People Analytics - A Long and Complicated History / Humans + Tech - #46

+ Automated human-less retail stores


I hope you had a productive week :) … Let’s get to the articles for this week.

🎩 Hat tip to Kartik for sending me some of the links featured in this newsletter.

The long, complicated history of “people analytics”

Our eternal quest to understand ourselves and our nature has led us to exploit our fellow humans long before modern technology came into the picture. In this article [MIT Technology Reivew], Christine Rosen talks about Jill Lepore’s book, If Then, that tells the story of Simulmatics Corporation, founded in 1959, which Lepore describes as a “Cold War America’s Cambridge Analytica.”

But that past can offer some much-needed guidance and humility. Despite faster computers and more sophisticated algorithms, today’s “people analytics” is fueled by an age-old reductive conceit: the notion that human nature in all its complexities can be reduced to a formula. We know enough about human behavior to exploit each other’s weaknesses, but not enough to significantly change it, except perhaps on the margins.


The company’s name, a portmanteau of “simulation” and “automatic,” was a measure of its creators’ ambition: “to automate the simulation of human behavior.” Its main tool was the People Machine, which Lepore describes as “a computer program designed to predict and manipulate human behavior, all sorts of human behavior, from buying a dishwasher to countering an insurgency to casting a vote.” It worked by developing categories of people (such as white working-class Catholic or suburban Republican mother) and simulating their likely decision-making. (Targeted advertising and political campaigning today use broadly similar techniques.)

Humans never deal well with uncertainty. And human nature is very unpredictable and quite random. But studying people in masses or in groups is much more predictable than trying to predict the actions of a single human. This is what makes Facebook, Google, Amazon, and all the other tech companies that track, monitor, and run analytics on all their users so dangerous – they hold enormous power in swaying opinions through the ability to sell this power to the highest bidder via their ad targeting networks. After Simulmatics helped John F. Kennedy win the presidential elections in 1960, one scientist said, “You can’t simulate the consequences of simulation.”

Half a century later, we are seeing Facebook and other social networks have a similar influence on elections around the world through people analytics that are many hundreds or thousands of times more powerful than those that Simulmatics had. And we still can’t simulate the consequences of simulation. The only thing we can see so far is that this is harming human society severely.

As Lepore notes:

The study of human behavior is not the same as the study of the spread of viruses and the density of clouds and the movement of the stars. Human behavior does not follow laws like the law of gravity, and to believe that it does is to take an oath to a new religion. Predestination can be a dangerous gospel. The profit-motivated collection and use of data about human behavior, unregulated by any governmental body, has wreaked havoc on human societies, especially on the spheres in which Simulmatics engaged: politics, advertising, journalism, counterinsurgency, and race relations.

Often, in moments of frustration, I too wish there was a formula to explain the reason for everything. But when I think with a cooler head, I realise that if everything was predictable, life would be completely uninteresting. In issue #24 of this newsletter on predicting the future with AI, I posted a quote from one of my favourite philosophers, Alan Watts. Here it is again:

“A completely predictable future is already the past. You’ve had it! That’s not what you want. You want a surprise.”

—Alan Watts, from “What do you desire?” [YouTube]

Unfortunately, those seeking power and wealth do not think in this way.

Automated human-less retail stores

We’ve covered Amazon Go, a completely automated grocery store, in previous issues. Similar models are appearing in different parts of the world. In Sweden, Lifvs has opened 19 stores that are made out of containers and are completely automated [The Guardian]. They serve rural areas and provide the most essential items for the small populations in these areas.

With only a small camera in one corner to supervise her, she studies fridges stocked by someone she’s never seen, selects the bottle she wants, calls up a barcode reader on the app, then scans and pays with another tap. This new unstaffed supermarket – a wall of fridges and another of shelves packed into a 22 sq metre container – has made it a lot more convenient to live in Eket, a village of 400 people in the far-north of Sweden’s Skåne region.

“It’s a bit weird,” Nilsson admits. “It’s strange to have a shop with no one to say ‘hi’ to. But if this is the only alternative for maintaining some kind of life in a small village like this, then it’s a really nice thing.”


Christian Larsson, the local mayor, is considering a similar unmanned solution for local libraries. “This kind of thing is happening all over Sweden right now. For small villages, if you don’t want everybody to leave, this is the future.”

In France, a similar startup called Storelift is dropping container stores called boxy, in neighbourhoods with a lack of good shopping options [VentureBeat]. Like Amazon Go, sensors, data, and AI track what a customer has lifted from the shelves and put in their baskets.

To shop at Boxy, a customer downloads the Boxy app on their smartphone and then scans the QR code to enter the store. When customers take a product, it is identified through the computer vision algorithms and the weight sensors on the shelves. When the customer leaves, they scan their QR code again and checkout happens automatically.

The key to improving efficiency is the data the company is collecting as people shop. The shelves that detect weight, along with computer vision cameras, know when a product is picked up, looked at, and then either put in the basket or back on the shelf. In some cases, because the company knows a customer is possibly interested in a product, they can offer a discount on the spot to nudge them along.

I am not comfortable with all the data these stores are collecting on people. But it’s positive that they are making life for people in underserved communities better by providing them with easy access to food and essentials.

Quote of the week

The profit-motivated collection and use of data about human behavior, unregulated by any governmental body, has wreaked havoc on human societies, especially on the spheres in which Simulmatics engaged: politics, advertising, journalism, counterinsurgency, and race relations.

—Jill Lepore, The long, complicated history of “people analytics” [MIT Technology Review]

I wish you a brilliant day ahead :)