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10 days ago
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DuckDuckGo is good enough for regular use

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Google recently launched a desktop redesign. The favicon and URL breadcrumbs were turned into a header for organic search results. Ads had the same design, but were identified using the string “Ad” instead of the favicon. This design wasn’t new. Google’s mobile web search has served this design since May 2019. But users and regulators complained that the desktop version blurred the distinction between ads and organic results. Google reverted the change a few weeks later, citing the backlash.

I experienced change aversion when I tried the redesign. Change aversion is a simple idea: users react negatively to new experiences, but they stop caring as new experiences become normal. Anyways, looking at the Google redesign gave me change aversion. I knew that I wouldn’t care about it within a few days. But I decided to put it to good use: I would try DuckDuckGo. If it was time for Google to experiment, then it was time for me to experiment. I had wanted to try it for a while. This finally gave me the activation energy to switch.

DuckDuckGo’s premise is simple. They do not collect or share personal information. They log searches, but they promise that these logs are not linked to personally identifiable information. Their search engine results seemingly come from Bing, but they claim to have their own crawler and hundreds of other sources on top of that. They do customize the results a little: geo-searches like bars near my location give me results from my home city of New York. But search results aren’t personalized. I’ve always wondered how good the results would be.

Anyways, here are the guidelines that I set for my experiment:

  • I would switch all of my browser’s default search engines to DuckDuckGo across all of my devices.
  • I would use DuckDuckGo for at least a month. This would give me enough time to learn some of its strengths and weaknesses.
  • I would not use any DuckDuckGo poweruser features unless I could guess that they existed. I wanted to understand the out-of-the-box experience on the site.
  • I could use the !g operator to search Google if DuckDuckGo failed. Some will point out that this violates the previous rule. But as soon as a discussion changes to DuckDuckGo usage, people can’t WAIT to talk about how often they use !g or g!. Do you need an example? I discussed it in this paragraph and tried to blame it on other people. I’m serious: people can’t talk about DuckDuckGo without talking about !g. It’s the law. So I know about it and I will use it.

I haven’t tried a new search engine since I tried Bing in 2009. It was time to find out how good DuckDuckGo is in 2020. What was the biggest difference that I found?

Google is the king of low-intent searches

Google has a structured understanding of many domains. This is a difficult moat for other search engines to cross. This is evident when comparing low-intent searches. These are searches with an ambiguous purpose. The subject is broad and it’s not clear what the user wanted. The user might not even “want” anything except to kill five minutes before a meeting.

Let’s try a low-intent search. Type harry potter into Google. In response, Google throws everything at the wall to see what sticks. In addition to the organic links, Google serves me:

  • A panel on the right with a ton of metadata. This includes oddly-specific structured data like “Sport: Quidditch”.
  • A list of five of the seven books in the series.
  • Fantasy books from five related searches.
  • A news panel containing three articles about Harry Potter actors.
  • The harry potter Google Maps search, centered on the New York area.
  • A “People also ask” panel with four questions.
  • A link to three Harry Potter-related YouTube videos.
  • Three recent tweets from @HarryPotterFilm.
  • A panel with 7 “Fantasy book series” results.
  • A panel with 7 “Kids book series” results.
  • 8 other search strings related to harry potter.

This makes sense: what did I want when I searched for harry potter? Google can’t know. So Google returns information from many domains to attempt to satisfy the query. Google returns so much information that something will be close enough. This is a huge competitive advantage. They can serve good results for bad searches by covering as many domains as possible.

This is a departure from how search used to work. When I was in grade school, I was taught how to craft search queries. Someone herded us into a library and explained how to pick effective keywords, quote text, use operators like AND or OR, etc. These days are dead. None of this matters on Google. If you want to know showtimes for “Harry Potter and the Cursed Child,” a search for harry potter will get you close enough.

In comparison, DuckDuckGo’s results for harry potter are relaxing. It serves a small knowledge panel to the right and three recent news articles at the top, some organic links, and nothing else. It’s much easier to scan this page. It’s a more relaxed vibe. But if I actually wanted something, it likely wouldn’t be on this page. You can make the argument that I got what I deserved: I didn’t clearly communicate what I wanted, and therefore I didn’t get it. But Google has trained everyone that broad queries are effective. It feels like magic. It’s not. It’s the result of years of developing a structured understanding of the world and crafting ways to surface the structure. And it’s something that potential competitors will need to come to terms with.

