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Speaker 1: It's Night Side with Dan Ray. I'm tell Youbsy Costing's

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Speaker 1: news radio.

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Speaker 2: Brady J for Dan to night on nights Side. The

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Speaker 2: phrase algorithmic pricing may not strike fear into your heart yet,

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Speaker 2: but only because you might not know what it is,

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Speaker 2: and actually you might think it's a good thing. I

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Speaker 2: think it might depend on who you are, how much

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Speaker 2: money you have, how much you value your privacy, et cetera,

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Speaker 2: or how much you value that notion that long since

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Speaker 2: gone notion of privacy that's there is no real privacy anymore.

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Speaker 2: But we're gonna find out about algorithmic pricing. Did you

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Speaker 2: know you may likely be charged a different amount for

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Speaker 2: the same goods depending on who you are. The machine

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Speaker 2: that sells you your computer is gonna know all about you.

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Speaker 2: And figure hm, that's Dot from Boston. I think she'll

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Speaker 2: pay two hundred and ninety five dollars for the whereas

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Speaker 2: somebody else on a different computer with different information. Oh,

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Speaker 2: I bet they'd pay five hundred.

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Speaker 3: Is that fair? Do you think it's coming? Well, we'll

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Speaker 3: find out algorithmic pricing. Our guest is Noah G and Sarahcusa.

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Speaker 3: And if I'm not mistaken, that U means John of Syracuse,

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Speaker 3: Syracusa in Sicily, home of the great inventor Archimedes? Is that?

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Speaker 3: Am I on track? There?

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Speaker 4: You are spot on that. It's absoulley correct. I'm impressed

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Speaker 4: you did your homework there.

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Speaker 2: I like words, and I knew Syracusa had to be that,

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Speaker 2: and then of course the GM has got to be John.

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Speaker 3: So there you go.

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Speaker 4: Most people just think of Syracuse, New York, but you

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Speaker 4: know that's close enough.

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Speaker 2: Tell us about you before we get into this algorithmic pricing.

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Speaker 4: So I'm a math professor at Bentley and Waltham, and

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Speaker 4: I try to keep up with the news for my

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Speaker 4: teaching because I want to help my students know what's

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Speaker 4: going on in a lot of the world right now

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Speaker 4: as AI and algorithms, And something that's been coming up

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Speaker 4: in the news lately is this algorithmic pricing. It goes

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Speaker 4: by feet different names. If you've heard of it, you

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Speaker 4: might have heard of it as personalized pricing or dynamic pricing.

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Speaker 4: And it's the basic idea what you said, that you

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Speaker 4: might get a different price, like if you, let's say,

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Speaker 4: door dash your dinner, you might potentially get a different

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Speaker 4: price as even someone in your own house, just on

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Speaker 4: a different phone, different computer, different data goes into it.

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Speaker 4: So we can get into why that happens and why

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Speaker 4: it's scary. But that's what it is, just different people

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Speaker 4: getting different prices.

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Speaker 2: So first it seems as though if big business decides

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Speaker 2: that's the way I want to go, there's nothing we

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Speaker 2: can there's nothing we can do as as consumers about it.

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Speaker 3: Or is there if we did not like it?

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Speaker 4: You know? Good question. So literally just today, I'm not

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Speaker 4: making this up. I had lunch. I'm actually in DC

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Speaker 4: at the time. That's why I'm calling on the phone

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Speaker 4: and said, have in your studio. I had lunch with

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Speaker 4: the lawyer from former lawyer from the FTC. He's moved

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Speaker 4: on last year or so, and I was asking him

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Speaker 4: this very question. I said, is this even illegal? You know,

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Speaker 4: we might want to try to stop it, but is

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Speaker 4: is it illegal? And he said I didn't know this

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Speaker 4: when the FTC, So the Federal Trade Commission was founded.

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Speaker 4: They sort of have a charter that introduced the FTC.

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Speaker 4: And one of the things, you know, I forget the number,

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Speaker 4: paragraph five or section five or something like that. It's

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Speaker 4: on legal term says, I forget the exact name the

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Speaker 4: wording that he used, but it was something like unfair

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Speaker 4: and misleading practices are illegal as far as FTC is concerned.

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Speaker 4: So how you exactly interpret that? You know, that goes

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Speaker 4: to courts and judges and juries. So there's a lot

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Speaker 4: that goes into that. But there is some central idea

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Speaker 4: that goes across the whole country that unfair practices in

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Speaker 4: retail are illegal. We don't need a specific law that

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Speaker 4: bans this kind of pricing. That it's just you can't

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Speaker 4: do unfair things. So he said that there is a

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Speaker 4: reasonable argument that the whole idea, you know, tech companies

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Speaker 4: want to do this, that it should be illegal, but

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Speaker 4: it's not obviously breaking a law, like it's something that's

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Speaker 4: going to have to go to corporates.

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Speaker 3: Two things first, if it's unfair, how is it unfair?

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Speaker 4: Okay? So that great question. So part of it was

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Speaker 4: this idea, I forget again the exact wording that he used,

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Speaker 4: but it's something like misleading or a deceptive that was

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Speaker 4: a deceptive practices, And he said it's it's not exactly

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Speaker 4: clear what the argument would be, but there is some

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Speaker 4: idea that imagine again like you know, i'm i'm i

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Speaker 4: live in actin with my wife, and if I order

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Speaker 4: something and she orders something and it costs her a

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Speaker 4: different amount than me, there's some fundamental feeling that that's

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Speaker 4: not fair. You know, why is she getting a different price.

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Speaker 4: It's not because she is in some loyalty program or

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Speaker 4: she you know. The only thing that might be different

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Speaker 4: is our data. So what does data mean? Well, let

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Speaker 4: me let me give you a little backstory. So the

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Speaker 4: Internet for the last twenty years has developed around the

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Speaker 4: idea that things should be free online. But we see

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Speaker 4: a lot of ads. So you don't pay to do

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Speaker 4: Google searches, you don't pay to watch videos on TikTok,

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Speaker 4: you don't pay to watch videos on YouTube. All those

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Speaker 4: things are free because they're funded by ads a lot

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Speaker 4: like radio and TV mostly is. But what's different between

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Speaker 4: radio and TV and the Internet is on the Internet,

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Speaker 4: there are fancy algorithms that target these ads. So when

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Speaker 4: you browse through, when you watch TikTok videos, the algorithms

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Speaker 4: are paying attention to what you're watching, what you're commenting,

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Speaker 4: what you're doing, and it uses all of that data,

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Speaker 4: the trail of your actions to find exactly the ads

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Speaker 4: that they find products that they think you're interested in

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Speaker 4: if you watch a lot of sports videos, maybe start

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Speaker 4: seeing ads for sports equipment. So they have this whole

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Speaker 4: infrastructure built up on learning as much as they can

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Speaker 4: about you and then using these algorithms to target with ads.

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Speaker 4: And now we're in the situation where they can use

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Speaker 4: very similar algorithms to instead of figure out what you

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Speaker 4: might buy, figure out how much you might buy it for.

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Speaker 4: And so your data like what you search and google,

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Speaker 4: what you watch on YouTube. Potentially, I don't think it's

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Speaker 4: happening yet, but in theory, could the conversations you have

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Speaker 4: with your AI chatbots could go into some algorithm and

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Speaker 4: end up determining how much things cost online. So it's

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Speaker 4: scary to be honest.

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Speaker 3: So you're you're.

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Speaker 2: Saying unfair, and I need a better word than that,

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Speaker 2: and I think it might have it.

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Speaker 3: I mean unfairnair.

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Speaker 2: I feel like if I were the attorney who was

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Speaker 2: suing one of the Googles, say sorry, Jeff, I would

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Speaker 2: say it's.

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Speaker 3: Just a form of discrimination.

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Speaker 4: Yep.

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Speaker 3: So here if I go to a bar.

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Speaker 2: If I go to a bar and order a white

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Speaker 2: Russian and it's nine bucks for me and twelve bucks

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Speaker 2: for the person beside me.

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Speaker 3: That's discrimination.

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Speaker 2: And I guess the discrimination would be based on how

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Speaker 2: much money they think I have. And why should I

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Speaker 2: be discriminated against because I.

