Decision making under uncertainty : a study of heuristic bias in estimating in construction industry

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Decision making under uncertainty : a study of heuristic bias in estimating in construction industry

 

Author: Tse, Yee Fai Sheeta
Title: Decision making under uncertainty : a study of heuristic bias in estimating in construction industry
Degree: M.Sc.
Year: 2012
Subject: Construction industry -- Decision making.
Construction industry -- Management.
Decision making.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Building and Real Estate
Pages: 78 leaves ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2554587
URI: http://theses.lib.polyu.edu.hk/handle/200/6888
Abstract: The future is largely unknown and is hardly predictable. In the management of construction projects, surveyors, architects, engineers or other professionals, with imperfect information about all the circumstances, can only make estimates or forecasts under the condition of risk and uncertainties. The use of statistical calculations is non-realistic due to the harsh assumptions. To reduce the complex tasks of assessing probabilities and predicting values, rules of thumbs, which are also known as heuristics, are adopted to simplify judgmental operations. Although heuristics, in many situations, are effective strategies to guide our rational thinking, in certain cases, they lead to systematic errors and cognitive biases that may distort and ruin our choices. Psychologists have been studying these biases with lay people. A study that aimed at testing these biases experimentally in a specific context with individuals trained in the construction industry was carried out. The results of the study showed that some professionals working in the industry demonstrated certain types of errors and biases. The implications of the results are discussed. It is believed that if construction professionals gain an explicit and thorough understanding with the literature on biases and the details of the main findings, this should give potential for the production of improved information from estimates and forecasts.

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