Tartle Best Data Marketplace
Tartle Best Data Marketplace
Tartle Best Data Marketplace
Tartle Best Data Marketplace
Tartle Best Data Marketplace
Tartle Best Data Marketplace
July 27, 2021

Carl Jung - The Modern Man and the Philosophy of Data

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BY: TARTLE

Jung, Stats, and You

“The statistical method shows the facts in the light of the ideal average, but that does not give us a picture of their empirical reality.” – Carl Jung

Pithy, isn’t it? Okay, it’s actually a rather dense quote. What it means is “stop putting people in buckets”. Thanks for coming to our TED talk and we hope you enjoy the day. Just kidding, let’s dig into this a bit. 

First, isn’t it interesting how people can often spot problems early, long before the rest of us catch up. Typically, we ignore them and their concerns until it is years, sometimes decades later and someone else remembers the lost insight. That is the case here. That quote from the great psychologist is from 1957, decades before the digital revolution was underway, yet it is incredibly relevant to the present day. It is an indictment of our over reliance on statistics in our decision making processes. 

Even the fact we tend to ignore insights like this, insights that are ahead of their time, proves the point of the quote. We ignore things like this based on an unconscious analysis that is grounded in statistics. Fifteen years ago, most people would have said, “I’ll never really ignore people in favor of my phone or an attractive spreadsheet.” Because a thing has never happened or has only happened rarely, that doesn’t mean it can’t or won’t happen. We hear this kind of thing in politics all the time. “No one has ever been elected with this….” Insert whatever statistical fact you want. And then it happens.

The truth is, statistics are great predictors, until they aren’t. Just because a thing usually happens in a certain way, there is no particular reason to think they will always go that way. What’s worse is that we think knowing some statistics is the same thing as really understanding something. We tend to treat them as explanatory, when they are only descriptive at best. There are many times when statistics aren’t even properly descriptive. Instead, they are illustrative of the analyst’s biases. 

This is particularly true when applied to people. Imagine someone who gets a ton of ads for Christmas music. Why might that be? Because they often buy Christmas albums? Not necessarily. Remember, the algorithms that drive the ads operate my cross referencing certain behaviors. In this case, let’s imagine that this person with all the Christmas music ads tends to order a new ugly sweater on Amazon every year. The algorithm assumes that the person likes everything having to do with Christmas. Maybe this individual does like most things associated with the holiday. Everything but Christmas music. In fact, our sweater wearing friend hates Christmas music but endures it for the sake of the annual ugly sweater party with his friends. I can guarantee those ads are not going to convert him into a sale for the latest Mariah Carey Christmas album. 

Why do we do this? Why do we make all of these guesses? Why rely so much on assumptions and allow our decisions to be guided by statistics and algorithms? Because it is easy. Find a few statistical correlations and develop an algorithm from them and then run all your data through that. Broadly speaking, the picture it forms may even be accurate. But you don’t really know for sure. You certainly don’t know where it falls short or why. The only way you really can be sure is by going to the individuals behind the statistics, the people actually generating the data that all these programs are trying to classify. Then ask them, “what were you thinking when you did ‘x’?” That’s how you get real knowledge, and real understanding, by treating data with the respect you give to the people who generate it. Because that data represents them and their thoughts. That is powerful and understanding is the first step on the path to real, truthful knowledge.

What’s your data worth?

Feature Image Credit: Envato Elements
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For those who are hard of hearing – the episode transcript can be read below:

TRANSCRIPT

Speaker 1 (00:07):

Welcome to TARTLE Cast with your hosts, Alexander McCaig and Jason Rigby. Where humanity steps into the future and source data defines the path.

Alexander McCaig (00:26):

Hello, everybody. Welcome back to TARTLE Cast. Welcome, welcome. Who to ever thunk that Carl Jung could have anything to say about modern man and data?

