October 2018

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Your Bias is Showing

The essence of Cognitive Bias is to make skewed decisions based on pre-existing factors such as personal experience or preference rather than on the data and other hard evidence.

Paul Campbell,
Director of Analytics,
Phoenix Energy Technologies

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Let us do a thought exercise and quickly examine energy management from a data science perspective. We would be well-served to first make the distinction between data and parameters:

  1. Data is empirical, meaning it is metered or measured and is continuous in nature, subject to changing value over time.
  2. Parameters are artificial thresholds laid upon the data as one sheet of overhead projector transparency film would overlay another in order to gauge if performance conforms to a desired outcome. Parameters do not reside on the same plane as data.

You would be surprised how little this distinction is acknowledged or understood and what the implications can be.

In the field of data science, an arbitrary parameter is commonly accepted as a reasonable starting place in the early stages of data examination (defining cluster boundaries by “eyeballing it” for example). It is not however accepted as a destination for data examination (ultimately the data should express through the model where the boundaries are among clusters) and I think that gets lost in the energy management domain. There is just enough cross pollination of talent among the energy management and data science domains for an individual to achieve but also impair because one can slip seamlessly from starting data examination with an arbitrary parameter into full blown Cognitive Bias unconsciously baked into an Energy Control Measure.

Cognitive Bias is akin to an invasive species that can overtake the habitat of a native species and measures must be taken to eradicate it while preserving a proper energy management habitat. It has many manifestations as there are about 170 varieties of Cognitive Bias hence the invasive species analogy. We’ll just touch upon just a few and organize them in a simple hierarchy to illustrate.

Cognitive Bais

The essence of Cognitive Bias is to make skewed decisions based on pre-existing factors such as personal experience or preference rather than on the data and other hard evidence. Let us now imagine playing the role of Energy Manager and that as part of as part our R&D duties we are expected to prospect and model an Energy Control Measure affecting our national portfolio and see how Cognitive Bias can contaminate our ECM.

Sampling and Selection biases occur in data preparation prior to modeling. These are simple biases of omission where perhaps we skipped a step or got lost among the nuts and bolts. The solution is to apologize, backtrack and reapproach. Embarrassing but not fatal.

Survivor and Confirmation biases are biases of commission. Survivor Bias is based on a false assumption and its solution may entail starting all over again and perhaps we will have to answer for extending the deadline. Confirmation Bias is a conscious choice, perhaps intended to accelerate the pace to meet a deadline while masking evidence that the ECM is contraindicated. It is the lid atop the boiling pot.


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