Implementable Prescriptive Decision Making

Miroslav Kárný
Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers, PMLR 58:19-30, 2017.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v58-karny17a, title = {Implementable Prescriptive Decision Making}, author = {Kárný, Miroslav}, booktitle = {Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers}, pages = {19--30}, year = {2017}, editor = {Guy, Tatiana V. and Kárný, Miroslav and Rios-Insua, David and Wolpert, David H.}, volume = {58}, series = {Proceedings of Machine Learning Research}, month = {09 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v58/karny17a/karny17a.pdf}, url = {https://proceedings.mlr.press/v58/karny17a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Implementable Prescriptive Decision Making %A Miroslav Kárný %B Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers %C Proceedings of Machine Learning Research %D 2017 %E Tatiana V. Guy %E Miroslav Kárný %E David Rios-Insua %E David H. Wolpert %F pmlr-v58-karny17a %I PMLR %P 19--30 %U https://proceedings.mlr.press/v58/karny17a.html %V 58 %X 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.
APA
Kárný, M.. (2017). Implementable Prescriptive Decision Making. Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers, in Proceedings of Machine Learning Research 58:19-30 Available from https://proceedings.mlr.press/v58/karny17a.html.

Related Material