www.bayesia.com does not fully support your browser (Internet Explorer 6).
We suggest upgrading to IE 7 or downloading Firefox for a more enjoyable web experience.

BEKEE, Expert Knowledge Modeling with Bayesian Belief Networks

Everybody is talking about "Big Data" and all the manifold opportunities that are associated with it. Very often though, in the same breath, we hear about the challenges that come with the flood of data. Where to store it, how to analyze it, how to explain it, the list goes on and on. We think this is a very nice problem to have. Much more serious problems exist on the opposite end of the spectrum, where there is not enough data. Unfortunately, all the advanced knowledge discovery algorithms fail when data is too scarce.

In over ten years of continuous development, and in increasingly sophisticated ways, BayesiaLab has permitted deriving knowledge from data through its machine learning algorithms, very much in the spirit of understanding "Big Data". However, BayesiaLab has maintained an equal focus on managing knowledge that exists beyond measurable and countable data points, such as the knowledge contained in the human mind. BayesiaLab's graphical user interface has made it highly intuitive for individual subject matter experts to encode their own domain understanding into a Bayesian network, thus capturing what they explicitly or implicitly know. What is especially important, one can very easily and formally capture causal directions in a Bayesian network graph, which is something that few other frameworks can do.

However, when it comes to consolidating the collective knowledge from a group of experts, rather than from an individual, the process is not as straightforward any longer. Traditionally, one would perhaps bring the experts together in a brainstorming session and let them form a common understanding. Subsequently that consensus could be then encoded manually. Needless to say, however, brainstorming sessions are prone to introducing a wide range of biases, which can be disastrously counterproductive in studying complex domains.

BAYESIA Expert Knowledge Elicitation Environment, or BEKEE for short, is a new web application that is designed to minimize detrimental group biases. The central idea is not to coerce consensus, but rather to elicit everyone's individual views regarding the domain under study. In order to ensure independent elicitation of probabilities, BEKEE queries stakeholders individually via an interactive questionnaire linked to the core BayesiaLab application. Retrieving expert views in such a fashion generates many "parallel universes" in terms of domain understanding. These different perspectives can be formally compared by the facilitator and potentially returned to the group for a formal debate in the case of seriously conflicting assessments.

BayesiaLab compiles all experts’ views and produces a unifying Bayesian network. This graph is now the summary of all the available expert opinions. As such, it can be utilized as a formal representation of the underlying domain. Most importantly, this graph is not merely a qualitative illustration, but a fully computable model of the domain, which immediately allows the simulation of what-if scenarios.

Download article (16 MB)