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Everyone's an Expert, but a Computer Program May Be Able to Pick the Best Ones

The algorithm compared historic data with the expert's predictions.

An algorithm developed by Pennsylvania State University researchers analyzed the estimates of an agribusiness expert and helped an agricultural chemicals company's division improve its forecast accuracy.

Credit: natasaadzic/iStock

Pennsylvania State University (PSU) researchers have developed an algorithm that analyzed the estimates of an agribusiness expert and helped a business division at an agricultural chemicals company improve its forecast accuracy, yielding a 2% to 3% profitability increase and a 6% to 7%t reduction in costs.

The researchers developed the computer model to estimate the risk associated with yield, then used a mathematical model to translate the quantile estimates into mean and standard deviation of yield.

"The mean provides estimates for how many bushels the firm can expect on average, while the standard deviation captures the expected variability in the growth process," says PSU professor Saurabh Bansal.

After comparing the historic data with the expert's predictions, Bansal says the program provided insights into the bias of the expert's mental models. He notes the technology enables company officials to compare and select experts and quantify how effective their training is for experts.

From Penn State News
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Abstracts Copyright © 2017 Information Inc., Bethesda, Maryland, USA


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