The Ranking Game

The only guy who seems to have escaped the rankings game is Adam. He got into the record books without trying: Before him there was nobody. Eve had to settle for runner-up, and look what happened when she tried to get ahead by snacking on a piece of fruit…”

I am reading about what people wrote about the ranking game.  Stephen Joel Trachtenberg is president emeritus  of George Washington University published in 2011 a witty article .

“The ancient Greeks picked up the game, fashioning bits of gold, silver and bronze to represent win, place and show. Earning an Olympic medal meant, and still means, you are the best of the best, the top dog in your chosen category of competition. It is absolute and objective, not relative and subjective. The best sprinter gets the gold because she is fast, not because she is popular…”

Thank you, Prof. Trachtenberg!

Emerging Europe and Central Asia University Rankings

Here is the new QS World University Rankings (thanks for Gyuri Bazsa for writing me).

if you click to Methodology, you see again the magic numbers  and categories.

Academic reputation (30%)

Employer reputation (20%)

Faculty/student ratio (15%)

Papers per faculty (10%)

Web impact (10%)

Staff with a PhD (5%)

Citations per paper (5%)

International faculty (2.5%) and international students (2.5%).

Lomonosov is still the first. Written on ~ November 7th, 2017.

A new model for efficient ranking in networks

Caterina De BaccoDaniel B. LarremoreCristopher Moore  published a new algorithm with the title A physical model for efficient ranking in networks.  The model is based on binary interactions among the entities. As often in  physical models, interactions via edges are considered as mechanical springs, and the optimal rankings of the nodes are minimizes the total energy (or “energy”) of the system. They show some examples for identifying  prestige, dominance, and social hierarchies in human and animal communities.

Further studies will tell how efficient is the new algorithm.



Rank reversal

Algorithms are maybe objective. A famous example now known how PageRank gives different results by changing the numerical value of what is cold the ”damping factor”. PageRank is based on an assumption how a web-surfer behaves. For a while the surfer will click to links she is seeing in a certain page, but get bored with the actual page she visits, and then jump to another page randomly (as with directly typing in a new URL rather than following a link on the current page). The original algorithm assumed that the probability of being bored is 0.15, so the numerical value of the damping factor was set as 1-0.15=0.85. So, setting the damping factor for other numbers we may get different ranking. The phenomenon is called rank reversal. Rank reversal is a change in the rank ordering depending on some not important, or many times irrelevant factors. While I find the paper of Seung-Woo Son, Claire Christensen, Peter Grassberger, Maya Paczuski PageRank and rank-reversal dependence on the damping factor  excellent, my opinion does not count much, during almost six years its citation number is just six.

On a somewhat different note it is reasonable to expect that the ranking of any two candidates, A and B, should be preserved even if one more candidate C enters the race. In the theory of election systems it is called the ”rank reversal rule”. This rule was infamously violated in the US election in 2000, when Ralph Nader captured a few per cent of the vote in Florida, giving the election to George W. Bush (over Al Gore). As all we know Gore would have won if Nader was not in the race.

A plan to rate and rank citizens and legal persons

(Thanks to János Tóth).

Big data meets Big Brother as China moves to rate its citizens

,,,Imagine a world where many of your daily activities were constantly monitored and evaluated: what you buy at the shops and online; where you are at any given time; who your friends are and how you interact with them; how many hours you spend watching content or playing video games; and what bills and taxes you pay (or not). It’s not hard to picture, because most of that already happens, thanks to all those data-collecting behemoths like Google, Facebook and Instagram or health-tracking apps such as Fitbit. But now imagine a system where all these behaviours are rated as either positive or negative and distilled into a single number, according to rules set by the government. That would create your Citizen Score and it would tell everyone whether or not you were trustworthy. Plus, your rating would be publicly ranked against that of the entire population and used to determine your eligibility for a mortgage or a job, where your children can go to school – or even just your chances of getting a date….:

Read the whole article! Comments are welcome!