Abstract
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.
Highlights
There is a broad spectrum of decision-making tasks, e.g. engineering, economics, sociology, ecology, informatics etc., see e.g. [1,2,3,4,5,6,7]
These realistic decision-making tasks are often difficult to solve by limited available input information items (III)
The only solution is to increase data / knowledge inputs into decision making processes. It means that no available information item may be ignored
Summary
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. 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. 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.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