I don’t personally miss most of Google’s result panels. Especially the panels that highlight information snippets. It’s easy to find these. Searching microsoft word justify text provides me a snippet from Microsoft’s Office’s support page explaining what to click or type to justify text. I’ve learned not to trust information in these panels without reading the source they came from. Google seems to cite this information uncritically. I’ve found enough oversimplified knowledge panel answers that I’ve stopped reading most of them. Recently, I was chatting with a Googler who works on these. I asked them if I was wrong to feel this way. And they replied, “I trust them, but I’ve read enough bug reports and user feedback that I don’t blame you.” So my position is wrong, but not very wrong. I’ll take that.

Some of Google’s panels are great. I miss them. I haven’t found anything better than Google’s stock panel for quickly looking at after-hours stock movements. Searching Google for goog stock will show you this panel. I miss you buddy. I hope you’re doing well.

Ultimately, it stresses me out when Google returns many panels in a search. I’m sure that each is a marginal gain for Google. But I don’t like how Google feels as a result. I’m continually glad to see just 10 links on DuckDuckGo, even if this means that I’m not getting what I wanted. This has been training me to craft more specific searches.

DuckDuckGo is good enough

Let’s move away from Google’s competitive advantages. How does DuckDuckGo perform for most of my search traffic? DuckDuckGo does a good job. I haven’t found a reason to switch back to Google.

I combed through my browser’s history of DuckDuckGo searches. I compared it to my Google search history. When I fell back to Google, I often didn’t find what I wanted on Google either.

Most of my searches relate to my job, which means that most of my searches are technical queries. DuckDuckGo serves good results for my searches. I’ll admit that I’m a paranoid searcher: I reformat error strings, remove identifiers that are unique to my code, and remove quotes before searching. I’m not sure how well DuckDuckGo would handle copy/pasted error strings with lots of quotes and unique identifiers. This means that I don’t know if DuckDuckGo handles all technical searches well. But it does a good job for me.

There are many domains where Google outperforms DuckDuckGo. Product search and local search are some examples. I recently made a window plug. It was much easier to find which big-box hardware stores had the materials I need with Google. I also recently bought a pair of ANC headphones. I got much better comparison information starting at Google. Google also shines with sparse results like rare programming error messages. If you’re a programmer, you know what I’m talking about: imagine a Google search page with three results. One is a page in Chinese that has the English error string, one is a forum post that gives you the first hint that you need to solve the problem, and one is the error string in the original source code in Github. DuckDuckGo often returns nothing for these kinds of searches.

Even though Google is better for some specific domains, I am confident that DuckDuckGo can find what I need. When it doesn’t, Google often doesn’t help either.

Sample of times when both Google and DuckDuckGo failed me

  • I tried to write a protobuf compiler plugin using the official PHP protocol buffer bindings. I now believe that writing a protobuf compiler plugin in PHP is impossible due to several arbitrary facts, but I needed to piece this information together myself. My searches sprawled over Google and DuckDuckGo across several days before I concluded that it could not be done and that I could not find a workaround. This isn’t DuckDuckGo or Google’s fault. Some things just don’t have answers online.
  • I often fell back to Google for gif searches. It turns out that I’m bad at finding gifs. Sometimes I get exactly what I want, like searching for gritty turning around. But I had a lot of trouble finding a string that gave me this. Eventually I found it by remembering a Twitter user that had posted it and scanning their “Media” posts.
  • Trying to find a very specific CS:GO clip that I had seen on Reddit years ago. I found it via a combination of Reddit search and skimming the bottom of Reddit threads for video links.
  • What is australian licorice? Is it a marketing gimmick? Stores sell it. It’s tasty. But I can’t find an explanation anywhere.

If you’re thinking of switching to DuckDuckGo because of the Google redesign, I’ll save you the trouble: DuckDuckGo’s inline ads are formatted similarly to the Google redesign that got reverted. If anything, DuckDuckGo’s ads are harder to spot because DuckDuckGo’s (Ad) icon is on the right, while Google’s was on the left where my eyes naturally skim.

It turns out that I care about privacy, but I still use Google Analytics on my blog. I haven’t been thinking about digital privacy for long enough to have a consistent and principled opinion. Sorry about that.

