Research aims to remove bias from decision-making

Wednesday, 31 May 2017

Improved industry decision-making by removing biases is the focus of a University of Adelaide-led research project announced today.

The project will be funded by the Australian Government through the Australian Research Council (ARC)’s Linkage Projects scheme which promotes collaborative projects between universities, industry, government and other partners.

The project involves working with the oil and gas industry but will be broadly applicable to wherever judgements and forecasts are used to make decisions.

The team led by Professor Steve Begg, Head of the University’s Australian School of Petroleum, has been awarded $311,000 for a three-year project to develop techniques to eliminate biases from groups working together to make judgements or forecasts.

Additional cash and in-kind support from partners Woodside Petroleum and Santos brings the total value of the project to $1,181,438.

“Subject matter experts, like all people, fall prey to a range of inherent biases when making judgements or predictions,” says Professor Begg.

“These judgements are then used to inform many important decisions, whether in oil and gas, other industries, government, charities and so on. With biased inputs there is a greater chance of poor decisions, leading to bad outcomes with undesirable consequences which may be economic, environmental or social.

“Dr Matthew Welsh and I have developed new techniques to ameliorate these problems for individual judgements. This new project extends our current work to situations where groups of experts are making the judgements together. Groups are known to be subject to additional biases.

“The ultimate goal is to develop a computerised tool for bringing together the input information to help make better decisions.”

Dr Matthew Welsh, Senior Research Fellow – Decision Making, says the work applies psychology to industry decision-making.

“The work focuses primarily on biases that occur inherently from people’s cognitive processes rather than any specific motivation,” says Dr Welsh.

“The techniques we are developing are based around how best to ask questions of people to avoid these biases and how to combine individual responses to give the best, unbiased group forecast.”

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