Jason Rigby (00:36):

Mr. McCaig, what I want to get into, Alexander, is this. Whenever we look at data and psychology, why is it so important and why is data so attached to humanity, in human consciousness?

Alexander McCaig (00:53):

Because a thought proceeds absolutely every action on this planet. Everything starts with a thought. Whether you want to start a business, whether someone wants to go out there and buy a product. It doesn't matter what it is, somebody has to think first before they actually will themselves to go do it.

Jason Rigby (01:09):

Yeah.

Alexander McCaig (01:09):

It's not will then think, it's think then will, right. So we have to amass this sort of idea of, what's going to happen? What they're choosing to do? So when you look at data, a collection of these habits and behaviors, a thought had to preclude that thing. It's something that started before it.

Alexander McCaig (01:24):

So unless you actually go back to the individual and then once you're at that individual, start to analyze their thoughts, independent of everybody else, then you can start to have a proper picture of what's going on.

Alexander McCaig (01:37):

Now, lucky for us today, we have so much computing power. We have the ability to look at someone as an individual and study just that specific individual with a lot of computing power, and then study everybody else with just that amount of focus and then comparatively analyze them.

Alexander McCaig (01:54):

But instead we choose to just do a comparative analysis and put them through some sort of statistical model and say, that's what that person's like. Well, that's just not true. You haven't actually considered the thoughts of that individual. You've just assumed that they belong in a very rigidly defined bucket of what a human being should look like in a statistical model. But with that, it doesn't carry a lot of value.

Alexander McCaig (02:16):

And so what I want to do is, I'm going to share a quote that I think this was 1955, 1957, that Carl Jung talked about this. And he was talking about the unknown aspects of humanity. But if you focus on it and apply statistical sense, it actually eradicates the value of the human being, their psyche, their thinking, their behaviors.

Alexander McCaig (02:39):

So you're looking at behavior as a lagging indicator and then comparatively analyzing that behavior into buckets across everybody else on mass, and assuming that, that derives a value.

Alexander McCaig (02:49):

But if you skip the bucket approach, the statistical flattening of an individual, and you look at them as an individual, as each one independently unique and then putting the collective together rather than just put it in a collective and define it. It's a totally different value that is derived from that data or from that person through that analysis.

Alexander McCaig (03:10):

And at the same time, you're also respecting that person as a human being, rather than just say, they're a cog in the wheel. It's like, great we have a labor force. Well, that's off of the same criteria that we use on cattle.

Jason Rigby (03:22):

Yes, exactly.

Alexander McCaig (03:23):

Does that number define a human being? No, but that's what we do. We even do it with standardized testing. This idea of statistics is starting to define people, but it flattens personality. It flattens the psyche, it flattens the value of a human being. So, what's your take on that?

Jason Rigby (03:39):

Yeah... No, I want to get into the quote because... And we'll break down the quote, if that's fine.

Alexander McCaig (03:44):

Yeah.

Jason Rigby (03:44):

And this is Carl Jung. This book was written in the 1950s.

Alexander McCaig (03:47):

1957.

Jason Rigby (03:49):

Right, yeah.

Alexander McCaig (03:50):

Four years before he died.

Jason Rigby (03:51):

Yeah, four years before he died. It said, "The statistical method shows the facts in the light of the ideal average." Which you just talked about, "... but does not give us a picture of their empirical reality."

Alexander McCaig (04:02):

So if you talk about empirical reality, what can actually be tested through experience? But through statistics, you don't need the experience. You just say that, this is the experience and then define that experience onto people.

Jason Rigby (04:13):

And taking out experience-

Alexander McCaig (04:16):

Yeah.

Jason Rigby (04:17):

... out of the human?

Alexander McCaig (04:18):

Yeah.

Jason Rigby (04:20):

You're getting such bad data then.

Alexander McCaig (04:22):

There's no human.

Jason Rigby (04:23):

That would be like having Christmas and take all the emotions out but you've [crosstalk 00:04:28] already sampled...