Let’s go back to the original selling point of DuckDuckGo: they don’t track you.

I have been reading my DuckDuckGo searches in my browser history for this post. It’s wonderful that all of these searches remained private. Some of them should remain private for stupid reasons. I don’t want anyone to know that I searched for what is the value of a human life because it makes me sound like a killer robot. Other searches are much more sensitive. One is the name of a medication I’m on. Others are searches about pains and fears that I have. DuckDuckGo allows me to perform these searches without building a profile of me. I’m sure that advertisers pick up the scent as soon as I click a link. But I appreciate the delay. I didn’t think about the traces I left online when I searched on Google. But now that I know I have the choice, I’m actively comforted by reviewing my DuckDuckGo search history and reading everything that they didn’t track.

I also noticed that many searches show trends. I knew that this was true in theory. But it’s different when you see it in your own search results. A month ago, many of my searches related to vacation planning. But now they don’t anymore. The coronavirus scrapped my plans. But there are many life events that could have also caused this: health reasons, family problems, etc. These are things that ad networks could piece together as I visit sites. It’s possible to imagine even darker versions of this – imagine the months of searches that relate to a pregnancy with a miscarriage. Many companies could profit from a couple going through that process, if they showed the right ads in the right places at the right time. There is a lot of trend information that you just want to keep to yourself.

What happens moving forward?

I will continue using DuckDuckGo. I don’t see a reason to switch back to Google. I’m going to continue to fall back using !g when I need to. I’m going to try to avoid talking about the fallback (but let’s be honest, I just did it again).

I still use lots of Google products. I’m not in the process of porting away from any of them. I still use Chrome in addition to Firefox and mobile Safari. Google Docs still holds a place in my heart. Etsy is hosted on GCP and uses Google Apps. Google Photos is still the best place for me to store and share my photos.

I liked the exercise of reading a month of my search history. You should do it, too. It became clear that I broadcast lots of information by having these very personal conversations with search engines. I’d like to understand more about the digital traces I leave online.

I don’t want to turn into a digital hermit. But I would like to become more deliberate about the traces that I leave around the internet. Even as a developer, I’m not sure what will happen if I disable third-party cookies across the internet. But I’d like to start reading more about digital privacy to understand what tradeoffs I am making.

Disclaimer: I worked at Google from 2010-2015, but did not work on search.

The post DuckDuckGo is good enough for regular use appeared first on www.bitlog.com.

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12 days ago
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Outbreak — Melting Asphalt

by Kevin Simler
March 16, 2020
Harry Stevens

at The Washington Post recently published a


elegant simulation of how a disease like COVID-19 spreads. If you haven't already, I highly recommend

checking it out


Today I want to follow up with something I've been working on: playable simulations of a disease outbreak. "Playable" means you'll get to tweak parameters (like transmission and mortality rates) and watch how the epidemic unfolds.

By the end of this article, I hope you'll have a better understanding — perhaps better intuition — for what it takes to contain this thing. But first!...


This is not an attempt to model COVID-19.

What follows is a simplified model of a disease process. The goal is to learn how epidemics unfold in general.


: I'm not an epidemiologist! I defer to infectious disease experts (and so should you). I have almost certainly made mistakes in this article, but I'll correct them as quickly as I can. If you see any problems, please

get in touch



Let's do this.

A grid of people

We're going to build our model up slowly, one piece at a time.

The first thing a disease needs is a population, i.e., the set of people who can potentially catch the disease. Ours will live in neat rows and columns, like the 9x9 grid you see here:

Each square represents a single person. The poor soul at the center, as you may have guessed, is Infected. Meanwhile, everyone else is Susceptible.


Now let's incorporate time into our model.

The "Step" button (below) moves the simulation forward 1 day per click. Or you can press the ▷ button to watch things happen on their own:

Oh no. It looks like everyone sneezed on their immediate neighbors — north, east, south, west — and the whole world got sick.


But people don't stay sick forever. Let's see what happens when they get better after 2 steps (i.e., 2 days):

Great, now people can transition from Infected to Recovered.

Here's a handy legend:

  •  Susceptible
  •  Infected
  •  Recovered

For purposes of our simulation, once someone is Recovered, they can't get reinfected. This is hopefully (and probably) true for COVID-19, but not certain.

