bibtype C - Conference Paper (international conference)
ARLID 0479462
utime 20240103214701.5
mtime 20171012235959.9
title (primary) (eng) Implementable Prescriptive Decision Making
specification
page_count 12 s.
media_type E
serial
ARLID cav_un_epca*0479516
ISSN Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers
title Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers
page_num 19-30
publisher
place Cambridge
name JMLR
year 2017
editor
name1 Guy
name2 Tatiana Valentine
editor
name1 Kárný
name2 Miroslav
editor
name1 Rios-Insua
name2 D.
editor
name1 Wolpert
name2 D. H.
keyword fully probabilistic design
keyword distributed decision making
keyword cooperation
author (primary)
ARLID cav_un_auth*0101124
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
full_dept Department of Adaptive Systems
share 100%
name1 Kárný
name2 Miroslav
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2017/AS/karny-0479462.pdf
cas_special
project
ARLID cav_un_auth*0331019
project_id GA16-09848S
agency GA ČR
abstract (eng) The need for inspecting (ir)rationality in decision making (DM) - the observed discrepancy between real and prescriptive DMs - stems from omnipresence of DM in individuals’ and society life. Active approaches try to diminish this discrepancy either by changing behaviour of participants (DM subjects) or modifying prescriptive theories as done in this text. It provides a core of unified merging methodology of probabilities serving for knowledge fusion and information sharing exploited in cooperative DM. Specifically, it unifies merging methodologies supporting a flat cooperation of interacting self-interested DM participants. They act without a facilitator and they are unwilling to spare a non-negligible deliberation effort on merging. They are supposed to solve their DM tasks via the fully probabilistic design (FPD) of decision strategies. This option is motivated by the fact that FPD is axiomatically justified and extends standard Bayesian DM. Merging is a supporting DM task and is also solved via FPD. The proposed merging formulation tries to be as general as possible without entering into technicalities of measure theory. The results generalise and unify earlier work and open a pathway to systematic solutions of specific, less general, problems.
action
ARLID cav_un_auth*0351361
name NIPS 2016 Workshop on Imperfect Decision Makers
dates 20161209
mrcbC20-s 20161209
place Barcelona
country ES
RIV BC
FORD0 10000
FORD1 10100
FORD2 10103
reportyear 2018
num_of_auth 1
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0275506
confidential S
arlyear 2017
mrcbU14 SCOPUS
mrcbU24 PUBMED
mrcbU34 WOS
mrcbU63 cav_un_epca*0479516 Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers 1938-7228 19 30 Cambridge JMLR 2017 Proceedings of Machine Learning Research volume 58
mrcbU67 Guy Tatiana Valentine 340
mrcbU67 340 Kárný Miroslav
mrcbU67 340 Rios-Insua D.
mrcbU67 340 Wolpert D. H.