Alexander McCaig (04:28):

But we're at Christmas.

Jason Rigby (04:29):

Right.

Alexander McCaig (04:30):

So, then we're just going to define that these people love Christmas. What if you have the worst time of Christmas all the time, but you still go to it? There's no empirical study because you are defining an ideal average.

Jason Rigby (04:41):

Yes.

Alexander McCaig (04:42):

There was a, no man steps in the same river twice, or just because the average... If someone told you, this river's average three feet deep, I still wouldn't walk across it. What if it's an inch deep, but in the dead center it's 100 feet down?

Jason Rigby (04:56):

Well, I know with digital marketing, everything is beigey. And then we've talked about that before and we encourage people... If you roll the dice enough.

Alexander McCaig (05:03):

Yeah.

Jason Rigby (05:03):

There's going to be that dice is not perfect. It doesn't have a perfect shape to it. So-

Alexander McCaig (05:08):

Wow.

Jason Rigby (05:09):

... if you roll that dice 100,000 times, you're going to start getting some variables on it.

Alexander McCaig (05:13):

All the time.

Jason Rigby (05:15):

So, whenever we look at data, that's the whole idea. If I show this at 100,000 times and then I get a click-through rate of this, this, and then from there, I get a conversion of this, then I can turn around and throw, okay, demographic, age, location. Then I can start... And we've talked about this before.

Alexander McCaig (05:35):

And assuming that age, that demographic-

Jason Rigby (05:36):

Assuming, yeah.

Alexander McCaig (05:36):

.... that person.

Jason Rigby (05:36):

I don't need experience.

Alexander McCaig (05:38):

No, but that's your self defined reality rather than letting the individual define the reality of how they measure themselves. Instead, you are measuring them. Jung, saw this in 1957 and we're still applying these completely unreal ideas. And when you talk about an average, nobody's average. Every single individual is completely unique.

Jason Rigby (05:58):

And when we get into experience, it's... We did that whole article on Telecom, when it came to understanding people working from home and that shift from going into a call center and then people actually doing the calls from home and doing the texting and all that stuff.

Alexander McCaig (06:15):

Yeah.

Jason Rigby (06:16):

But here's the problem, the first thing that they said was they were worried about the efficiency of it. Not the experience that the customer's going to receive and your employee is going to receive.

Alexander McCaig (06:27):

Correct.

Jason Rigby (06:27):

So TARTLE, we would want to hit the both experiences on that because we know if the experience is great, the ROI is going to be great.

Alexander McCaig (06:37):

Correct. And if you just rely on a statistical idea, it's not really founded in the truth. Because first of all, statistics aren't perfect. And statistics also don't speak to reality. Statistics, try to talk about how we perceive reality through an inflexible model. We're just saying, this is how the world works, judging on how we've measured it over time.

Jason Rigby (06:58):

And why are algorithms so based off of this archaic of viewpoint and then so inflexible?

Alexander McCaig (07:05):

But if they're inflexible, because the aspects of our scientific progress have been around statistics or theories around statistics. When someone calls something a scientific law it's because statistically it's happened so many times that it becomes improbable for it-

Jason Rigby (07:21):

Yeah.

Alexander McCaig (07:21):

... to happen otherwise.

Jason Rigby (07:22):

Yes, yes.

Alexander McCaig (07:23):

So, we become so absorbed and reliant on statistics. It's eradicated all the beauty and distinct characteristics of this world or individual things. Whether it's a rock or a human being or a bird or an atom. All of these things are special in and of themselves. But when we lump them into statistical models, then we make a theory about the world rather than looking at empirically, through observational experience about what we really know, rather than using statistics to assume that we know.

Jason Rigby (07:54):

Yeah, and it's like... I was sharing with you, the guy that created the whole... Henry Ford and all of them used it, in the sense of taking machines and then taking a person, and then taking a stopwatch and then getting it more efficient, the process, more efficient, more efficient, more efficient, and more efficient.