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Speaker 3: Work hard or got lucky. I don't know.

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Speaker 2: I don't think so now they could as a market system,

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Speaker 2: we can charge anything we want. Yes, that you have

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Speaker 2: to charge it uniformly, like Uber currently does. If it's

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Speaker 2: surge pricing, it's surge pricing for everybody. I think there

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Speaker 2: should be anyway. But this is different because you're getting

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Speaker 2: picked out because of your income and that there.

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Speaker 4: You go, oh yeah, so let's let's untack. You said

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Speaker 4: a lot of great things. Let's untack those. So first

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Speaker 4: of all, I, personally, you know this is made just

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Speaker 4: as a human being. Agree with what you're saying, like

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Speaker 4: that sounds what you describe sounds unfair and discriminatory. But

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Speaker 4: let's step back. Is it actually illegal to discriminate in

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Speaker 4: that manner? So I didn't know this until fairly recently,

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Speaker 4: But it turns out only in certain categories. So there's

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Speaker 4: the Federal Civil Rights Act from the nineteen sixties that

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Speaker 4: makes it illegal to discriminate in certain but they're restricted.

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Speaker 4: I think it's three housing finance and medicine. I think. So, like,

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Speaker 4: you know, if you're trying to buy a house or

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Speaker 4: rent a house, you can't charge different people different rents

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Speaker 4: based on the race or gender and things like that.

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Speaker 4: But there isn't actually a law in the US that

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Speaker 4: says discrimination price discrimination just across the board is illegal.

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Speaker 4: It's only in these settings. So if you're selling laptops,

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Speaker 4: you can discriminate, this doesn't break the law. So it

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Speaker 4: gets kind of subtle with you know what we're talking about,

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Speaker 4: this online discrimination. It depends what we're talking about buying,

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Speaker 4: and it depends what kind of discrimination, what kind of

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Speaker 4: data goes into this. So in other words, it's it's

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Speaker 4: more subtle than I realized. And I was asking this

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Speaker 4: FTC lawyer and I said, oh, you know, if the

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Speaker 4: FDC says it's illegal to have the unfair price algorithms happening,

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Speaker 4: I said, well, these seem pretty unfair to me. And he's,

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Speaker 4: you know, I'm not a lawyer, I'm a math, technical guy.

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Speaker 4: He gave this long sentence that was describing how they

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Speaker 4: tend to interpret the word unfair, and I don't remember

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Speaker 4: the details, but it was it's you know, complicated, so

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Speaker 4: lawyers don't have a very clear simple definition of what

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Speaker 4: unfair is. It doesn't mean discrimination, but it certainly that's

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Speaker 4: one of the things that comes up in it.

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Speaker 3: All right, So according to your legal friend, it is uh,

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Speaker 3: not illegal.

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Speaker 4: Well so that's not what he said. He said, it's

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Speaker 4: it's not obviously unfair, but he kind of thought probably

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Speaker 4: if these things do go to court, there'll be an

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Speaker 4: argument that they're deceptive, and if the judge doesn't buy that,

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Speaker 4: then there'll be an argument that they're unfair, you know,

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Speaker 4: sort of misleading people that if I go on Amazon

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Speaker 4: and I try to buy diapers for my baby and

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Speaker 4: it says this particular diapers ten dollars for a package,

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Speaker 4: and someone else goes and they see that it's five dollars,

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Speaker 4: there's an argument, you.

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Speaker 3: Know, you don't have to believe that there's a price

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Speaker 3: is X.

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Speaker 4: Yeah, it's not X.

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Speaker 3: The price is actually for you. So the price is

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Speaker 3: not X.

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Speaker 2: Really in all cases, So that's the deception, okay, exactly.

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Speaker 4: And imagine, you know, I physically walk into a store

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Speaker 4: and I go to buy something and the shopkeeper looks

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Speaker 4: at me, kind of sizes me up and says this,

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Speaker 4: you know, this whatever piece of clothing is twenty bucks.

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Speaker 4: Then he looks at the customers to me and he says,

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Speaker 4: for you ten dollars. Yeah, that's that's what we're talking about,

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Speaker 4: just happening in a computer instead of in person. So

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Speaker 4: we don't know how the lock's going to play out.

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Speaker 4: That's that's the truth. We don't know, but these are

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Speaker 4: the ideas and the arguments that might go into it.

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Speaker 2: Yeah, we'll get into a little more of the tech.

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Speaker 2: And I love to hear if you think that's fair.

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Speaker 2: And you may think it is fair because if you

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Speaker 2: don't have a lot of money, you may be able

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Speaker 2: to pay less.

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Speaker 3: Now I don't know.

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Speaker 2: I don't know if the bar will go down for

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Speaker 2: someone with less money, or does it only go up

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Speaker 2: for some of them more money that that wouldn't help

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Speaker 2: anybody really.

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Speaker 3: But we'll discus.

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Speaker 2: I want to take a break now and we'll continue

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Speaker 2: in just a moment on WBZ.

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Speaker 1: Night Side with Dan Ray on WBZ Boston's News Radio.

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Speaker 2: Algorithmic or dynamic pricing, it's kind of I feel it's

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Speaker 2: kind of ugly. And the longer we go here, the

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Speaker 2: more details I'll give you about why. And we have

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Speaker 2: a real great guest with us talking about this. Noah's

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Speaker 2: young Sarah Cusa, Sarah Cusa and Noah. We have Dan

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Speaker 2: and Boston who's been waiting to talk with us and

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Speaker 2: weigh in on this. So Dan, welcome to Night Side.

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Speaker 4: Thank you, Bradley.

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Speaker 5: Uh well, this has kind of already been going on

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Speaker 5: a little bit. I feel like a lot of supermarkets

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Speaker 5: will do this type of thing. I'll pay a different

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Speaker 5: price for a gallon of milk depending it's not my

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Speaker 5: digital footprint, but on the zip code of the town

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Speaker 5: I buy that milk in.

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Speaker 3: It's more expensive at that store all the time, though.

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Speaker 5: Yeah, so just because it's a town of median average

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Speaker 5: income maybe more or or so, it's gonna be a

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Speaker 5: little bit more expensive for the same gallon at a

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Speaker 5: certain supermarket as it would be the same supermarket a

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Speaker 5: town over. If they're average, you know, gross incomes are

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Speaker 5: a little less, so they're kind of already gauging it

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Speaker 5: in a way.

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Speaker 3: That's right.

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Speaker 2: That's a very broad brushed, ham fisted early version of it,

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Speaker 2: I guess. But not only it's even worse when you're

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Speaker 2: in the store and the person behind you has to

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Speaker 2: pay less, and who are they to decide that I

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Speaker 2: should pay more. Who is my priorities? The money I

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Speaker 2: spend on other things is my business. If I want

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Speaker 2: to go on fancy trip or have a fancy car,

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Speaker 2: and should I have to pay more for my food

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Speaker 2: too because of that?

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Speaker 3: No, I shouldn't if I have to pay more. I

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Speaker 3: agree that if I have to pay more for my food,

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Speaker 3: then I can't afford the fancy trip and the fancy car.

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Speaker 5: Yeah, that's big brother creeping in on us. And it's

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Speaker 5: unfair that people pay different prices for the same services rendered.

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Speaker 3: I appreciate that, Dan, Thank you nobody.

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Speaker 4: And I think kind of the point, you know, dance

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Speaker 4: raising and you're you're, you know, falling up Bradley, is

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Speaker 4: we're gonna have to decide as a society what we're

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Speaker 4: okay with and what we're not. And it's not a

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Speaker 4: simple matter of just saying all of this is illegal

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Speaker 4: or all of this is not. All this is okay,

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Speaker 4: and all of it we're gonna have to go through

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Speaker 4: and say some price discriminations okay, and some is not. So.

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Speaker 4: I think dance Point is using zip codes, Okay, I

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Speaker 4: don't know. Maybe some people think it is. Maybe isn't

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Speaker 4: but knowing it's happening is a key, and that's what's

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Speaker 4: really frustrating with the algorithmic stuff. It's really hard to

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Speaker 4: know when it's happening. So when I go on Amazon,

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Speaker 4: when I go on buy my plane tickets on Delta

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Speaker 4: or wherever it is, I don't know if they're doing

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Speaker 4: this algorithm pricing, personalized pricing or not. I just see

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Speaker 4: the price that I see. At least in the grocery store.

