BUSINESS CHALLENGE
"MANAGERS CAN IMPROVE THEIR DECISION MAKING
The point Scientific studies repeatedly show that decisions made by computers are often just as good or better than the decisions made by human experts. Early studies primarily focused on clinical applications (showing for instance the computer's ability to diagnose certain diseases), but later studies found similar results when it comes to financial and business matters, including managerial decisions. In the scientific literature the comparison of man-made vs. machine-made decisions is called "clinical vs statistical prediction". In my own research I have repeatedly tested how managerial decisions made by computer models compare to the decisions made by managers with various degrees of expertise. To cut a longer story short: the computer model easily wins.
Can computers improve decisions in my organization? More often than you might think. There are a couple of rules thumb on when computers are likely to do at least as good as humans in a business context: + It must be a decision that can be quantified. That's obvious. You cannot tell a computer to design your marketing strategy and present it to the board, at least not yet. Instead you want a model to look at questions such as: how many people should we put on this sales team? How much should we invest in trying to make our contract water-tight? Should I go to my supervisor to have him check this? Which supplier should we choose? + It must be a decision about which you have at least some quantified data available (you should at least be able to collect such data in a reasonable amount of time). Also pretty obvious: if there are no data available to predict from, no computer model can be made. + It must be a decision that occurs relatively frequently. The more data you have (or can collect in a reasonable amount of time), the better the model will be able to predict. Moreover, the more often this kind of decision occurs, the better the computer can show its superior performance. Note: in my own research a model with only a handful of predictor variables easily outperforms experts who have more than twice as much data and a lifetime of experience available. So yes, there are requirements on having data, but no massive and complicated data mining is necessary.
A business challenge
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Frequently asked questions If these models are so good, why isn't everybody using them? For several reasons. First of all, in many cases people just do not like the idea that computers take over. Compare it to going on an airplane where you know that there is nobody in the cockpit, just a big button saying "Press start to fly ". Even if computers would be able to do this perfectly, many will still object to the idea. A second reason is that those who are now having a job that includes some or even many of these kinds of decisions, are not exactly embracing the idea that a machine can do what they can do. The final reason is one of implementation. If a human makes a mistake, we rightfully see this as normal and part of the risk of the job. If a model makes a mistake, we see it as proof of the fact that the model doesn't work. Usually, you need someone at the top of the hierarchy in an organization who is willing to go off the beaten track and resist the initial hesitation of those whose jobs are about to change. If I let these computers make my decisions, wouldn't that make me superfluous? For one thing, it would make you a better manager. Managers often consider their job to be one that involves many strategic decisions. As one manager expressed it "I am playing chess all day long, so to speak". My reply was that in that case it would be wise to bring a chess computer to work, and I wasn't joking. Everyone knows that chess computers can beat top-grandmasters nowadays. That computer models can beat expert managers in decision making is less well known. Bring your chess computer to work before you find out that others have been using theirs already! Where is the science? Try Tazelaar and Snijders (2004) and the references therein.
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