Alexander McCaig (08:10):

Yeah.

Jason Rigby (08:11):

To the point of where it's like... And he even said this... A racist comment like, "I don't need these people to think, I need them to do this action."

Alexander McCaig (08:19):

And that's what it's becoming. Large tech also operates like a state dictator. This is how we want people to think. This is what we want them to buy. There's actually no respect for the human being when you apply them to a statistical model. It's a function of coercion rather than education.

Jason Rigby (08:36):

Well, it's also... Not to interrupt you, but Alex, this is really important because it's also this whole idea of imposing on someone. For instance, I think the Subaru's a great example.

Jason Rigby (08:52):

And we've talked about this 100 times, so I don't want to get into example, but it's like, I know you're looking at a Subaru Outback, so I'm going to sit there and hammer you, literally hammer you with retargeting ads, over and over and over and over again.

Jason Rigby (09:08):

And then I'm going to try to take you from upper funnel, mid funnel, lower funnel, and then to get you to make a purchasing. But I'm assuming right off the bat, I'm assuming, because you went to a subaruusa.com and looked at a car, that you're in the market.

Alexander McCaig (09:23):

Yeah. But you're also forcing people into that statistics.

Jason Rigby (09:26):

Yes, that's what I'm saying. Yeah.

Alexander McCaig (09:27):

You're not allowing them to sit outside of it because that's what you've limited the model to, you've limited everything to.

Jason Rigby (09:32):

Well, how expensive is that?

Alexander McCaig (09:32):

It's so expensive.

Jason Rigby (09:34):

Where Subaru could come into TARTLE and say, hey, everybody that hits our page, we want to turn around and invite them to TARTLE. And then we're going to ask them, how far out? Are you 30 days out? Are you 60 days out? Are you 90 days out?

Alexander McCaig (09:46):

Yeah.

Jason Rigby (09:46):

Are you comparing it to the RAV4? Are you comparing it to Mazda CX-5? Whatever it may be.

Alexander McCaig (09:51):

And that's a good comparisons. What about you just ask them about their emotional state? Are you looking at a car because you're just thinking about the future and what prosperity might look like for you?

Jason Rigby (09:58):

Yeah. Is 24 to 36 year old females that are looking at Outbacks, are they looking at the safety with the car seat in the backseat?

Alexander McCaig (10:05):

Yeah, what is-

Jason Rigby (10:06):

Is that the number one driver?

Alexander McCaig (10:07):

Or you have the correlations. It's not the fact that you're looking at the car and the color and their age, but what if is it, are you pregnant?

Jason Rigby (10:13):

Yes.

Alexander McCaig (10:14):

Do you have a family on the way?

Jason Rigby (10:15):

Mm-hmm (affirmative).

Alexander McCaig (10:17):

Interesting.

Jason Rigby (10:17):

Right.

Alexander McCaig (10:18):

That's why you're looking at the car. It has nothing to do with the other comparative aspects of the brand or the price. [crosstalk 00:10:23].

Jason Rigby (10:23):

Well that's because we're taking-

Alexander McCaig (10:24):

You're only looking at family and safety.

Jason Rigby (10:26):

That's because we're taking the empirical reality out of it. And he said this too, "... while reflecting an indisputable aspect of reality."

Alexander McCaig (10:34):

Okay. So that's what the statistic is doing. It's reflecting an indisputable aspect of reality. It can falsify the actual truth in a most misleading way. So we have an aspect of reality, right, but it's a reflection. It's not taking the actual part of reality. It's just saying, how do we reflect it into a mathematical model? Okay.

Alexander McCaig (10:56):

And then how do we take that and say that the reflection is the truth. That's like saying, I'm going to base my entire life on a mirage. If you're in the desert, are you going to base your life choice of survival on a mirage?

Alexander McCaig (11:08):

Or you're going to go to the legitimate oasis over here? What you're doing is, you're looking at the statistical model here but the truth is over here to your left. You're guiding yourself in the wrong direction even though the compass is telling you to go west.