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Speaker 4: I can drive around and say, hey, you know, milk's

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Speaker 4: a little cheaper at this one store than the other.

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Speaker 4: So part of it is a transparency issue. We don't

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Speaker 4: if we don't know what's happening, we just feel like

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Speaker 4: we're being more manipulated. And it's creepier when we are

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Speaker 4: left out of the equation, so to speak.

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Speaker 2: I myself feel like it's okay for it for one

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Speaker 2: town too, for any institution to charge what they want

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Speaker 2: if they think that their clientele will pay more. It's

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Speaker 2: the market system as long as it's for everyone that

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Speaker 2: comes in the door and that they're not changing it and.

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Speaker 4: It's sort of open right. You walk in, you see

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Speaker 4: what the price is, and as you said, everyone else

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Speaker 4: sees the same price. That's fine, But it's it's the

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Speaker 4: more that it gets secret and kind of behind closed

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Speaker 4: doors that we run into issues.

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Speaker 2: Okay, there's a logistic question, and I see how he's

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Speaker 2: easily done when you do it online because they have email,

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Speaker 2: addressent poof, they know everything about you. But when you

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Speaker 2: go to and it's brick and mortar store. So you

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Speaker 2: want to buy a tempopeeding mattress and it's you know,

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Speaker 2: five grand. That's not me, but somebody might. They don't

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Speaker 2: know who you are. They have to put the price

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Speaker 2: out there before they know who you are. So I

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Speaker 2: don't know if they can do algorithmic algorithmic pricing with

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Speaker 2: a brick and mortar store.

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Speaker 3: Can they?

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Speaker 4: Yeah? Great question. So it is more of an online thing,

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Speaker 4: but guess what, More and more of our shopping is online.

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Speaker 4: But we actually did see earlier versions that are kind

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Speaker 4: of related to Dan's calling question. When you have a

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Speaker 4: loyalty card at a grocery store, why do we all

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Speaker 4: have to get those stupid cards and scan them every

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Speaker 4: time we shop and buy stuff to get hate that?

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Speaker 3: Well, they wasn't want your infant, Why do they want

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Speaker 3: us to do that?

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Speaker 4: Want our data?

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Speaker 3: So they can use it or sell it.

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Speaker 4: Exactly, They want to know who is buying what, how

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Speaker 4: much when they want as much data as possible, and

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Speaker 4: that's how they collect and save the data to as

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Speaker 4: he's exactly said, to use or to sell the data.

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Speaker 4: Why do we agree to that because we get price discrimination,

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Speaker 4: we get discounts, you know, price discrimination in the good way.

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Speaker 4: We get lower prices if we participate in that system.

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Speaker 4: So you brought up a really good point I want

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Speaker 4: to touch on right before the break. He said, well,

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Speaker 4: hold on, price discrimination sounds bad when I'm paying higher prices,

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Speaker 4: but what about when I get lower prices? And yeah,

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Speaker 4: we may all be annoyed at these loyalty cards, but

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Speaker 4: we all do it to get a discount. So let

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Speaker 4: me give your listeners. I think, to me, what was

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Speaker 4: like a really eye opening case about this. If I

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Speaker 4: told you I'm going to start discriminating price based on

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Speaker 4: people's age, are you okay with that? You might think,

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Speaker 4: Oh no, that's gross, right, that's that sounds creepy. What

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Speaker 4: I said, Oh no, What I meant is I'm going

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Speaker 4: to give senior citizens discounts to use public transits. Well,

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Speaker 4: that sounds great. That is price discrimination based on a

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Speaker 4: personal data point based on people's age.

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Speaker 3: Oh, it's semantics. Just shows semantic's subtle and it is semantic.

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Speaker 2: And surge pricing dynamic pricing is they're they're thinking about

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Speaker 2: it for the tolling to come on roads that come

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Speaker 2: into Massachusetts. And it can be phrased different ways. It

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Speaker 2: can be phrased you're going to pay more for example

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Speaker 2: if you come or in peak hours, or they can

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Speaker 2: phrase it, hey, you want to get a break on

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Speaker 2: the price, come in off hours and you'll and you'll

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Speaker 2: get a break. But the only real way they can

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Speaker 2: do that is if if they give you a break

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Speaker 2: off peak hours, they have to raise it. I think

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Speaker 2: if they're going to keep continuing the same amount of revenue,

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Speaker 2: if revenue is happens to be the goal, they can't

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Speaker 2: really just give you a break for coming in out peak.

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Speaker 2: They're going to have to raise it during peak.

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Speaker 4: So yeah, yeah, I mean if they want to keep

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Speaker 4: their budget the same. But you know, if one goes up,

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Speaker 4: the others go down. That's right. Yeah, that's the thing

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Speaker 4: is a discount sounds great until you realize there's no

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Speaker 4: real such thing as a you know, if one price

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Speaker 4: is down, the others are going up. If everything is

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Speaker 4: going to balance out to the way they were.

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Speaker 2: Can you give us some idea of the points, the

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Speaker 2: info points, the details that an algorithm gathers on us

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Speaker 2: to Really, no, it's probably going to be a little

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Speaker 2: bit shocking when we realize.

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Speaker 4: Yeah, okay, you you earlier said, you know, it's easy

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Speaker 4: to imagine. Once they have the email, they have all

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Speaker 4: our information. But let me just spell out, and you

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Speaker 4: know I can't resist to my mask, professor. Let me

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Speaker 4: just go a little bit technical, but I promise all

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Speaker 4: your listeners will be included. This is you know, won't

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Speaker 4: be too bad. Here's the basic idea. Imagine I show

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Speaker 4: you some product, you know, let's say diapers, just to

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Speaker 4: be concrete, and I charge twenty bucks. Let's pretend I'm

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Speaker 4: on Amazon or something, and you see it and you

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Speaker 4: just scroll buy, you don't buy them, and then you

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Speaker 4: scroll by again, but now they're for fifteen dollars. Now

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Speaker 4: you scroll by again and they're ten dollars, and finally

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Speaker 4: you buy it. If I'm a smart algorithm, I'm going

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Speaker 4: to learn Okay, that's your your pain point, that's the

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Speaker 4: price that you finally are willing to buy these. I

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Speaker 4: can't do that for every single shopper online, but this

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Speaker 4: is where the data science comes in. I can look

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Speaker 4: for other shoppers that have very similar data as you.

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Speaker 4: So in the kind of traditional old school that might

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Speaker 4: have meant similar gender, age, zip code, that kind of thing,

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Speaker 4: but nowadays it could mean anything. It could mean you've

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Speaker 4: watched similar videos on TikTok, you've typed similar things into

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Speaker 4: Google or chatbots, you have similar web browsing histories, so

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Speaker 4: whatever it means. They look for people with similar data

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Speaker 4: as you, and they figure, hey, if Bradley starts buying

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Speaker 4: diapers at ten dollars, than people who look like Bradley

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Speaker 4: in a data sense are probably also going to start

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Speaker 4: buying them at ten dollars. And that's how this price

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Speaker 4: discrimination works. They look for they kind of test it

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Speaker 4: on some people, and they extrapolate it to others.

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Speaker 2: Do you think it's possible that prices get raised if

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Speaker 2: you check something online an online place more than once,

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Speaker 2: The algorithm says, ooh, this person's kind of interested in this.

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Speaker 3: Do you think they would jump it up? Or maybe

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Speaker 3: if you've checked.

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Speaker 2: Maybe if you've checked an airline price twice, as they

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Speaker 2: might say, this person really wants to go there, they're

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Speaker 2: checking it out.

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Speaker 3: You think they would jump it out or maybe they

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Speaker 3: would reduce it. I don't know.

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Speaker 4: That's here. Yeah, So you hit the nail on the

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Speaker 4: head with the very last comment there that I don't know. Here,

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Speaker 4: there's two very similar but very different questions. You're kind

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Speaker 4: of asking could they do this? One? Yes, do they

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Speaker 4: do this? We have no idea, and that's the problem.

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Speaker 4: We don't know, even the the FTC. So remember I

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Speaker 4: was meeting up this lawyer from the FTC Federal Trade Commission.

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Speaker 4: They're the ones that kind of are trying to monitor

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Speaker 4: this and sort of police it. They don't know when

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Speaker 4: companies are doing this kind of price games, algorithmic discrimination stuff.

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Speaker 4: They don't know. The best they can do is try

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Speaker 4: to read the news. I asked this lawyer, I said,

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Speaker 4: how do you know when when companies are doing this

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Speaker 4: and maybe violating this law? He said, we read the news,

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Speaker 4: we have customers file reports, we listen to the radio,

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Speaker 4: and hear what people are saying. They don't really know.

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Speaker 4: So to go back to answer your question again, absolutely,

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Speaker 4: from a technological perspective, what you described could happen. People

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Speaker 4: could come up with all kinds of strategies where if

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Speaker 4: you're you know, you keep returning to the same flight,

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Speaker 4: they say, oh, this person's really interested, but you know

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Speaker 4: they're returning, so they're they're not quite ready to pull

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Speaker 4: the trigger. Let's lower the price of it. Or maybe

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Speaker 4: the opposite, as he said, maybe like, oh they're interested,

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Speaker 4: let's raise the price because we know they're going to

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Speaker 4: take debate. Eventually, they can do all this stuff if

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Speaker 4: they want. We just don't know if and when. We

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Speaker 4: don't know who is doing this, so that's the big issue.

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Speaker 4: It's just there's so little information. There's no transparency, so

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Speaker 4: a lot of our conversation is kind of hypothetical because

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Speaker 4: we don't know what's happening.

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Speaker 2: Folks, what do you think maybe you'll get a deal

434
00:22:12,920 --> 00:22:17,520
Speaker 2: with this dynamic price if you're poor, maybe you will

435
00:22:17,520 --> 00:22:20,480
Speaker 2: get a lower price for goods if the algorithm knows this.

436
00:22:21,520 --> 00:22:23,960
Speaker 2: Bob and Medford doesn't have a whole lot of money,

437
00:22:24,280 --> 00:22:29,280
Speaker 2: we think he'll pay X minus twenty. I mean, would

438
00:22:29,320 --> 00:22:31,080
Speaker 2: you be willing to put up with the big brother

439
00:22:31,160 --> 00:22:34,800
Speaker 2: factor for perhaps a chance at a lower price, or

440
00:22:34,800 --> 00:22:37,280
Speaker 2: do you think there would be any lower pricing it

441
00:22:37,280 --> 00:22:42,520
Speaker 2: would simply be more for people who they perceive through

442
00:22:42,560 --> 00:22:45,520
Speaker 2: their algorithm to be rich and able to pay. We'll

443
00:22:45,520 --> 00:22:48,600
Speaker 2: continue with her no our guests in a moment on WBZ.

444
00:22:51,480 --> 00:22:54,919
Speaker 1: If you're on Night Side with Dan Ray on WBZ

445
00:22:55,320 --> 00:22:56,880
Speaker 1: Boston's news radio.

446
00:22:56,800 --> 00:22:59,960
Speaker 2: We continue talking about dynamic pricing and all the ramification

447
00:23:00,040 --> 00:23:02,919
Speaker 2: as we can think of that you may not have

448
00:23:03,640 --> 00:23:09,199
Speaker 2: thought of yet. So Noah, excuse me, Noah gy and

449
00:23:09,560 --> 00:23:15,120
Speaker 2: Syracusa who is a mathema mathematician and factually associated Harvard

450
00:23:15,200 --> 00:23:20,160
Speaker 2: and teaches the Bentley Professor at Bentley. I just during

451
00:23:20,160 --> 00:23:25,040
Speaker 2: the break was kind of thunderstruck by this thought that

452
00:23:25,240 --> 00:23:32,960
Speaker 2: dynamic pricing could hurt productivity in a town, state, or

453
00:23:32,960 --> 00:23:37,080
Speaker 2: even a nation. If I know that I'm going to

454
00:23:37,160 --> 00:23:40,520
Speaker 2: have to pay more for stuff, if I have more money,

455
00:23:41,760 --> 00:23:44,159
Speaker 2: that's incentive for me not to work as much or

456
00:23:44,240 --> 00:23:46,960
Speaker 2: not to work as hard. What's the point I bust

457
00:23:47,000 --> 00:23:52,920
Speaker 2: in my self to work extra hours If the algorithm

458
00:23:52,960 --> 00:23:54,520
Speaker 2: is going to see it and charge me more stuff,

459
00:23:54,680 --> 00:23:57,440
Speaker 2: I'm going to end up being able to with the

460
00:23:57,480 --> 00:24:00,720
Speaker 2: same purchasing power even though I worked harder. So why

461
00:24:00,800 --> 00:24:04,600
Speaker 2: not just quit my job and get stuff cheaper? And

462
00:24:04,640 --> 00:24:06,760
Speaker 2: that in some ways doesn't make a lot of sense,

463
00:24:06,800 --> 00:24:09,040
Speaker 2: but a lot of people are on the edge of

464
00:24:09,440 --> 00:24:10,439
Speaker 2: kind of quitting anyway.

465
00:24:10,520 --> 00:24:12,760
Speaker 3: And if you realize that.

466
00:24:13,119 --> 00:24:14,919
Speaker 2: Extra hard work is going to be seen by the

467
00:24:14,960 --> 00:24:16,960
Speaker 2: algorithm and going to charge you more, not just for

468
00:24:17,040 --> 00:24:24,840
Speaker 2: some things, but for everything, for food, drink, cars, computers, everything,

469
00:24:26,440 --> 00:24:31,280
Speaker 2: then why work twenty percent harder? If the algorithm's going

470
00:24:31,320 --> 00:24:33,720
Speaker 2: to charge you twenty percent more for everything you're you're

471
00:24:34,200 --> 00:24:36,919
Speaker 2: purchasing power would remain the same even though you worked harder.

472
00:24:37,280 --> 00:24:37,680
Speaker 3: Thoughts.

473
00:24:39,080 --> 00:24:42,119
Speaker 4: Yeah, so first of all, it's a practical advice for

474
00:24:42,200 --> 00:24:42,960
Speaker 4: your listeners.

475
00:24:43,280 --> 00:24:43,800
Speaker 5: Don't do it.

476
00:24:43,840 --> 00:24:47,400
Speaker 4: Don't quit your job, because I don't think this personalized

477
00:24:47,400 --> 00:24:49,320
Speaker 4: pricing is widespread yet. I don't.

478
00:24:49,520 --> 00:24:51,359
Speaker 3: I mean no, you know, when it happens.

479
00:24:52,080 --> 00:24:54,000
Speaker 4: We're not telling people to go ahead and do this.

480
00:24:54,119 --> 00:24:57,040
Speaker 4: But you're absolutely right hypothetically, if it were widespread and

481
00:24:57,080 --> 00:24:59,560
Speaker 4: this is how all of our retail shopping is. I

482
00:24:59,560 --> 00:25:06,480
Speaker 4: think you're actually right, you know it, just I think

483
00:25:06,480 --> 00:25:08,159
Speaker 4: you're right. I think you're hitting in a really important

484
00:25:08,200 --> 00:25:11,720
Speaker 4: point that if having more money means you're gonna have

485
00:25:11,720 --> 00:25:14,280
Speaker 4: to pay more prices for everything, not that oh, now

486
00:25:14,280 --> 00:25:17,080
Speaker 4: I can afford some luxury goods that have an inflated price,

487
00:25:17,359 --> 00:25:20,960
Speaker 4: but everything I was buying before, I'm gonna lose those

488
00:25:21,000 --> 00:25:23,880
Speaker 4: extra earnings because the price went up. That doesn't feel

489
00:25:23,920 --> 00:25:27,120
Speaker 4: like a win for anyone. So, you know, going tying

490
00:25:27,160 --> 00:25:29,879
Speaker 4: back to the our earlier part of a conversation, is

491
00:25:29,880 --> 00:25:33,640
Speaker 4: this actually illegal? One of the criteria is this unfairness.

