Every organisation needs to forecast. Startups need to forecast their success (or failure). Government agencies need to forecast changes in the industries they regulate. Public-health departments need to forecast disease outbreaks. Disaster-relief organisations need to forecast the effectiveness of their distribution strategies. These are just a few examples.
Good forecasting is crucial to good decision making and strategic planning. Unfortunately, good forecasting is hard to do. Sometimes we have data and statistical models upon which we can make our forecasts—this is how weather forecasters do their forecasting. But often we don’t have data, or we don't have good models for that data. In these cases, we have to rely on human expertise.
The problem with relying on human expertise is that we humans make all sorts of systematic mistakes when we forecast. We're often overconfident in our judgements and when we get together to collaborate we usually end up in groupthink, and certain members of the group can dominate the discussion. The result can be disastrous: high-stakes decisions are made on low-quality forecasts.
It doesn't have to be this way. Organisations are often full of intelligent, thoughtful people who have the ability to collaborate and produce accurate forecasts. They also have diverse perspectives and thinking styles that can be harnessed to improve forecast accuracy. Moreover, detailed knowledge about critical issues is often spread throughout an organisation. What if the people of your organisation could come together to share that knowledge and analyse it collectively, while also benefiting from the wisdom of their diversity?
That's where DelphiCloud can help. We've developed an easy-to-use forecasting platform that is based on the latest scientific research in human judgement and decision making. Using our software, you can send out any question you have to the people in your organisation and see what they have to say.
We use sophisticated techniques to elicit the best opinions from your people. These techniques get the knowledge out of your team, while also quantifying the real uncertainty that surrounds it. We then use advanced algorithms to optimally combine the diverse opinions you get from your people. These algorithms take into account a host of factors, such as the diversity of your group, their expertise, their past performance, and the sophistication of their opinions.
We then use an enhanced version of the Delphi method so that your people can share evidence and refine their opinions. The basic idea behind the Delphi method is that your people can express their opinions privately so that they don't suffer from the problems that often arise in group deliberations, such as groupthink and the anchoring effect.
The result is a scientifically-backed forecast that will allow you to make the most optimal decisions in states of high uncertainty.
First, you identify a question that is important to your organisation and gather your best group of people who may know something about your question. (It's often best to make sure your group has a diverse range of skills and perspectives.)
Then your team uses DelphiCloud to enter their answers to the question. They go through our enhanced elicitation process and the Delphi method to maximise the chance that their answers are as precise and accurate as possible. This will involve a discussion session so that your people can trade evidence and share analyses. We'll also identify outliers and encourage them to share their reasoning — this helps prevent groupthink.
We take all of that information and combine it with profiles of your people to aggregate their different answers into a single collective answer. A lot of factors go into this process. For example, those who have been accurate in the past will tend to influence the collective answer the most. And those who have high-quality and coherent opinions will also tend to have more influence. (There's much more than that, but you get the idea.)
If the true answer to your question is eventually discovered, this is then used to measure performance, update our algorithms, and to teach your people how to improve their forecasting in the future.
The result is a scientifically supported collective forecast and a much deeper understanding of the issues that relate to your question, which will help you make better decisions and improve your strategic planning.