Jason Rigby (11:20):

Yeah. And we had talked about it before, but these footprints on the moon.

Alexander McCaig (11:24):

Yeah.

Jason Rigby (11:25):

And we don't know there was some debris trash but it looks... It's in a weird pattern. It looks like there's these crazy footprints on the moon. If we look at the footprints alone, we can sit there and analyze that and analyze that and analyze that, and then come to a conclusion that could be so far off, based off of those tracks.

Alexander McCaig (11:47):

Correct.

Jason Rigby (11:48):

But we don't know the true intent of those tracks.

Alexander McCaig (11:51):

Yeah.

Jason Rigby (11:53):

We're assuming and guessing and being passively aggressive. And then we can turn around and say, yeah, those are alien based.

Alexander McCaig (12:01):

Yeah.

Jason Rigby (12:01):

And that's proof on the moon that there was a large alien walking.

Alexander McCaig (12:06):

Huge, [mongo 00:12:07] -

Jason Rigby (12:07):

A huge alien.

Alexander McCaig (12:08):

[Mongo 00:12:08] alien, yeah.

Jason Rigby (12:08):

Yeah. A huge alien walking. And then we're going to study, how long it takes for decay, and list goes on and on. But now we've created something. Think about this, now, we've created a reality that's false.

Alexander McCaig (12:22):

The thing is, did you-

Jason Rigby (12:23):

Based off the tracks.

Alexander McCaig (12:24):

Did you watch the tracks actually occur?

Jason Rigby (12:26):

Yeah.

Alexander McCaig (12:26):

No.

Jason Rigby (12:26):

Yes.

Alexander McCaig (12:27):

So, you've taken this mirage idea in your mind with statistics and you've analyzed it comparatively to something where you never even actually witnessed it happening.

Jason Rigby (12:36):

Mm-mm (negative).

Alexander McCaig (12:37):

So, now the individual experience of the creation of that track or whatever it be, it's stripped, it's gone. It actually doesn't matter at that point. You're just said, what matters is the mirage. It's this idea around what we actually think is the truth. And now we're just going to analyze what we think is the truth.

Jason Rigby (12:55):

And then what we think is the truth, we've developed algorithms based off of what we think is the truth. Now we're force-feeding.

Alexander McCaig (13:02):

Yes.

Jason Rigby (13:02):

That's a great word. Now, we're force-feeding consumers this faults mirage.

Alexander McCaig (13:07):

There's nothing empirical, there's nothing [crosstalk 00:13:09] objective about that experience at all.

Jason Rigby (13:10):

The next sentence in this statement, and this is only one paragraph of Mr. Jung. It says, "This is particularly true of theories, which are based on statistics."

Alexander McCaig (13:20):

Yeah.

Jason Rigby (13:21):

"This is particularly true of theories, which are based on statistics."

Alexander McCaig (13:24):

Particularly true. You'll find that, that is what a statistic is. You're trying to measure the world, but you apply averages to it. Mean, median, mode, box whisker, I don't care what it is. You're applying an idea to something when you really have no individual experience. You're just trying to fill in this experience because you weren't there to experience it, or you weren't there to speak with the people to have them experience it.

Alexander McCaig (13:49):

That's why you forced them into boxes. You thought taking this statistical, economically efficient route was more efficient, but in the end you squeeze the value out of it.

Jason Rigby (13:58):

Yes, yes.

Alexander McCaig (13:58):

There's no value left.

Jason Rigby (14:00):

Exactly.

Alexander McCaig (14:00):

You're not analyzing anything. You're essentially testing against your own model, which already sucks in the first place. It's like I've applied a valueless model or we're doing a great job with our valueless model.

Jason Rigby (14:10):

Yeah.