492
00:25:33,680 --> 00:25:36,679
Speaker 4: And I think what you're hitting on is is an

493
00:25:36,800 --> 00:25:39,840
Speaker 4: argument for why maybe a court should consider this unfair,

494
00:25:40,440 --> 00:25:43,320
Speaker 4: as if it's hurting the whole economy, if this is

495
00:25:43,359 --> 00:25:46,960
Speaker 4: not really benefiting the nation, you could argue this is unfair.

496
00:25:46,960 --> 00:25:49,600
Speaker 4: It's it's making people not want to work, right, and

497
00:25:49,640 --> 00:25:51,600
Speaker 4: you go the other way. Talk what you're saying, and.

498
00:25:51,640 --> 00:25:53,240
Speaker 2: You go the other way. They're gonna cap it out.

499
00:25:53,400 --> 00:25:56,200
Speaker 2: If they're gonna do that, then maybe I'd be a

500
00:25:56,240 --> 00:26:02,160
Speaker 2: little better with it. If they would charge Jeff Bezos

501
00:26:02,400 --> 00:26:05,080
Speaker 2: one hundred dollars for a can of tuna because he's

502
00:26:05,080 --> 00:26:07,600
Speaker 2: got so much money.

503
00:26:07,640 --> 00:26:09,720
Speaker 4: Yeah, you know, and you're right right. You could go

504
00:26:09,800 --> 00:26:12,040
Speaker 4: the discount way, like, oh, I look at some product,

505
00:26:12,080 --> 00:26:13,720
Speaker 4: but I can't afford it. Oh, and then they lower

506
00:26:13,800 --> 00:26:15,520
Speaker 4: the price because I don't have as much money, So

507
00:26:15,560 --> 00:26:19,040
Speaker 4: that's great. So it does go both ways. But I

508
00:26:19,119 --> 00:26:21,359
Speaker 4: just want to step back and remind people, this is

509
00:26:21,440 --> 00:26:23,520
Speaker 4: what you're talking about, is you know, income or net

510
00:26:23,520 --> 00:26:25,560
Speaker 4: worth or whatever you want to call it. That's just

511
00:26:25,640 --> 00:26:30,120
Speaker 4: one of the potential factors. There's many other factors.

512
00:26:29,800 --> 00:26:30,119
Speaker 3: They can do.

513
00:26:30,200 --> 00:26:33,080
Speaker 2: You want to get exactly what I want to go

514
00:26:33,119 --> 00:26:35,119
Speaker 2: in next? What are the factors that we just haven't

515
00:26:35,119 --> 00:26:36,520
Speaker 2: thought of that you may have thought of?

516
00:26:37,400 --> 00:26:39,680
Speaker 4: All? Right? So one that came up. I didn't think

517
00:26:39,680 --> 00:26:41,560
Speaker 4: of this, but I read about it. It's so close enough,

518
00:26:42,320 --> 00:26:45,320
Speaker 4: is uh. There's some evidence. It's not proven, but there's

519
00:26:45,359 --> 00:26:48,359
Speaker 4: some pretty compelling evidence that some of the ride sharees.

520
00:26:48,400 --> 00:26:50,399
Speaker 4: So we don't know if it's uber Lyft or one

521
00:26:50,440 --> 00:26:54,000
Speaker 4: of their competitors, but something like that actually seem to

522
00:26:54,080 --> 00:26:55,879
Speaker 4: have used. I don't know if they still do or

523
00:26:55,920 --> 00:26:59,000
Speaker 4: if they this in the past, but some evidence that

524
00:26:59,040 --> 00:27:02,680
Speaker 4: they use batter the battery life left on your phone

525
00:27:03,160 --> 00:27:06,919
Speaker 4: for a factor. What does that mean? Imagine you're standing

526
00:27:06,920 --> 00:27:09,320
Speaker 4: on a street corner, you want to get home, it's late,

527
00:27:09,600 --> 00:27:13,040
Speaker 4: and your batteries down to five percent. You're kind of desperate, right,

528
00:27:13,520 --> 00:27:15,760
Speaker 4: you want to get home before that battery runs out.

529
00:27:15,880 --> 00:27:18,359
Speaker 4: They can see your batteries at five percent, and they

530
00:27:18,359 --> 00:27:20,960
Speaker 4: can charge you more. And it looks the data that

531
00:27:21,080 --> 00:27:24,239
Speaker 4: some researchers glected looks like that's what happens. That the

532
00:27:24,240 --> 00:27:26,760
Speaker 4: lower your battery, the more desperate you are, and the

533
00:27:26,800 --> 00:27:28,760
Speaker 4: higher prices you're going to get on your right share.

534
00:27:29,240 --> 00:27:30,919
Speaker 4: So that has nothing to do with net worth or

535
00:27:30,920 --> 00:27:33,639
Speaker 4: how much money you have. That's just taking advantage of

536
00:27:33,680 --> 00:27:34,200
Speaker 4: your phone.

537
00:27:34,320 --> 00:27:36,800
Speaker 3: That's yeah, it's.

538
00:27:36,640 --> 00:27:39,720
Speaker 4: A desperation factor. So I don't see any ethical justification

539
00:27:39,840 --> 00:27:42,119
Speaker 4: for that that it seems creepy.

540
00:27:42,840 --> 00:27:46,160
Speaker 2: Then if they would do that for a ride share,

541
00:27:47,080 --> 00:27:51,080
Speaker 2: what if they did that with medical equipment and medicine.

542
00:27:51,119 --> 00:27:54,040
Speaker 2: What if they yes, saw that you're desperate and you

543
00:27:54,119 --> 00:27:57,320
Speaker 2: have kidney disease and it's advanced.

544
00:27:57,560 --> 00:28:00,600
Speaker 4: And yeah, so we you know, how could they know that.

545
00:28:00,640 --> 00:28:03,560
Speaker 2: Advance your kidney disease is the more different desperate you are,

546
00:28:03,560 --> 00:28:04,640
Speaker 2: the more they charge you for.

547
00:28:04,600 --> 00:28:06,560
Speaker 3: The for the medication.

548
00:28:08,080 --> 00:28:10,440
Speaker 2: If you could justify the other, I suppose you could

549
00:28:10,480 --> 00:28:11,080
Speaker 2: justify that.

550
00:28:12,280 --> 00:28:14,520
Speaker 4: Yeah, so you're absolutely right, And you know, I'll be

551
00:28:14,520 --> 00:28:16,760
Speaker 4: a little speculative here. So this is kind of you know,

552
00:28:16,880 --> 00:28:19,520
Speaker 4: border on SI fi, but it's not much. So cell

553
00:28:19,560 --> 00:28:22,000
Speaker 4: phone battery life is easy, like they can get that data,

554
00:28:22,400 --> 00:28:24,359
Speaker 4: but the kinds of things you're mentioning with, you know,

555
00:28:24,440 --> 00:28:26,480
Speaker 4: medical stuff, all right, how are they going to really

556
00:28:26,480 --> 00:28:28,479
Speaker 4: get that data? Well, let me just try to make

557
00:28:28,520 --> 00:28:32,240
Speaker 4: a concrete Imagine someone's talking to their favorite AI chatbot,

558
00:28:32,320 --> 00:28:35,040
Speaker 4: you know, chat GPT or one of the others. Think

559
00:28:35,640 --> 00:28:38,120
Speaker 4: a lot of people are describing their medical symptoms and

560
00:28:38,160 --> 00:28:41,920
Speaker 4: ailments to their their chatbots. That's data. So if you're

561
00:28:42,000 --> 00:28:46,720
Speaker 4: open AI, for instance, who runs chat GPT. I'm not

562
00:28:46,720 --> 00:28:49,440
Speaker 4: saying they're using this data, but they certainly have access

563
00:28:49,480 --> 00:28:51,840
Speaker 4: to a lot of data of what people are confiding

564
00:28:51,840 --> 00:28:54,080
Speaker 4: in their chatbots. So if you say, like, oh, you know,

565
00:28:54,160 --> 00:28:56,720
Speaker 4: I have one of these kidney problems, that's it. That

566
00:28:56,840 --> 00:28:59,760
Speaker 4: data's out there if they wanted. There's no laws preventing

567
00:28:59,760 --> 00:29:02,440
Speaker 4: them from selling that data to other companies that might

568
00:29:02,480 --> 00:29:04,960
Speaker 4: want to use it. So absolutely, what you're saying that

569
00:29:05,040 --> 00:29:07,920
Speaker 4: the desperation factor that could be used in many ways.

