Everybody is talking about "Big Data" and all the manifold opportunities that are associated with it. Very often though, we hear almost as much about the challenges that come with this 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 in the absence of data.
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 such a consensus could be encoded manually. 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 the 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.
In most cases, this is an iterative process and, even if stakeholder opinions do not converge, BayesiaLab will compile all views and produce a unifying Bayesian network. This graph is now the mathematically correct 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. Rather, a Bayesian network is fully computable model of the domain, which immediately facilitates the simulation of what-if scenarios.
In fact, we can evaluate this Bayesian network model the same way as a statistical model estimated from "Big Data". One might still prefer a data-based model, if data were indeed available, but in the absence thereof, the formally-encoded collective expert knowledge best represents what is known at the time.
Expert Knowledge Modeling with BEKEE and BayesiaLab
Manager/Facilitator Side of BEKEE
Often in any business environment either there are too many experts or too few....what would you say is the optimal number of experts for BayesiaLab to work perfectly?
Once the BBN’s objective is clearly defined (the target node), we identify the conceptual dimensions that are linked to the target (e.g. HR, Management, Production, …). The optimal number of experts is directly dependent on the number of conceptual dimensions. Having 2 or 3 experts per dimension generally allows generating fruitful debates without loosing too much time in useless discussions.
Note that this is a “rule” for our 3-day in-person sessions, mainly dedicated to the elicitation of the structure. Once the structure fully validated and documented, the Batch part of BEKEE can also be used to gather quantitative knowledge from additional experts.
Suppose non-directional links were used. What's the difference? Does that just mean we have to supply conditional probability values in both directions?
A BBN is a Directed Acyclic Graph. As such, we cannot have any undirected link between two variables.