Alexander McCaig (14:11):

How well did you see that mirage? Well, I saw it pretty well and I also saw giant elephant walking around the middle of the desert. No you didn't. No, you didn't. That's just because you thought you did.

Jason Rigby (14:20):

No, no. What you've done is, you've created the elephant in the room.

Alexander McCaig (14:25):

I know that. That's what I'm saying, it's a mirage. It's this-

Jason Rigby (14:27):

Yeah.

Alexander McCaig (14:27):

... idea in your mind.

Jason Rigby (14:28):

Yeah.

Alexander McCaig (14:28):

It's a delusion of these models. This is a huge part of the problem. And this is why technology has evolved so much, science so much in a statistical sense but it has left man behind. There's no evolution of man to come with it because we have not met man with the technology. We've actually separated from it. We thought we could take something that was in a sense, non-human, the statistics and apply it to define a human being.

Jason Rigby (14:53):

You're so right.

Alexander McCaig (14:54):

Human beings created statistics.

Jason Rigby (14:56):

Yes.

Alexander McCaig (14:56):

Statistics did not create human beings. That's just the logical structure of it. Why do you keep focusing your analytical models, your datasets, your algorithms, all of these things in a comparative sense when it's not truly comparing things properly? You have no empirical evidence.

Jason Rigby (15:11):

We did that whole episode. It's the same on the consensus-

Alexander McCaig (15:13):

Yes.

Jason Rigby (15:14):

... here in the United States. You're interrupting people and then the people that actually respond, who are they? And this is so interesting. That's why I wanted to say that because this goes right in hand with this. It says, "The distinctive thing about real facts; however, is their individuality."

Alexander McCaig (15:32):

Our real fact realize with that individual experience, that's the fact. I cannot apply that experience on everything else. I can't do it. That fact belongs with that, with that individual person. If I go and stare at a piece of art at museum, the way I interpret that is going to be different from all the tens millions of people that go through that art museum.

Jason Rigby (15:54):

Well, I mean, using the consensus here, here's another fact is, I can sit there if I pull emotion out.

Alexander McCaig (16:01):

Yeah.

Jason Rigby (16:01):

I can sit there and say, well, people in the Midwest just don't fill out the consensus.

Alexander McCaig (16:04):

How do you know that?

Jason Rigby (16:05):

They're disobedient, they're not Americans.

Alexander McCaig (16:07):

Blanket statements.

Jason Rigby (16:08):

Where they could be fairly conservative, far right, and have a huge distrust for the government.

Alexander McCaig (16:13):

Yeah.

Jason Rigby (16:13):

They're not going to fill out any consensus. They're not going give you information.

Alexander McCaig (16:15):

They're disobedient in your model.

Jason Rigby (16:17):

Yes.

Alexander McCaig (16:17):

Because-

Jason Rigby (16:18):

Exactly.

Alexander McCaig (16:18):

... you created a model about what you thought reality was, of course they don't fit in it.

Jason Rigby (16:21):

Yes.

Alexander McCaig (16:22):

Because it's not a real empirical model that values the individuality of their own facts.

Jason Rigby (16:28):

And we could ask them that through TARTLE. We could pay for their response and then understand exactly what that collective human thought is, in those different regions.

Alexander McCaig (16:38):

Because defines the action.

Jason Rigby (16:39):

Yes.

Alexander McCaig (16:40):

Thought defines our future, thought defines that destiny of that individual, your company is bound by thought.

Jason Rigby (16:46):

Yeah. And this statement right here blows my mind on this. "That the real picture..." This is Mr. Jung, "That the real picture consists of nothing but exceptions to the rule."

Alexander McCaig (16:59):

Yeah.

Jason Rigby (16:59):

"And that, in consequence, absolute reality has predominantly the character of irregularity."

Alexander McCaig (17:07):

I say this all the time. We're completely irrational all the time.

Jason Rigby (17:09):

Yes, yes.

Alexander McCaig (17:10):

We're always doing whatever the hell we want.