570
00:29:08,200 --> 00:29:11,200
Speaker 4: So you know that AI is taking over, like there's

571
00:29:11,320 --> 00:29:12,800
Speaker 4: you know, the sky's the limit. They can use it

572
00:29:12,840 --> 00:29:14,239
Speaker 4: for all kinds of things if they want it.

573
00:29:14,400 --> 00:29:17,720
Speaker 2: Do you know that for a fact that rideshare companies

574
00:29:18,040 --> 00:29:21,000
Speaker 2: can see your battery life?

575
00:29:21,240 --> 00:29:23,600
Speaker 4: I think it's pretty safe to say for a fact

576
00:29:23,640 --> 00:29:26,760
Speaker 4: that they can see it. Do we know for a

577
00:29:26,800 --> 00:29:30,400
Speaker 4: fact that they're using it in their pricing that we don't.

578
00:29:30,880 --> 00:29:33,920
Speaker 4: We can't say definitively. So there's been some evidence that

579
00:29:34,000 --> 00:29:37,320
Speaker 4: strongly suggests it, but you know, to give a little

580
00:29:37,520 --> 00:29:39,360
Speaker 4: legal wiggle room, we can't say for sure.

581
00:29:39,880 --> 00:29:42,560
Speaker 2: And every if they can see the battery life, they

582
00:29:42,600 --> 00:29:43,840
Speaker 2: can see everything on your phone.

583
00:29:45,240 --> 00:29:47,880
Speaker 4: I mean, I wouldn't go I wouldn't make that extrapolation, right,

584
00:29:47,920 --> 00:29:50,920
Speaker 4: you know, battery life might be one thing that So

585
00:29:51,000 --> 00:29:54,120
Speaker 4: this comes down to the app designers. You know, when

586
00:29:54,160 --> 00:29:55,680
Speaker 4: you're using an app, it says, oh, do you grant

587
00:29:55,720 --> 00:29:58,719
Speaker 4: permission to like, I don't know, let WhatsApp see your

588
00:29:58,760 --> 00:30:02,160
Speaker 4: photo album. There's different permissions and levels when you're on

589
00:30:02,200 --> 00:30:04,600
Speaker 4: your phone. It doesn't just share everything, but I share

590
00:30:04,680 --> 00:30:08,480
Speaker 4: some things, and sometimes you're given the choice of allowing

591
00:30:08,520 --> 00:30:12,080
Speaker 4: certain permissions. I just click accepted, okay. You know, I'm

592
00:30:12,280 --> 00:30:14,280
Speaker 4: I study the stuff, and I still just clicked up

593
00:30:14,360 --> 00:30:17,240
Speaker 4: accepting okay. Because I want my apps to run. I

594
00:30:17,240 --> 00:30:20,160
Speaker 4: don't really pay attention to what data it is giving, okay,

595
00:30:20,160 --> 00:30:22,200
Speaker 4: But I have to admit that's dangerous, right, I'm giving

596
00:30:22,280 --> 00:30:24,720
Speaker 4: all the way all that data giving access, So I

597
00:30:24,720 --> 00:30:26,760
Speaker 4: don't know how long it lasts. What if I click

598
00:30:26,800 --> 00:30:30,280
Speaker 4: accept and then one year later it turns out that

599
00:30:30,320 --> 00:30:32,560
Speaker 4: what I accepted is is feeding a bunch of data

600
00:30:32,600 --> 00:30:35,400
Speaker 4: to some price discrimination algorithm. You know, it's impossible to

601
00:30:35,480 --> 00:30:36,240
Speaker 4: keep track of the stuff.

602
00:30:36,240 --> 00:30:39,640
Speaker 2: Well, I'm sure that if you accept it that it's

603
00:30:39,720 --> 00:30:43,240
Speaker 2: will well stay accepted until you get rid of the app.

604
00:30:43,280 --> 00:30:45,680
Speaker 2: And who knows, maybe even after you get rid of

605
00:30:45,680 --> 00:30:47,720
Speaker 2: the app, maybe they stick something in your phone even

606
00:30:47,720 --> 00:30:48,320
Speaker 2: if you get rid.

607
00:30:48,280 --> 00:30:48,760
Speaker 3: Of the app.

608
00:30:49,440 --> 00:30:50,080
Speaker 4: Yeah, we don't know.

609
00:30:50,280 --> 00:30:51,920
Speaker 3: Okay, one quick question. I don't know if you know

610
00:30:52,000 --> 00:30:55,320
Speaker 3: the answer to this. It can.

611
00:30:56,320 --> 00:31:00,000
Speaker 2: Actors see what's on your desktops? You have all kinds

612
00:30:59,960 --> 00:31:04,400
Speaker 2: of documents. Maybe you pay your taxes, Uh, most people

613
00:31:04,400 --> 00:31:07,680
Speaker 2: pay their taxes online now and maybe you keep the

614
00:31:07,720 --> 00:31:09,240
Speaker 2: receipt and a folder and your documents.

615
00:31:09,280 --> 00:31:11,680
Speaker 3: Can they see all that that?

616
00:31:11,880 --> 00:31:14,080
Speaker 4: So I'm just gonna speculate here because you're right, I

617
00:31:14,080 --> 00:31:16,240
Speaker 4: don't know for sure, but I would say I doubt

618
00:31:16,800 --> 00:31:19,720
Speaker 4: you know anyone that you're interacting with has access and

619
00:31:19,720 --> 00:31:23,040
Speaker 4: can see that. But I would suspect, you know, let's

620
00:31:23,040 --> 00:31:26,680
Speaker 4: say I'm on a Windows computer, which is by Microsoft.

621
00:31:27,520 --> 00:31:29,840
Speaker 4: Everything nowadays is in the cloud. I would suspect that

622
00:31:29,920 --> 00:31:33,320
Speaker 4: Microsoft would have access to that data. But if I

623
00:31:33,360 --> 00:31:35,720
Speaker 4: go to buy some things on Amazon, I would suspect

624
00:31:35,720 --> 00:31:37,800
Speaker 4: that Amazon does not have that kind of data. So

625
00:31:38,000 --> 00:31:39,240
Speaker 4: it depends unless.

626
00:31:38,960 --> 00:31:40,480
Speaker 3: They bought it from Microsoft.

627
00:31:41,280 --> 00:31:43,000
Speaker 4: Yeah, and then then we don't know, you know, whose

628
00:31:43,080 --> 00:31:45,040
Speaker 4: data is sold ware and where it travels.

629
00:31:45,120 --> 00:31:50,760
Speaker 2: That's right, So on the dark web, you can buy data.

630
00:31:50,800 --> 00:31:53,040
Speaker 2: Companies can buy your data on the dark Web.

631
00:31:53,320 --> 00:31:55,400
Speaker 4: I wouldn't know. I've never been there, but I know

632
00:31:55,480 --> 00:31:56,080
Speaker 4: that from fact.

633
00:31:56,760 --> 00:32:00,880
Speaker 2: That's where That's where nefarious actors go. That's where hackers go.

634
00:32:01,200 --> 00:32:03,720
Speaker 2: They want to get your you know, your money out

635
00:32:03,760 --> 00:32:06,400
Speaker 2: of your bank account. They can go there and buy

636
00:32:07,000 --> 00:32:10,800
Speaker 2: big swaths of information. How did that get there, Well,

637
00:32:11,120 --> 00:32:13,800
Speaker 2: somebody's probably sold it to somebody who put.

638
00:32:13,640 --> 00:32:14,120
Speaker 3: It up there.

639
00:32:14,800 --> 00:32:16,240
Speaker 4: A little bit of a don't ask, don't tell.

640
00:32:16,840 --> 00:32:18,680
Speaker 2: Just a few more minutes to go in this hour

641
00:32:18,760 --> 00:32:22,320
Speaker 2: with our guests Noah talking about dynamic pricing and the

642
00:32:22,400 --> 00:32:27,720
Speaker 2: various extrapolations ramifications of that here on WBZ.