Incubation period

In discussions of COVID-19, you may have heard that the disease has a long incubation period. This is the time between when a person initially contracts the disease and the onset of first symptoms.

With COVID-19, it seems that patients are contagious during the incubation period. They may not even realize they're sick, but they're still able to infect others.

We will replicate this feature in our disease model. (But remember, we're not trying to model COVID-19 precisely!)

Here's what an incubation period looks like:

The way I've chosen to model this disease, there's no important distinction between the pink and red states. As far as the virus is concerned, both states behave the same.

Nevertheless, I wanted to include the incubation period as a (visual) reminder that carriers of COVID-19 are lurking among us, hidden from the official statistics, totally unaware that they're infected.

... unaware that they're spreading the disease to others.

Even as you read this, you may be such a person.

  •  Susceptible
  •  Infected (incubation period, no symptoms)
  •  Infected (with symptoms)
  •  Recovered

Probabilistic infection

OK, enough.

Real diseases don't spread outward with 100 percent certainty. They spread probabilistically.

So let's introduce a new parameter: the transmission rate. This controls the chance that an infection gets passed from person to person.

Can you find a value for the transmission rate that keeps the disease from spreading to the entire population?

👆 Pro-tip: You can adjust sliders while the simulation is running.

Q: What's the largest transmission rate where the disease doesn't seem capable of spreading forever (e.g., reaching all four edges of the grid)?

In my experiments, it seems to be around 0.35, maybe 0.34. Below that, I've seen the infection fizzle out every time. Above, it generally infects most of the grid.

Here's how transmission works in our disease model.

Every day, each person has a fixed number of encounters with the people nearby.

Thus far, we've allowed people to interact only with their immediate neighbors, for a total of 4 encounters per day. We'll vary these assumptions below.

During each encounter, the transmission rate determines the probability that an Infected person will give the disease to a Susceptible person. The higher the transmission rate, the more likely the disease gets passed along.

In reality, there are many different types of encounters. You might brush past someone on the sidewalk. Or sit next to them on a bus. Perhaps you'll share an ice cream cone. Each of these encounters would result in a different probability of transmitting the infection. But in our model, for simplicity, all encounters share the same transmission rate.


As you continue playing with these simulations (above and below) and thinking about their relevance to coronavirus/COVID-19, here's something to keep in mind:

Transmission rate is partly a function of the disease itself (how naturally infectious it is), but also a function of the environment that the disease lives in. This includes both the physical environment (e.g., air temperature and humidity) as well as the social environment (e.g., people's behaviors).

For example, when people wash their hands and wear masks to contain coughs, the transmission rate per encounter goes down — even if the virus itself doesn't change.

Now, for any viral-growth process, it's possible to find a transmission rate low enough to completely stop the spread. This is called the "critical threshold," and you can learn more about it



But COVID-19 is so infectious, it's hard to get below the critical transmission rate. We can only wash our hands so many times a day. Even wearing masks out in public won't be enough enough to bring transmission down far enough (though every inch is helpful).

We could all wear hazmat suits every time we leave the house; technically that would solve the transmission problem (without changing our patterns of social interaction). But since that's, uh, impractical, let's consider other ways to keep this disease from consuming us.


Here's another unrealistic assumption we've been making: we've been allowing people to interact only with their immediate neighbors.

What happens when we let people travel farther afield? (We're still assuming 4 encounters per day, a parameter we'll expose in the next section.)

As you pull the travel radius slider below, you'll see a sample of the encounters that the center person will have on any given day. (We can't draw everyone's encounters because it would get too crowded. You'll just have to use your imagination.) Note that in our model, unlike in real life, each day brings a new (random) set of encounters.

Note that if you restrict travel from the beginning (e.g., to a radius of 2 units), you can slow the infection down a great deal.

But what happens when you start with unrestricted travel, let the infection spread pretty much everywhere, and only restrict travel later?

In other words, how early in the infection curve do you have to curtail travel in order for it to meaningful slow the outbreak?

Go ahead, try it. Start with a travel radius of 25. Then play the simulation, pausing when you get to about 10 percent infected. Then reduce the travel radius to 2 and play it out. What happens?

Takeaway: travel restrictions are most useful when they're applied early, at least for the purpose of flattening the curve. (So let's get them in place!)

