This seminar was recorded on May 12, 2017, at Indiana Wesleyan University in West Chester, Ohio.
We shall see that systematically eliciting and encoding numerous pieces of (admittedly imperfect) knowledge into a Bayesian network can produce a remarkably useful approximation of the underlying domain. In a way, we leverage the "wisdom of crowds" to quantify a multitude of interactions within our high-dimensional problem domain. As a result, the audience's collective understanding is represented in a Bayesian network, which provides us with a common framework for reasoning about the problem domain, such as producing a volume estimate or evaluating policy options, e.g. tax incentives, etc.
Furthermore, by using a Bayesian network model, we can preserve all the uncertainty that exists in the audience's knowledge and perform inference consciously taking into account all that uncertainty. Thus, we manage to avoid two common and problematic extremes in reasoning, i.e., (i) suppressing uncertainty by calculating with the fake precision of single-point estimates, or (ii) being overwhelmed by uncertainty and abandoning quantitative reasoning altogether.
Knowledge Elicitation Exercise: The Potential of Electric Vehicles
Back in 2011, President Obama predicted that by 2015 there would 1 million electric vehicles on the road in the U.S. As it turns out, less than 30% of that target was achieved by 2015. Today, the number of electric vehicles being sold remains tiny considering the size of the overall vehicle market. It is easy to come up with a long list of reasons for why sales are so low: limited range, inadequate charging infrastructure, long charging time, high battery cost, relatively low fuel prices, etc.
What's the True Potential?
More important than looking for what currently constricts electric vehicle sales is determining what would happen under counterfactual conditions: what if the electric vehicle range were better, what if there were more charging points, what if batteries were cheaper, what if fuel prices were higher? Such counterfactual conditions might help us understand the ultimate volume potential of electric vehicles. That is the quantity we clearly wish to know for making sound decisions about long-term investments in infrastructure and product development.
No Data = No Model = No Science?
Of course, if we had a mathematical model describing all the dynamics in this problem domain, we could compute various scenarios and come up with a volume forecast. However, we don't have any data from which we could generate such a model. Currently, none of the available electric vehicles are close to the technical specifications that we may wish to consider as realistic scenarios for the future.
Without data, and without a mathematical model generated from such data, are we, then, stuck with mere speculation? Is it even possible to come up with reasonably objective volume estimate?
In this day and age of "Big Data," we may be led to believe that facts can only be established from data, especially in the context of a scientific inquiry. This is a misconception. Even without data, humans do often possess reliable knowledge, qualitative or quantitative, tacit or explicit, about many aspects of the world. We believe that a useful amount of knowledge exists regarding the problem domain at hand. Also, there is one particular type of knowledge that data on its own cannot yield, and that is causality. For that, we always have to rely on human expertise.
The Wisdom of Crowds
Although there may not be a single expert who can fully comprehend the entire domain, there may be numerous individuals, or stakeholders, who are more or less knowledgeable about different parts of the puzzle. It is our objective to break down the overall problem domain into numerous simpler questions, which are perhaps more easily "knowable," at least to some.
So, we are not looking for a single authoritative opinion. Rather, we are looking to collect and consolidate the full spectrum of thought, including causal relationships, from a number of stakeholders. This is where the "wisdom of crowds" comes into play. We want stakeholders to provide their individual and independent assessments of different elements and relationships within the problem domain.
Knowledge Elicitation with the Bayesia Expert Knowledge Elicitation Environment
While the objective of collecting multiple opinions is straightforward, there are many technical and practical challenges in terms of implementing such a process. In this seminar, we propose employing the Bayesia Expert Knowledge Elicitation Environment (BEKEE) for this purpose. More specifically, we plan to utilize BEKEE for collecting the opinions of our seminar participants about the forces that affect the electric vehicle market, such as the issues mentioned above. We certainly don't expect to have any bona fide experts in this field. Rather, we anticipate a fairly diverse range of opinions given the presumably wide range of backgrounds of the participants.
Spreadsheets are Deterministic, Opinions are Probabilistic
One may be tempted to think that the knowledge collected via BEKEE can be assembled in a spreadsheet so that various conditions can be simulated. Unfortunately, spreadsheets are of little use in this context. A multitude of opinions cannot be neatly translated into spreadsheet formulas.
Quite naturally, combining multiple assessments across numerous dimensions produces uncertainty, which cannot be directly encoded in a spreadsheet. A Bayesian network, on the other hand, can explicitly capture the uncertainty generated from the diversity of opinions. Using the BEKEE platform, we can directly encode the elicited knowledge, along with all the associated uncertainty, into a Bayesian network in using BayesiaLab software platform.
Reasoning with Bayesian Networks and BayesiaLab
On the basis of the newly-generated Bayesian network, which represents the collective knowledge of the seminar participants, we can use the simulation tools in BayesiaLab to reason probabilistically about the implications of various hypothetical conditions. Thus, the collective knowledge will produce a volume estimate, i.e. the principal quantity of interest in this seminar.
Furthermore, we can use the same Bayesian network model to reason about the causal effects of hypothetical policy mandates and other government interventions beyond the natural equilibrium state of the electric vehicle market.