643
00:32:28,760 --> 00:32:33,400
Speaker 1: It's Night Side with Dan Ray on wb Boston's News Radio.

644
00:32:34,320 --> 00:32:37,560
Speaker 2: We return with Noah gn Siracusa and talk a little

645
00:32:37,560 --> 00:32:44,160
Speaker 2: bit more about algorithm pricing, So algorithms, let me back up,

646
00:32:44,600 --> 00:32:52,920
Speaker 2: it's illegal to conspire to price fix m I believe. However,

647
00:32:53,640 --> 00:32:59,520
Speaker 2: I believe it's possible for algorithms to do that without

648
00:32:59,560 --> 00:33:03,560
Speaker 2: actually any contact. They can they can be programmed to

649
00:33:03,600 --> 00:33:06,320
Speaker 2: do that on their own. Does that make sense? And

650
00:33:06,400 --> 00:33:10,640
Speaker 2: that would be a problem with dynamic pricing. You couldn't

651
00:33:10,720 --> 00:33:12,520
Speaker 2: program the algorithm.

652
00:33:12,240 --> 00:33:12,920
Speaker 3: To do that.

653
00:33:14,480 --> 00:33:17,040
Speaker 4: Look, yeah, let's let's go through that slowly because there's

654
00:33:17,040 --> 00:33:20,560
Speaker 4: a lot to digest there. So if let's say we're

655
00:33:20,600 --> 00:33:22,880
Speaker 4: in some market where there's a handful of companies like

656
00:33:23,160 --> 00:33:28,120
Speaker 4: airlines and Delta Continental, few other if all the leading airlines,

657
00:33:28,280 --> 00:33:30,600
Speaker 4: the CEO sit in the room and they said, what

658
00:33:30,640 --> 00:33:33,880
Speaker 4: if we all raise our price ten percent, where no

659
00:33:33,880 --> 00:33:36,080
Speaker 4: one's going to win or lose any customers, but we're

660
00:33:36,120 --> 00:33:38,920
Speaker 4: all going to make more money. Let's do a twenty percent.

661
00:33:39,360 --> 00:33:42,920
Speaker 4: There's nothing you know, they would all win there, they're

662
00:33:42,920 --> 00:33:45,560
Speaker 4: not undercutting each other. They would all just make more

663
00:33:45,560 --> 00:33:48,200
Speaker 4: money and all of us customers would lose. So that

664
00:33:48,360 --> 00:33:51,960
Speaker 4: is called collusion, and that is illegal. So you can't

665
00:33:52,240 --> 00:33:54,800
Speaker 4: just get in a backroom and negotiate with your competitors

666
00:33:54,800 --> 00:33:58,760
Speaker 4: to all raise your prices. But exactly if you're saying,

667
00:33:58,800 --> 00:34:01,360
Speaker 4: what if I's an algorithm that just says, look at

668
00:34:01,360 --> 00:34:04,760
Speaker 4: my peers and let me just match the price, but

669
00:34:04,840 --> 00:34:07,320
Speaker 4: raise it by a few dollars. What if they run

670
00:34:07,320 --> 00:34:10,440
Speaker 4: the same algorithm. Now, my competitor is going to look

671
00:34:10,440 --> 00:34:12,160
Speaker 4: at my prices and raise it by few dollars, and

672
00:34:12,200 --> 00:34:14,200
Speaker 4: my algorithms gonn look at their prices and raise it

673
00:34:14,200 --> 00:34:16,680
Speaker 4: by few dollars. We could all end up just raising

674
00:34:16,680 --> 00:34:20,120
Speaker 4: our prices and raising our prices. So the algorithms might

675
00:34:20,320 --> 00:34:23,839
Speaker 4: intentionally collude, or they might even just accidentally collude. They're

676
00:34:23,840 --> 00:34:27,200
Speaker 4: just running some math formulas, so the collusion could happen

677
00:34:27,719 --> 00:34:31,360
Speaker 4: just baked into the algorithm. And it's not entirely clear

678
00:34:31,960 --> 00:34:36,120
Speaker 4: if that's the same illegality as the humans in the

679
00:34:36,160 --> 00:34:40,879
Speaker 4: back room smoking the cigars, shaking hands. So I've asked

680
00:34:40,920 --> 00:34:43,000
Speaker 4: someone about this, and the answer I got it was

681
00:34:43,800 --> 00:34:47,640
Speaker 4: it's probably illegal as algorithms do it, but intent really

682
00:34:47,640 --> 00:34:51,560
Speaker 4: matters in the law, and computers don't have intent.

683
00:34:51,680 --> 00:34:53,319
Speaker 3: Right the programmers program it.

684
00:34:54,480 --> 00:34:56,840
Speaker 4: Programmers may not have the intent. They might just be

685
00:34:56,960 --> 00:35:01,000
Speaker 4: programming an algorithm to do something. They might not intending

686
00:35:01,120 --> 00:35:06,040
Speaker 4: to collude, but what they program may result in collusion. Okay,

687
00:35:06,040 --> 00:35:08,080
Speaker 4: So that's where it gets tricky. So there was a

688
00:35:08,120 --> 00:35:11,640
Speaker 4: proposed law recently I forget which state or which office

689
00:35:11,920 --> 00:35:15,480
Speaker 4: that was basically not saying that algorithmic collusion is legal,

690
00:35:15,680 --> 00:35:18,040
Speaker 4: but saying, let's just go ahead and make a loss,

691
00:35:18,080 --> 00:35:22,200
Speaker 4: spelling out explicitly that if algorithms collude in any capacity,

692
00:35:22,239 --> 00:35:24,919
Speaker 4: that's illegal. So that we don't have that law yet.

693
00:35:24,960 --> 00:35:28,239
Speaker 4: But this is just showing there is some interest in

694
00:35:28,280 --> 00:35:31,839
Speaker 4: Congress in making sure collusion does not happen. But right

695
00:35:31,880 --> 00:35:35,360
Speaker 4: now it's not clear exactly what's legal, and it's not

696
00:35:35,480 --> 00:35:37,360
Speaker 4: when computers do it versus humans.

697
00:35:37,480 --> 00:35:41,480
Speaker 2: Okay, So if you cannot program them to collude, or

698
00:35:41,520 --> 00:35:45,240
Speaker 2: you can't that means you cannot not program them to collude,

699
00:35:45,320 --> 00:35:48,560
Speaker 2: so you can't prevent that from happening, then maybe it

700
00:35:48,680 --> 00:35:52,360
Speaker 2: just should be all illegal if it's going to potentially

701
00:35:52,400 --> 00:35:56,560
Speaker 2: result in collusion. Another thing is price gouging depending on

702
00:35:56,640 --> 00:36:01,920
Speaker 2: an event, And I mean at a say, a grocery store.

703
00:36:02,320 --> 00:36:06,400
Speaker 2: You know how when snowstorm comes, people flock to the

704
00:36:06,440 --> 00:36:07,200
Speaker 2: grocery store.

705
00:36:07,560 --> 00:36:10,680
Speaker 3: Now in you have paper.

706
00:36:11,920 --> 00:36:15,520
Speaker 2: Labels, paper price tags on everything, the staff can't go

707
00:36:15,640 --> 00:36:16,839
Speaker 2: around and reprice everything.

708
00:36:16,880 --> 00:36:18,320
Speaker 3: It would take a month.

709
00:36:19,160 --> 00:36:26,000
Speaker 2: But with this dynamic pricing or or just digital, this

710
00:36:26,080 --> 00:36:29,600
Speaker 2: isn't really this isn't really the same exact algorithmically operated thing.

711
00:36:29,719 --> 00:36:33,640
Speaker 2: But if your your market goes to digital pricing, they

712
00:36:34,000 --> 00:36:37,560
Speaker 2: jump everything up by ten with the push of a button.

713
00:36:38,000 --> 00:36:42,719
Speaker 4: And yeah, you're absolutely right. There are a lot of

714
00:36:42,719 --> 00:36:45,400
Speaker 4: price a lot of grocery stores now that are moving

715
00:36:45,400 --> 00:36:48,240
Speaker 4: in or experienting with as exactly as you said, digital label.

