Understanding what we mean by “Decision” in ML/AI/DI

In artificial intelligence, machine learning, decision intelligence, statistics, and science, we use the word “decision” to mean a lot of things. Let’s tease out some distinctions:

Decision TypeNameQuestion answeredPrimary information SourceTypical success criterionTypical method
AML classification“Decisions That”: “What is this picture?” “What disease does this person have?” “Is this a cat?”DataTrue positive, true negativeSupervised learning
BML regression“Decision about a prediction”: “What will be the Covid-19 incidence next month?” “What will be this security’s price next month?”DataMean squared error, R^2Supervised learning
CDecision intelligence (forward model)Decision to take an action, action-to-outcome mapping: “If I take this action, in this context, what will be the outcome?”Humans (causal model), ML, economics, complex systems models, much more (causal model links)Correct mapping of actions to outcomesComplex systems simulation
DDecision intelligence (optimization)“Given my set of possible actions, what is the best set of actions to take to meet my goals”(same as above)Best set of decisions to reach multi-objective outcomesComplex systems simulation, optimization
EReinforcement learningPolicy creation: “For each state that I can be in, what is the best next action?” (policy)Data and simulationBest set of policies to maximize value of objective functionReinforcement learning simulation
Types of decisions in ML/RL/DI

Cassie Kozyrkov and I have realized that there’s unmet need to fill the space marked “DI”, above, where:

  1. We don’t necessarily have data for the entire decision, yet
  2. people need to make this decision *today*, without waiting to have time to gather the data, and
  3. the decision has big impact, and
  4. we want to make better decisions, and we have some, if not all, data and human expertise available to us yet it’s not being well utilized.

We, along with a few thousand others, have realized that this is an important, yet massively under-treated, corner of the decision problem formulation space.

You can learn about it in this course: Getting Started with Decision Intelligence.

“Decision intelligence is the discipline of turning information into better actions at any scale.” Cassie Kozyrkov, Head of Decision Intelligence, Google

“Decision intelligence answers the question, ‘If I make this decision today, which leads to this action, what will be the outcome tomorrow?’—Lorien Pratt, Chief Scientist, Quantellia

Do you agree? Lately, I’ve heard a case that “Decision Intelligence” should be expanded to include both A and B above.  What do you think?

Thanks to @thenatlog, @neuralnets4life, and @twimlai for inspiring this post

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