprits, E
Winschel, V
Zahn, P
author_sort Hedges, J
collection OXFORD
description This paper investigates a surprising relationship between decision theory and proof theory. Using constructions originating in proof theory based on higher-order functions, so called quantifiers and selection functions, we show that these functionals model choice behavior of individual agents. Our framework is expressive, it captures classical theories such as utility functions and preference relations but it can also be used to faithfully model abstract goals such as coordination. It is directly implementable in functional programming languages. Lastly, modeling an agent with selection functions and quantifiers is modular and thereby allows to seamlessly combine agents bridging decision theory and game theory.
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spelling oxford-uuid:8aab8b09-2dcd-4978-9355-665d44a271522022-03-26T22:33:05ZHigher-order decision theoryConference itemhttp://purl.org/coar/resource_type/c_5794uuid:8aab8b09-2dcd-4978-9355-665d44a27152Symplectic Elements at OxfordSpringer Verlag2017Hedges, JOliva, PShprits, EWinschel, VZahn, PThis paper investigates a surprising relationship between decision theory and proof theory. Using constructions originating in proof theory based on higher-order functions, so called quantifiers and selection functions, we show that these functionals model choice behavior of individual agents. Our framework is expressive, it captures classical theories such as utility functions and preference relations but it can also be used to faithfully model abstract goals such as coordination. It is directly implementable in functional programming languages. Lastly, modeling an agent with selection functions and quantifiers is modular and thereby allows to seamlessly combine agents bridging decision theory and game theory.
spellingShingle Hedges, J
Oliva, P
Shprits, E
Winschel, V
Zahn, P
Higher-order decision theory
title Higher-order decision theory
title_full Higher-order decision theory
title_fullStr Higher-order decision theory
title_full_unstemmed Higher-order decision theory
title_short Higher-order decision theory
title_sort higher order decision theory
work_keys_str_mv AT hedgesj higherorderdecisiontheory
AT olivap higherorderdecisiontheory
AT shpritse higherorderdecisiontheory
AT winschelv higherorderdecisiontheory
AT zahnp higherorderdecisiontheory