But travel restrictions can help even in the later stages of an outbreak, for at least two reasons:

  1. Buses, trains, and airports are places where people gather together in cramped quarters. When people stop using these modes of transport, they reduce the number of encounters they have with potentially infected people. (We'll explore this more below.)
  2. Reducing travel is critical in concert with regional containment measures. If one region gets the outbreak under control, but neighboring regions are still on fire, you have to protect the controlled region. (We're not going to explore containment measures in this article, but they may be important soon, and if you're interested, you might start here.)

Number of encounters

Alright, let's really open this thing up.

In the simulation below, you can vary the encounters per day.

Let's start at 20. What's the minimum value we need to keep the outbreak contained?

As you can see, reducing encounters per day has a dramatic effect on the outbreak. It easily flattens the curve, and even has the potential (when taken very seriously) to completely quench an outbreak.

This is the effect we're hoping for when we call for "social distance." This is why so many people are pleading with their officials to stop the parades and close the schools, and why all of us should stay away from bars and coffee shops and restaurants, and work from home as much as possible.

The NBA did their fans a tremendous service by canceling the rest of the season. Now we need to follow suit and cancel everything.

In my understanding (again, not an expert), this is the single most important lever we have for fighting this thing.


Not every patient recovers from a disease. Many end up Dead.

Enter the fatality rate.

In our simulation, fatality rate is the probability that a patient who gets infected will ultimately die of the infection, assuming they get normal/adequate medical care.

(Update: an earlier version of this article made a distinction between case fatality rate and mortality rate, but failed to define the terms correctly. Collapsing this distinction and using the term "fatality rate" instead.)

The fatality rate for COVID-19 has been estimated between 1 percent and

6 percent

. It might turn out to be lower than 1 percent, if there are a lot of undiagnosed cases. It's definitely higher when the medical system is overburdened (more on that in a minute).

We'll start at a 3 percent fatality rate for our disease model, but you can vary the parameter below:

Those scattered black dots may not look like much. But remember, each is a human life lost to the disease.

Hospital capacity

Below you'll find one last new slider. It controls hospital capacity.

This is the number of patients (expressed as a percentage of the population) that can be treated by our medical system at any one time.

Why does hospital capacity matter?

When there are more patients than the system can handle, they can’t get the treatment they need. And as a result, they have significantly worse outcomes. As we've seen in Italy, some may be left to die in the hallways.

I've heard people speak of hospital capacity as the “number of beds,” or “number of ICU beds.” My take is that mere "beds" can be set up in a gymnasium on very short notice. I think the real bottleneck is medical equipment — specifically ventilators. But I'm not sure. Maybe it’s medical personnel.

In reality, this matters a lot. We need to identify what the bottleneck is, and do our best to alleviate pressure there. But for a simulation, we can just wave our hands and assume there's limited capacity somewhere in the system. Remember, we're not trying to model reality too carefully.

In our disease model, here's how the medical system breaks:

When there are more infections than hospital capacity, the fatality rate doubles.

Give it a try. Pay special attention to the input fatality rate (the value on the slider), which defines how often people die even in the best circumstances, vs. the actual death rate (highlighted below the chart), which tells us how the system behaves under strain.

"Flatten the curve"

You've heard this before. You know why it's important. But now you're about to get a feel for it.

This is your final test today.

The input fatality rate is fixed at 3 percent. Hospital capacity is fixed at 5 percent.

Play out the simulation and note the actual death rate: 6 percent. Then try to bring that number down.

In other words, flatten the curve:

However this worked out for you in simulation, reality is going to be so much harder. Real people don't respond like sliders in a UI.

And here's the kicker: even if we manage to "flatten the curve" enough to meaningfully space out the case load, we're still positioned to lose millions and millions of lives.

Maybe we won't lose as many as a worst-case scenario; maybe we won't lose them in hospital hallways. But as long as the virus continues to spread — which it shows every sign of doing — there's an unthinkable amount of suffering in our future.

Unless we do the right things today.

Stop traveling. Stop going out. Stop visiting your parents and your friends. Stop eating at restaurants. Pause everything you possibly can. If you're in charge of things, cancel them. Lock. It. All. Down.

Please: take decisive action now.

COVID-19 is coming for us, and it won't be stopped by half-measures.




— no rights reserved. You're free to use this work however you see fit, including copying it, modifying it, and distributing it on your own site.

Full model

The full model, with all sliders exposed, can be found at the very bottom of the page.


