🕵️‍♂️ How Google's PageRank algorithm works

The PageRank algorithm gives each page a rating of its
importance, which is a recursively defined measure of importance, based on if important pages link to it.
It's recursive because the importance of a page refers back to the importance of other pages that link to it
Here's how it works in practice:
1⃣ We start with some pages and crawl them for links
2⃣ Each page has 1/N points (where N as the total number of pages)
3⃣ Add points to each page for the amount of links to it, divided by the number
of links emanating from the sources of these links
4⃣ If a page has no links redistribute its points equally among all the other pages
🔁 Repeat until the page points stabilise (what really happens repeating is that at each repetition there's a damping of the redistribution, but that's not easy to understand or explain 😅 but you can imagine it as a "decay" of points being redistributed)

More from Tech

There has been a lot of discussion about negative emissions technologies (NETs) lately. While we need to be skeptical of assumed planetary-scale engineering and wary of moral hazard, we also need much greater RD&D funding to keep our options open. A quick thread: 1/10

Energy system models love NETs, particularly for very rapid mitigation scenarios like 1.5C (where the alternative is zero global emissions by 2040)! More problematically, they also like tons of NETs in 2C scenarios where NETs are less essential.
https://t.co/M3ACyD4cv7 2/10


In model world the math is simple: very rapid mitigation is expensive today, particularly once you get outside the power sector, and technological advancement may make later NETs cheaper than near-term mitigation after a point. 3/10

This is, of course, problematic if the aim is to ensure that particular targets (such as well-below 2C) are met; betting that a "backstop" technology that does not exist today at any meaningful scale will save the day is a hell of a moral hazard. 4/10

Many models go completely overboard with CCS, seeing a future resurgence of coal and a large part of global primary energy occurring with carbon capture. For example, here is what the MESSAGE SSP2-1.9 scenario shows: 5/10
What an amazing presentation! Loved how @ravidharamshi77 brilliantly started off with global macros & capital markets, and then gradually migrated to Indian equities, summing up his thesis for a bull market case!

@MadhusudanKela @VQIndia @sameervq

My key learnings: ⬇️⬇️⬇️


First, the BEAR case:

1. Bitcoin has surpassed all the bubbles of the last 45 years in extent that includes Gold, Nikkei, dotcom bubble.

2. Cyclically adjusted PE ratio for S&P 500 almost at 1929 (The Great Depression) peaks, at highest levels except the dotcom crisis in 2000.

3. World market cap to GDP ratio presently at 124% vs last 5 years average of 92% & last 10 years average of 85%.
US market cap to GDP nearing 200%.

4. Bitcoin (as an asset class) has moved to the 3rd place in terms of price gains in preceding 3 years before peak (900%); 1st was Tulip bubble in 17th century (rising 2200%).

You May Also Like