Jason Rigby (17:11):

Our purchase patterns are irrational.

Alexander McCaig (17:12):

We're always doing whatever the hell we want, but they force us into these purchase part... Let me ask you something. If there's a register when I go to buy something, that is the only way I can go buy it. I'm forced into that model. But otherwise, maybe I have a different preference about how I like to purchase.

Alexander McCaig (17:32):

There's irregularity to it, but statistic forces a regularity that doesn't belong. It's completely unnatural. If I had regular statistical models applied to nature, it wouldn't work. Why? Because every time a flower blooms, every petal is unique.

Jason Rigby (17:46):

Yes.

Alexander McCaig (17:47):

Every pestle is unique.

Jason Rigby (17:48):

Yes.

Alexander McCaig (17:49):

Every stem is unique, every cloud is unique. If I put a statistical model on what the shape of the next cloud is going to be, it's going to be wrong. Because every single one is specific to itself. You can't apply statistics to nature like that.

Alexander McCaig (18:01):

Every single person is an exception to the rule. That's because your rule suck. You rewrite your rules all the time. If you truly had an algorithm that gave you a definitive truth, you'd only have to write it once. It wouldn't be companies and all these people coming up with all these different algorithms to analyze something.

Jason Rigby (18:15):

Well, it's so amazing, especially... Because you can take this into the marketing scheme. It's so amazing whenever they assume, and we'll use Subaru again, we're not picking on them but-

Alexander McCaig (18:25):

No.

Jason Rigby (18:26):

... the automotive industry in general. It's so assumed to say, okay, you wanted to purchase a Forester? So, I got you mid funnel. So, now I need to push you with discount.

Alexander McCaig (18:35):

Yeah.

Jason Rigby (18:35):

Not value, but discount.

Alexander McCaig (18:37):

No. They're trying to coerce you into something.

Jason Rigby (18:38):

Where Nike turns around and says, hey... I'll use them an example. Hey, we have an app. You've got to log on there at 8:00 in the morning. And by 8:05, all those shoe... That specific customized individual shoe-

Alexander McCaig (18:50):

Yep.

Jason Rigby (18:50):

... is going to be sold out. Those Jordans are going to be sold out unless you get them within 10 minutes. So they've created scarcity, which is an evolutionary pattern.

Alexander McCaig (18:59):

Yeah. And it's also a false model.

Jason Rigby (19:00):

Yeah.

Alexander McCaig (19:01):

You're forcing otherwise, I wouldn't be here at 8:00 AM. I wouldn't be using your damn app. And you're going to be like, look at our response rate. It's because you're putting this idea of fear and scarcity-

Jason Rigby (19:10):

Yes, yes.

Alexander McCaig (19:10):

... into the individual.

Jason Rigby (19:10):

Exactly.

Alexander McCaig (19:11):

And forcing them to do something they otherwise wouldn't do.

Jason Rigby (19:13):

Which creates distress, which creates bots that are purchasing these shoes-

Alexander McCaig (19:17):

Yeah, on behalf of the individual.

Jason Rigby (19:19):

Yeah, on behalf. Because when there's scarcity, you're going to have competition.

Alexander McCaig (19:24):

Yeah. So then what do you have? You have a computer analyzing a computer, and they both use shit models.

Jason Rigby (19:26):

And so, what happens to the person, the actual... Not the bot, but the actual person that is legitimately and honestly wanting to purchase that shoe?

Alexander McCaig (19:34):

You squeeze them out of the market.

Jason Rigby (19:35):

Yes.

Alexander McCaig (19:36):

It's the same thing that's happened in finance. 85% of the trades are all done with computers.

Jason Rigby (19:39):

Yes.

Alexander McCaig (19:39):

Maybe more.

Jason Rigby (19:40):

Yeah.

Alexander McCaig (19:40):

People are like, people are training. And then you use your little financial analysis of highs and lows or... What do they call it? Support and resistance-

Jason Rigby (19:50):

Yeah, yeah, yeah.

