The cleanest way I could explain the Butterfly Effect was to say:

“Let’s say my shoe is loose. So I decide to bend down and tie it really tighter, inadvertently creating a knot. Let’s say the next morning, I have a hard time getting my shoe on – for let’s say, four minutes. Then let’s say that I miss my bus by just one minute. And the bus has a frequency of thirty minutes. Well then – one seemingly unrelated decision, made 16 hours before and taking all of 2 minutes to execute, has a 30 minute tardiness impact 16 hours later. That’s pretty much like the Butterfly Effect. Writ Small. And Mundane. Without bad acting.”

The Star Trek: TNG way of saying it would be “There’s a cascade failure in the warp core”. But enough of the Laforging.

Cause and Effect dynamics are devilish. After all, my lateness could have been chalked up to not being ten minutes early as I normally am. Or it could be chalked up to the bus being on time, which is unusual. I like to think of the world as a whole bunch of cones converging on a single point. Taken from this point of view, there are as many explanations for something happening as there are people. We all have our perception and are all entitled to own opinions. Though, we’re not entitled to our own facts. (wink).

It’s just a matter of which model has the greatest predictive strength. Normally I’d head down the rabbit hole into a bias about multiple regression…but no. This isn’t going to be a statistical rant. No. I have something far funner to read. (I hope).

And of what implications for the social systems we create?

Twitter is an excellent laboratory to study for that.

And that’s where we’re going to get into a lot of trouble with each other, as social media scientists.

‘How one seemingly innocuous tweet could cause a cascade failure in the warp core?’ will be one of those great analyses someday. And it will be contested. Loudly. By very educated and sinecure analysts.

It won’t necessarily because they won’t accept that little things can make such big impacts. I’ll be referring them on back to this post at that point. And surely, every very educated analyst should be familiar, and indeed, should have experienced such dynamics in their own lives so as to be able to relate. The Butterfly is in the Sky.

Rather, the debate might be how much causality to attribute to the originating tweet, and how much causality to attribute to the reinforcing effects. And indeed, this sub-branch of analytics, of reinforcement-attribution theory, is still very young in marketing science literature. (I salute those of you who have made contributions. It’s just that I wish we had a unified language to describe it.). Someday I’d like to be able to say: “Take a look. It’s in a book.”

How do we understand cause, intervening variables, and effect – and how much we decide to respect where each other is coming from, is by and large going to paint future debates. I’m optimistic that there will exist one school of social media measurement practitioners that will rely on evidence to make assessments. And I’d like to be in that school. I’m certain that we can go twice as high.

There was a little theme running throughout the post.

That’s how little things can make big impacts. And how something little will make something big.