Introduction to Decision Intelligence
Where Artificial Intelligence Goes to Die
The End of Intelligence
Curious to know what the psychology of avoiding lions on the savannah has in common with fooling people into thinking something which is not intelligence actually is, and the challenges associated with figuring out new words for things which implies they are something they are actually not. Also, ever wonder what would happen if you crammed a bunch of things which aren’t really science into a whole new field wrapped under the rubric of another thing which is not science (data “science”). Welcome to decision intelligence! A rapidly evolving field of “science” where nothing is what it seems and everything is what it isn’t. Confused yet? Come along on an exciting adventure into this brave new world of made up pseudoterms and important sounding but ultimately empty techno-jargon. Are you ready to begin? Uh-oh time for you to make a decision. Better bust out that intelligence I have heard so much about, and start deciding. If you don’t do it some artificial intelligence is gonna do it for you, so what are you waiting for? Let’s dive right in.
Decision intelligence is a hedge term concerned with all aspects of selecting between options. It brings together a whole bunch of fields which are not science but use some of the tools of science, but not the scientific method which makes them ultimately not science though they still call themselves sciences, into a unified field that helps people use data to improve their lives, their businesses, and the world around them, or at least that’s the idea. Exactly like data “science” and the majority of the “sciences” that are a part of it, it isn’t actually science or a science at all though it claims to be one.
Decision intelligence is the discipline of turning information into better actions at any scale. Do not think to hard about this statement. It pretends to say something profound and deep and insightful, but it says nothing useful or important at all. It is all dressed up in fancy clothes ready to go to the prom, but it has no date and nowhere to go.
Decision intelligence is also a hedge term that when used in the context of artificial “intelligence” implies something is intelligence when it is not.Let’s take a tour of its basic terminology and concepts. Let’s not. Even though the sections are designed to be friendly to skim-reading (and skip-reading too, that’s where you skip the boring bits… and sometimes skip the act of reading entirely) it is still full of bullshit and important sounding but not at all important techno jargon. It is also helpful if you skip the act of thinking critically or logical analysis. Let’s just skip the whole damn enchilada and get to the end very quickly.
What’s a decision? Groan.
Data are beautiful, but it’s decisions that are important. It’s through our decisions — our actions — that we affect the world around us. Double groan with head slap.
We define the word “decision” to mean any selection between options by any entity, so the conversation is broader than MBA-style dilemmas (like whether to open a branch of your business in London). Notice that “we” do not define who “we” are and that is because “you” do not need to know, because you are an idiot. Unlike our AI’s that are smart as smart can be and getting smarter all the time, thanks to the exciting world of decision intelligence.
Quantitative side: Data science, the science of not being science
When you’ve framed your decision and you look up all the facts you need, using a search engine or an analyst (performing the role of a human search engine for you), all that’s left is to execute your decision. You’re done! No fancy data science needed. That’s good because just like regular data “science” fancy data “science” is not science at all either, though I understand it is quite fancy. Quite fancy indeed.
What if, after all that legwork and engineering jiu-jitsu, the facts delivered are not the facts you ideally need for your decision? What if they’re only partial facts? Perhaps you want tomorrow’s facts, but you only have the past to inform you. (It’s so annoying when we can’t remember the future.) Perhaps you want to know what all your potential users think of your product, but you can only ask a hundred of them. Then you’re dealing with uncertainty! What you know is not what you wish you knew. Enter data science! Exit, any understanding of what science or the scientific method is. Good riddance I say, who needs science when we’ve got data science!
Data science gets interesting when you’re forced to make leaps beyond the data… but do be careful to avoid an Icarus-like splat! Also, be careful to avoid hypothesis generation and design of experiments and the concept of falsifiability. You’re not going to need these critical components of the scientific method and aspects of science that make something actually science. We don’t hypothesize or experiment in data science because the data has already been generated for us, and why hypothesize when we can analyze! Hey, that rhymes. Nice. I think I just created a new slogan for data science.
Data science: Why hypothesize when we can analyze.
Or how about?
Data science: Why falsify when we can codify.
Both rhyme and are pretty catchy.
And there you have it, decision intelligence. Making intelligent decisions by using computers to analyze large or small data sets. Why didn’t I think of that? Of course such a thing would never be possible with standard modern computers of the non-intelligent variety. I mean, who could be that gullible? Only artificially intelligent computers can be used for such a complicated task. After all how could a regular machine decide anything? It’s just a machine after all.