Alexander McCaig (19:50):

... levels, Fibonacci.

Jason Rigby (19:53):

So that's a perfect example because let's say, we have 85% of the trading based off Fibonacci model. That model may be totally debunked but if 85% [crosstalk 00:20:02].

Alexander McCaig (20:02):

But everything trades off of it.

Jason Rigby (20:03):

If everything's trading off of that, that creates this fault rule he's talking.

Alexander McCaig (20:07):

That's precisely correct. And then you don't appreciate the irregularities of it. You create a regular market, you statistically flatten it. And think about the idea of finance, the price goes up or the price goes down. There's no true variation. The vectors of which it can go up and down, but it can't shift left and right.

Jason Rigby (20:28):

Well, I think perfect example is this whole idea with GameStop.

Alexander McCaig (20:31):

Sure.

Jason Rigby (20:31):

And then Reddit, a Subreddit and then from there all these guys on Robinhood. The young guys on Robinhood, just sitting there purchasing and then going against these hedge fund managers and they're doing it out of spite or whatever, but the power...

Jason Rigby (20:45):

People need to understand, and the governments and big tech and all these companies don't want you to understand that. But just as GameStop, just a small version of this, if individuals will unite and get together and understand that 90%, they have unity. Like on the political thing, most people will agree more than disagree.

Alexander McCaig (21:06):

Yeah.

Jason Rigby (21:06):

Do not allow the media, do not allow governments, do not allow corporations to not just define you, but cause division because that's what they want. They want fear and scarcity.

Alexander McCaig (21:18):

Yeah.

Jason Rigby (21:18):

Because they want to make profit off of you.

Alexander McCaig (21:20):

Correct.

Jason Rigby (21:21):

And that is the thing that we see here in this. Because he says, "The real picture consists of nothing but exceptions to the rule." And you're an exception to the rule.

Alexander McCaig (21:29):

As you're-

Jason Rigby (21:30):

And I am an exception to the rule. I am an individual with freewill.

Alexander McCaig (21:32):

Same as this damn dog here in the room. It's an exception to the rule or the out dog psychology. Well, everything is different. Everything is completely unique.

Jason Rigby (21:40):

The chance of you being born and being in this world, what? I think it's one in something billion.

Alexander McCaig (21:43):

Yes. So, why are you putting it in a statistical bucket to say that I'm just like everybody else?

Jason Rigby (21:47):

You're not.

Alexander McCaig (21:48):

It's ridiculous.

Jason Rigby (21:49):

And how can we, the people, unite globally-

Alexander McCaig (21:54):

Yeah.

Jason Rigby (21:55):

... and take back our freedom?

Alexander McCaig (21:56):

The best way you can do that is remember that you are an individual. All of your thoughts are special and unique to you. And that you can come together in unity with everyone else, but you don't have to lose your identity in doing so.

Alexander McCaig (22:08):

Understand you have the freewill of choice. You have the freewill of thought, and that your thoughts will define that future. And if all of you come together through that collective decision and you don't give up what it means to be a human being, then you can drive yourselves back into that power of the human being. Taking charge of that future, truly evolving to where we need to evolve to.

Jason Rigby (22:28):

And how does TARTLE serve this purpose?

Alexander McCaig (22:31):

TARTLE serves this purpose because you're allowed as a data buyer to come meet those individuals who want to sell that information. That is their thoughts. That is their behaviors. That is them being the irregularity and meeting them as that very individual person in this world.

Alexander McCaig (22:49):

Don't meet them as a statistic. Don't meet them as a piece of cattle out in the field. Meet them as a human being. And it solves that because you're respecting them for their thoughts. You're actually analyzing the thought.

Speaker 1 (23:00):

Thank you for listening to TARTLE Cast with your hosts, Alexander McCaig and Jason Rigby. Where humanity steps into the future and the source data defines the path. What's your data worth?

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