Decision Intelligence and Data

Cassie Kozyrkov (link) has written about “decision intelligence is a new academic discipline concerned with all aspects of selecting between options”.

This decision intelligence “brings together the best of applied data science, social science, and managerial science into a unified field that helps people use data to improve their lives, their businesses, and the world around them”. Cassie views it as a “vital science for the AI era” and it turns “information into better actions at any scale”.

Cassie lists some basic terminology of the discipline:

Decisions: It’s through our decisions — our actions — that we affect the world around us. (It is “any selection between options by any entity’.)

Decision Maker: The person who is responsible for decision architecture and context framing, a creator of meticulously-phrased objectives.

Decision Making: taking responsibility for an action when there were alternative options.

Taxonomy: decision sciences (qualitative side). Cassie suggests “Think of the decision science side as dealing with decision setup and information processing in its fuzzier storage form (the human brain) rather than the kind that’s neatly written down in semi-permanent storage (on paper or electronically) which we call data“.

She adds: “Strategies based on pure mathematical rationality without a qualitative understanding of decision-making and human behavior are relatively naïve and tend to underperform relative to those based on joint mastery of the quantitative and qualitative sides”.

A large part of Cassie’s article looks at decision making and facts. She observes “with training in the decision sciences, you learn to reduce the effort that it takes to make rigorous fact-based decisions, which means that the same amount of work now gets you higher-quality decision-making across the board”. But, alas, “we live in the real world and often we must work for our information”.

She adds “Data science gets interesting when you’re forced to make leaps beyond the data…”. This involves the bringing together of diverse perspectives in decision intelligence.

It requires us, as Splunk suggests in a Priceonomics post (link) to make some important clarifications about dark data (“most of the data collected by businesses simply goes unused”) and our role in making decisions about the data we have collected and see as important.

Photo Credit

Frederick Tubiermont on Unsplash

Sylwia Bartyzel on Unsplash


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