Tag: economy

# Two Tinder Experiments: An Unequal Economy

I’ve seen a fair share of Tinder experiments come by, for instance, someone A/B-testing attractiveness with and without facial hair, but these new two posts on Medium are the best I’ve come across so far.

In his first experiment, this self-proclaimed worst online dater went catfishing. He made a Tinder account using stock photos of attractive and less attractive and old and young guys, looking and sampled some like ratio’s.

Basically, his conclusion was that “Tinder actually can work, but pretty much only if you are an attractive guy”

In the second experiment, the author decided to treat Tinder as an economy and study it as an (socio-)economist would:

The wealth of an economy is quantified in terms its currency. […] In Tinder the currency is “likes”. […] Wealth in Tinder is not distributed equally. Attractive guys have more wealth in the Tinder economy (get more “likes”) than unattractive guys do. […] An unequal wealth distribution is to be expected, but there is a more interesting question: What is the degree of this unequal wealth distribution and how does this inequality compare to other economies?

Original Medium Post by Worst Online Dater

The author notes some caveats of this analysis. First and foremost, the data was collected in quite an unethical way, by asking questions to 27 of the matches with the fake accounts the author set up. Moreover, self-report bias is quite likely, as it’s easy to lie on Tinder. Still, the results are quite amusing:

Basically, “the bottom 80% of men are fighting over the bottom 22% of women and the top 78% of women are fighting over the top 20% of men”

The Lorenz curve shows the proportion of wealth owned by the bottom x% of a population. If wealth was equally distributed the curve would be perfectly diagonal (a 45 degree slope). The steeper the slope, the less inequal an economy. The below shows the curve for a perfectly equal economy, the US economy, and the estimated Tinder economy:

Similarly, the Gini coefficient can be used to represent the wealth equality of an economy. It ranges from 0 to 1, where 0 corresponds with perfect equality (everybody has the same wealth) and 1 corresponds with perfect inequality (one dictator with all the wealth). While most European countries, and even the US, score quite low on this Gini index, the Tinder economy is estimated to be much more towards the lower end.

Finally, based on the collected data, the author was able to reduce Tinder Male Attractiveness to a function of the number of likes received:

According to my last post, the most attractive men will be liked by only approximately 20% of all the females on Tinder. […] Unfortunately, this percentage decreases rapidly as you go down the attractiveness scale. According to this analysis a man of average attractiveness can only expect to be liked by slightly less than 1% of females (0.87%). This equates to 1 “like” for every 115 females.

The good news is that if you are only getting liked by a few girls on Tinder you shouldn’t take it personally. You aren’t necessarily unattractive. You can be of above average attractiveness and still only get liked by a few percent of women on Tinder. The bad news is that if you aren’t in the very upper echelons of Tinder wealth (i.e. attractiveness) you aren’t likely to have much success using Tinder. You would probably be better off just going to a bar or joining some coed recreational sports team.

Original Medium Post by Worst Online Dater

# Learn from the Pros: How media companies visualize data

Past months, multiple companies shared their approaches to data visualization and their lessons learned.

Click the companies in the list below to jump to their respective section

## Financial Times

The Financial Times (FT) released a searchable database of the many data visualizations they produced over the years. Some lovely examples include:

## BBC

The BBC released a free cookbook for data visualization using R programming. Here is the associated Medium post announcing the book.

The BBC data team developed an R package (`bbplot`) which makes the process of creating publication-ready graphics in their in-house style using R’s ggplot2 library a more reproducible process, as well as making it easier for people new to R to create graphics.

Apart from sharing several best practices related to data visualization, they walk you through the steps and R code to create graphs such as the below:

## Economist

The data team at the Economist also felt a need to share their lessons learned via Medium. They show some of their most misleading, confusing, and failing graphics of the past years, and share the following mistakes and their remedies:

• Truncating the scale (image #1 below)
• Forcing a relationship by cherry-picking scales
• Choosing the wrong visualisation method (image #2 below)
• Taking the “mind-stretch” a little too far (image #3 below)
• Confusing use of colour (image #4 below)
• Including too much detail
• Lots of data, not enough space

Moreover, they share the data behind these failing and repaired data visualizations:

## FiveThirtyEight

I could not resist including this (older) overview of the 52 best charts FiveThirtyEight claimed they made.

All 538’s data visualizations are just stunningly beautiful and often very
ingenious, using new chart formats to display complex patterns. Moreover, the range of topics they cover is huge. Anything ranging from their traditional background — politics — to great cover stories on sumo wrestling and pricy wine.

# Cryptocurrency and Blockchain explained by 3Blue1Brown

Grant Sanderson is the owner of YouTube channel 3Blue1Brown, which aims to explain math and stats concepts in an entertaining way. Using animations, Grant grasps difficult problems and explains them in understandable language. I was already familiar with the great explanatory videos on Linear Algebra and Neural Networks, but this new video on cryptocurrencies and blockchain (below) is definitely one of the best explanations of Bitcoin I’ve seen so far: