Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F15%3APU114649" target="_blank" >RIV/00216305:26510/15:PU114649 - isvavai.cz</a>

Result on the web

<a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0131590" target="_blank" >http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0131590</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pone.0131590" target="_blank" >10.1371/journal.pone.0131590</a>

Result language

angličtina

Original language name

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

Original language description

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2015

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

PLOS ONE

ISSN

1932-6203

e-ISSN

—

Volume of the periodical

7

Issue of the periodical within the volume

10

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

1-18

UT code for WoS article

000358161200032

EID of the result in the Scopus database

2-s2.0-84941309553