716
00:36:48,320 --> 00:36:50,000
Speaker 4: So what that means, it's just a match in a

717
00:36:50,000 --> 00:36:54,640
Speaker 4: little display that's you know, like a calculator kind of display,

718
00:36:54,719 --> 00:36:56,680
Speaker 4: so it shows the numbers and they can just click

719
00:36:56,719 --> 00:36:59,920
Speaker 4: a button or type in centrally to update the numbers

720
00:37:00,160 --> 00:37:03,719
Speaker 4: the prices. And what grocery stores are saying is this

721
00:37:03,840 --> 00:37:06,520
Speaker 4: is just an efficiency thing. If we want to, you know,

722
00:37:06,560 --> 00:37:09,759
Speaker 4: lower the cost of bananas instead of having to have

723
00:37:09,800 --> 00:37:12,080
Speaker 4: a work or go and change all the prices manually,

724
00:37:12,320 --> 00:37:13,960
Speaker 4: it's just easier, you know. It's just a little bit

725
00:37:13,960 --> 00:37:16,719
Speaker 4: of automation. So that's how they present it. It's just

726
00:37:16,719 --> 00:37:18,319
Speaker 4: we're going to save time, we're going to make it

727
00:37:18,360 --> 00:37:21,400
Speaker 4: more efficient. Yeah, but you're pointing out.

728
00:37:21,360 --> 00:37:24,440
Speaker 2: They'll say that they're going to pass the savings onto us.

729
00:37:24,280 --> 00:37:27,880
Speaker 4: That's great, that's what they'll say. But exactly as you said,

730
00:37:28,200 --> 00:37:30,440
Speaker 4: it also makes it very easy for them to just

731
00:37:30,560 --> 00:37:33,040
Speaker 4: change the prices in a split second in whatever way

732
00:37:33,040 --> 00:37:36,560
Speaker 4: they want. So suddenly, you know the bananas are almost out.

733
00:37:36,640 --> 00:37:39,480
Speaker 4: They could raise the prices of banana and people might say, hey,

734
00:37:39,600 --> 00:37:42,000
Speaker 4: I want bananas. There's only a few left. I'm willing

735
00:37:42,000 --> 00:37:43,960
Speaker 4: to pay more to get that left banana. And if

736
00:37:44,000 --> 00:37:46,680
Speaker 4: there's a snowstorm, they raise the price on the salt.

737
00:37:46,880 --> 00:37:49,279
Speaker 4: You know, they can do these things and there's nothing

738
00:37:49,360 --> 00:37:49,960
Speaker 4: stopping them.

739
00:37:50,040 --> 00:37:53,600
Speaker 2: Since they have automated inventory, they could set it to

740
00:37:53,840 --> 00:37:57,520
Speaker 2: automatically raise the price the fewer bananas they have. Well,

741
00:37:57,600 --> 00:38:00,560
Speaker 2: we got one less banana, we raise. The price goes

742
00:38:00,640 --> 00:38:03,800
Speaker 2: up incrementally with each item sold.

743
00:38:03,960 --> 00:38:05,520
Speaker 3: One quick thing. We don't have what. We don't have

744
00:38:05,600 --> 00:38:06,000
Speaker 3: much time.

745
00:38:06,000 --> 00:38:11,120
Speaker 2: But you gotta wonder why this is not an industry

746
00:38:11,600 --> 00:38:14,880
Speaker 2: to help beat the system. Get like to have an

747
00:38:14,920 --> 00:38:19,600
Speaker 2: alternative identity or to mask what you do. It would

748
00:38:19,600 --> 00:38:21,960
Speaker 2: be great if I go and buy something with a

749
00:38:22,040 --> 00:38:23,640
Speaker 2: completely different identity.

750
00:38:23,880 --> 00:38:27,640
Speaker 3: I would I wouldn't be able to use it at

751
00:38:27,640 --> 00:38:31,560
Speaker 3: the for legal stuff. But it's got to be a

752
00:38:31,600 --> 00:38:34,480
Speaker 3: way for me to mask who I am. Like a scramble.

753
00:38:37,239 --> 00:38:42,120
Speaker 4: I think you're I think you're predicting. I think you're

754
00:38:42,120 --> 00:38:44,480
Speaker 4: predicting the future. I think we don't have that industry

755
00:38:44,520 --> 00:38:48,680
Speaker 4: because this algorithm pricing stuff is not as widespread yet.

756
00:38:49,520 --> 00:38:51,719
Speaker 4: But I think as as it gets to be more widespread,

757
00:38:51,760 --> 00:38:54,000
Speaker 4: if we do allow it, and it does, I think

758
00:38:54,120 --> 00:38:58,120
Speaker 4: the industry you're predicting is gonna arise. Probably just you

759
00:38:58,160 --> 00:39:01,000
Speaker 4: download an app that will try to, you know, mess

760
00:39:01,080 --> 00:39:03,600
Speaker 4: around with your data until it finds some configuration that

761
00:39:03,640 --> 00:39:08,160
Speaker 4: gives you the lower prices. I wonder if fire, why not.

762
00:39:08,360 --> 00:39:10,560
Speaker 2: If you have a VPN, you could take one factor

763
00:39:10,600 --> 00:39:12,520
Speaker 2: out of there and they won't know that you're from

764
00:39:12,520 --> 00:39:15,320
Speaker 2: a rich community. If you have a virtual person on network,

765
00:39:16,120 --> 00:39:19,279
Speaker 2: you could even choose. You could choose a server in

766
00:39:19,320 --> 00:39:22,640
Speaker 2: a poor community or a poor country. Even you can

767
00:39:22,719 --> 00:39:26,239
Speaker 2: say that you choose a server in Romania. But then,

768
00:39:26,239 --> 00:39:28,000
Speaker 2: of course, if you're gonna get it delivered, you gotta

769
00:39:28,040 --> 00:39:30,040
Speaker 2: have the you gotta give them your actual address.

770
00:39:30,360 --> 00:39:33,640
Speaker 3: At times, you know, I wish we had more. We'll

771
00:39:33,640 --> 00:39:34,680
Speaker 3: have to talk again soon.

772
00:39:36,360 --> 00:39:37,239
Speaker 4: Thanks for having me, of.

773
00:39:37,280 --> 00:39:41,120
Speaker 2: Course, No and Sarah, who's a really fun and excellent guest.

774
00:39:41,360 --> 00:39:44,480
Speaker 2: Thanks for coming and hanging around with us on WBZ

775
00:39:45,480 --> 00:39:49,480
Speaker 2: Take care open lines. Now we're gonna take a break.

776
00:39:49,560 --> 00:39:53,840
Speaker 2: I'm gonna get a I don't know, ginger ale. Maybe

777
00:39:54,400 --> 00:39:57,680
Speaker 2: maybe I'll go hardcore and go Coca Cola some water.

778
00:39:58,080 --> 00:39:59,879
Speaker 2: You could do the same, or you could just hang

779
00:40:00,560 --> 00:40:03,160
Speaker 2: and we'll have some fun with the open lines. I'll

780
00:40:03,160 --> 00:40:05,680
Speaker 2: have some questions for you, but really it's a great

781
00:40:05,680 --> 00:40:06,680
Speaker 2: time to just chat.

782
00:40:07,320 --> 00:40:09,480
Speaker 3: The pressure of the holidays.

783
00:40:08,960 --> 00:40:14,480
Speaker 2: Is over, New Year upon us, and it's fun to

784
00:40:14,680 --> 00:40:20,440
Speaker 2: just exercise, hang with the community, the w BSY community,

785
00:40:20,480 --> 00:40:24,200
Speaker 2: the number six one, seven, two, five, four, ten thirty

786
00:40:24,400 --> 00:40:26,680
Speaker 2: and look forward to your call and chatting with you.

787
00:40:26,680 --> 00:40:29,160
Speaker 3: You can refer to anything we've talked about.

788
00:40:29,000 --> 00:40:34,040
Speaker 2: Including the US action in Venezuela or anything else we've

789
00:40:34,040 --> 00:40:34,600
Speaker 2: talked about.

790
00:40:34,760 --> 00:40:36,120
Speaker 3: Ever, it's WBZ