This link has been bookmarked by 95 people . It was first bookmarked on 19 Oct 2007, by lucidus.
-
08 Nov 14
-
27 Sep 11
-
18 Mar 09
-
15 Feb 09
Suvi KorhonenEhdotetaan, että osa ihmisistä ei hyväksyisi evoluutiota, koska se tuntuu niin intuitionvastaiselta, ainakin kreationistisen maailmankuvan vinkkelistä. "Eihän se voi toimia". Vastaavia reaktioita aikaansaavina esimerkkeinä Wikipedia, prediction markets &
intuitio intuition creationism kreationismi wisdomofcrowds crowds crowdsourcing recommendation prediction datamining wisdom statistics wikipedia analysis collaboration atheism evolution religion economics psychology for:matrixx2.01
-
30 Apr 08
-
23 Apr 08
-
07 Apr 08
-
28 Mar 08
-
15 Feb 08
-
07 Jan 08
-
02 Nov 07
-
31 Oct 07
-
30 Oct 07
-
28 Oct 07
-
27 Oct 07
-
26 Oct 07
-
25 Oct 07
-
Most people who actually use the Wikipedia online encyclopedia on a regular basis recognize that it is an amazing resource, and is getting significantly better as time goes on.
-
Wikipedia has 10 times the amount of content as Britannica, is growing much more rapidly, and, most importantly, is being refined and improved every minute of every day. (not to mention, it is available online for free!)
-
Wikipedia founder Jimmy Wales described the online encyclopedia as being "like a sausage: you might like the taste of it, but you don't necessarily want to see how it's made."
-
Prediction Markets:
-
One of the purest examples of "wisdom of crowds" is prediction markets, where speculators can bet on the chances of future news events, such as the outcomes of sports events or political elections.
-
This isn't the percentage of people who are expected to vote for each candidate (as polls try to predict), but the actual percentage chance of winning -- a very different thing.
-
In effect, it tries to take into account everything that may factor in -- things that polls alone can't reach.
-
For instance, if I think Clinton has a greater than 46% chance of winning, I can buy a "contract" on her for $46. It will pay $100 if she wins, $0 if she loses.
-
It should not be surprising to hear that a great many people, when told of how prediction markets work, will claim that they can never produce meaningful results. After all, the market price, and therefore the prediction, comes solely from random people on the internet who decide to take a wild guess at who is likely to win.
-
Prediction markets turn out to be remarkably accurate, typically more accurate than any individual expert can predict, as non-intuitive as it may seem.
-
Meanwhile, those experts who consistently predict badly will tend to eventually pick another line of work which they are better at, while those who are best at picking will make lots of money doing so, and will therefore tend to be there with cash in hand whenever the prices stray far from their predictions.
-
Each expert tends to gravitate toward the specific things that they might have special expertise (or inside information!) on and therefore has the best chance of out-predicting the other experts. Over time, it becomes harder and harder to consistently outguess the market, no matter how good you are.
-
It is only when you step back far enough to see things from a statistical point of view does the true precision of the process come into view.
-
Recommendation systems:
-
This is a form of machine-learning known as collaborative filtering.
-
Last year, Netflix launched a contest, where they offered a million dollars to someone who could write software that does the job better than Netflix's own "world-class movie recommendation system." Specifically, the winner has to beat Netflix by 10 percent.
-
A year later, contestants are getting rather close to the million dollar prize, with about 8.5 percent improvement.
-
What interested me, though, was how steadfast these people were in declaring that the this information was so critical to being able to make sense of all the data and do reasonable predictions.
-
I debated with a few of them, and found it impossible to convince them that such data was completely unnecessary, and that the purely numerical data supplied in the original dataset was quite enough to very accurately categorize movies, detect the tastes of users, and predict their ratings on the additional set of movies.
-
To do this, my program starts by giving each user, and each movie, a random position in space. That is, each gets a value for X, Y and Z representing its position. For each of the 100 million ratings, the program simply adjusts the distances between each item: if a user likes a movie, it moves the user and movie closer to each other by a tiny amount. If the user dislikes a movie, it moves the user and movie further away from one another. The program iterates over and over, until the positions stabilize: that is, an equilibrium is reached. This takes quite a few hours, but once it has done it, small changes (such as modifying the data, or modifying a parameter within the program) take very few iterations to re-stabilize the model.
-
If a movie is near a user -- in the same neighborhood, so to speak -- it can be predicted that that user will probably like that movie, even if the user did not specifically rate it. Movies that are universally liked tended to move toward the center of the model ("Shawshank Redemption" being closest to center), disliked movies moved toward the outside.
-
I could type in the names of two movies, and ask "how similar" they are, and the results were almost always exactly what I would expect. I could type the name of a movie, and get a list, in order, of the top 20 movies that are seen as most similar.
-
Without the idea of divine creation (and its immense cultural support), people would have no choice but to look to evolution to explain the living things around us. Even if mentally challenging, it would be hard not to accept, given the lack of alternative explanations for life.
-
-
24 Oct 07
-
23 Oct 07
-
22 Oct 07
-
-
Indeed, the problem most people have with Wikipedia's quality and accuracy seems to have more to do with their knowledge of how it is made, rather than any observed problem with the end results.
-
The reason that Wikipedia is as good as it is (and the reason that living organisms are as sophisticated as they are), is not due to the average quality of the edits (or mutations). Instead, it is due to a much harder to observe process: selection.
-
Prediction markets turn out to be remarkably accurate, typically more accurate than any individual expert can predict, as non-intuitive as it may seem.
-
-
21 Oct 07
bigfacewormInteresting comparison between "wisdom of crowds" and evolution.
-
20 Oct 07
Michel Bauwenshow many potential editors looked at an article, subconsciously thought "I doubt I could improve this much," and chose not to try. Each of these can be considered a "selection event", and the number of such events vastly outnumbers the actual edits. Selec
Prediction-Markets Wikipedia Wisdom-of-Crowds P2P-Science P2P
-
annestI have long been fascinated with systems that tap into the "wisdom of crowds" -- systems that, in fact, have much in common with Darwinian evolution. Such systems doubtfully conflict with anyone's religion, and yet, I see the same sort of resistance to th
-
-
One of the purest examples of "wisdom of crowds" is prediction markets, where speculators can bet on the chances of future news events, such as the outcomes of sports events or political elections. For instance, at intrade.com
-
-
19 Oct 07
-
Brian WestI have long been fascinated with systems that tap into the "wisdom of crowds" -- systems that, in fact, have much in common with Darwinian evolution. Such systems doubtfully conflict with anyone's religion, and yet, I see the same sort of resistance to th
-
Comparing it to evolution, an edit of Wikipedia might be considered equivalent to a genetic mutation. A mutation, of course, is non-directed...that is, "random." It could be bad or good, but most of the time it is bad. If we were simply the average of all
atheism charles_darwin education evolution programming religion science statistics
Would you like to comment?
Join Diigo for a free account, or sign in if you are already a member.