Further reading

  • Coronavirus: Why You Must Act Now — Tomas Pueyo explains why we've been systematically underestimating this thing, and why that needs to change. Just read it.
  • Don’t "Flatten the Curve," Stop It! — Joscha Bach does some calculations on hospital capacity and concludes that "flattening the curve" won't be enough; we have to completely stop the outbreak.
  • The Washington Post's excellent simulation — brilliant use of billiard balls to show transmission and social distancing.
  • Going Critical — my previous exploration of diffusion and viral growth processes, including the nuclear reactions and the growth of knowledge.

Originally published March 16, 2020.

(Thanks to

Jason Legate

for suggesting and coding this addition to the disease model.)

In the simulation below, you can vary the self-quarantine rate, i.e., the chance that a patient will choose to isolate themselves once they become symptomatic. Patients who become Self-quarantined will be drawn in blue instead of red.

Additionally, you can vary how strict they are with the self-quarantine strictness parameters. At 100 percent strictness, patients who are isolating themselves have 0 encounters with other people. At 0 percent strictness, they have their normal number of encounters. And it varies linearly in between.

Let's start the self-quarantine rate at 25 percent and the strictness also at 25 percent. What does it take to keep the outbreak contained?

Self-quarantine strictness     25%

As you can see, if people voluntarily self-quarantine (once they show symptoms) and are strict about isolating themselves, the spread can be mitigated. Unfortunately, because patients are contagious during the incubation period (before they have a chance to notice their own symptoms), it's hard to stop the spread entirely.

For most diseases, self-quarantine won't solve the problem on its own. Rather, it's one tool among many (including better hygiene, social distances, travel restrictions, etc.) that all together can bring an outbreak under control. A big lesson here is that every strategy complements every other strategy.

Full model

Self-quarantine strictness     90%

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12 days ago
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Louisville, KY
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[4k, 60 fps] A Trip Through New York City in 1911


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Louisville, KY
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Further Research is Needed

3 Comments and 6 Shares
Further research is needed to fully understand how we managed to do such a good job.
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47 days ago
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3 public comments
46 days ago
So you’re saying you want the trump administration to start writing “scientific” papers...
West Hartford, CT
48 days ago
That's the researcher *mic drop*
48 days ago
Further research is needed to fully understand how we managed to do such a good job.

Google Maps Hacks


" 99 second hand smartphones are transported in a handcart to generate virtual traffic jam in Google Maps.Through this activity, it is possible to turn a green street red which has an impact in the physical world by navigating cars on another route to avoid being stuck in traffic. " #googlemapshacks The advent of Google’s Geo Tools began in 2005 with Maps and Earth, followed by Street View in 2007. They have since become enormously more technologically advanced. Google’s virtual maps have little in common with classical analogue maps. The most significant difference is that Google’s maps are interactive  – scrollable, searchable and zoomable. Google’s map service has fundamentally changed our understanding of what a map is, how we interact with maps, their technological limitations, and how they look aesthetically. In this fashion, Google Maps makes virtual changes to the real city. Applications such as ›Airbnb‹ and ›Carsharing‹ have an immense impact on cities: on their housing market and mobility culture, for instance. There is also a major impact on how we find a romantic partner, thanks to dating platforms such as ›Tinder‹, and on our self-quantifying behaviour, thanks to the ›nike‹ jogging app. Or map-based food delivery-app like ›deliveroo‹ or ›foodora‹. All of these apps function via interfaces with Google Maps and create new forms of digital capitalism and commodification. Without these maps, car sharing systems, new taxi apps, bike rental systems and online transport agency services such as ›Uber‹ would be unthinkable. An additional mapping market is provided by self-driving cars; again, Google has already established a position for itself. With its Geo Tools, Google has created a platform that allows users and businesses to interact with maps in a novel way. This means that questions relating to power in the discourse of cartography have to be reformulated. But what is the relationship between the art of enabling and techniques of supervision, control and regulation in Google’s maps? Do these maps function as dispositive nets that determine the behaviour, opinions and images of living beings, exercising power and controlling knowledge? Maps, which themselves are the product of a combination of states of knowledge and states of power, have an inscribed power dispositive. Google’s simulation-based map and world models determine the actuality and perception of physical spaces and the development of action models. text by Moritz Alhert - The Power of Virtual Maps
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