The last of these articles is of particular interest. It explicates my earlier hunch that the Wisdom of Crowds phenomenon has to do with something like the law of large numbers:
Surowiecki’s archetypal example comes from a 1906 county fair where 800 people participated in a contest to guess what the weight of an ox would be after it was butchered. The average guess was 1,197 pounds. The actual weight turned out to be 1,198 pounds. On its face, this seems like a dramatic testament to the ideals of democracy, but the accuracy of the average guess has much more to do with the nature of the problem than with the wisdom of the crowd.
Their task was clearly-defined and required no special information. Each person was free to guess any weight they wanted, but the higher or lower their guess, the more obviously wrong it would be. Random variation ensured that every high guess was counter-balanced by a low guess that was equally off the mark. After 800 such guesses, the average would stick right in the middle. In this case, the average happened to be the truth.
You can tease the same kind of wisdom out of a handful of dice. Say you hold a contest to guess the number you’re thinking of: 3.5. Only six-sided dice can enter this contest and, therefore, all guesses will range from 1-6. (Note that each die is physically incapable of guessing correctly, as dice can only express whole numbers.) Each die can enter the contest as many times as it wants and, eventually, you gather several hundred entries. Miraculously, the average “guess” is exactly 3.5! Again, the average just happens to be the truth.
The trick is that truly diverse (i.e. random) opinions will always vary around the mean. When you aggregate a whole lot of random opinions, you get a deceptively precise average, but this is not “wisdom” in any real sense. It’s a statistical artifact called the Law of Large Numbers and it has nothing to do with intelligence.
There are two frustratingly common factors that throw this trick right off the rails. The first is communication, as discussed above. It leads to the primacy effects and power law distributions that plague news aggregator sites. The second is bias that arises from common wisdom… or lack thereof.
What if you asked a crowd to answer the following well-defined question: “What is the distance to Alpha Centauri?” Because astronomical distances are so much larger than anything in a normal person’s experience, their guesses would probably fall short of 25 trillion miles. (An astronomer, on the other hand, would be right on the money.) In this case, the average just isn’t the truth.
The election of Donald “Buffoon” Trump got me thinking about the story of Kurt Gödel’s U.S. citizenship hearing and how he claimed to have discovered an inconsistency in the Constitution that could allow the U.S. to become a dictatorship.
Turns out there is some discussion and research of the topic:
WikiLeaks has recently released a collection of confidential documents that originated from within Hillary Clinton’s camp. They also claim that there will be more documents forthcoming within the next few months. Here are some articles on the matter:
One thing that I find problematic about these releases is that they are confined to just one of the two main presidential candidates. It might very well be the case that Donald Trump has done a ton of things that would be similarly damaging to his campaign. Without omniscience, without knowing that there is no such damaging information about Donald Trump, is it right to only release damaging information about the one candidate you happen to have the dirt on?
Sure there is a sense in which it is appropriate to disclose such information for the sake of truth and transparency. But in a situation such as an election, where damaging revelations can effect the outcome, the lack of total knowledge and asymmetry with regards to candidate revelations feels problematic.
To quote Wikipedia, “In news media an echo chamber is a metaphorical description of a situation in which information, ideas, or beliefs are amplified or reinforced by transmission and repetition inside an “enclosed” system, where different or competing views are censored, disallowed, or otherwise underrepresented”.
The internet and social media have really increased the prevalence of echo chambers. Here are some articles on the phenomenon:
It has become apparent that Twitter is largely (at least for me) a left-wing echo chamber. This makes me wonder about the possibility of an opposite effect, whereby someone aligned with one end of a spectrum moves some degree away from it as they become averse to the regressiveness, amplification, repetition and uncritical reinforcement conduced by the echo chamber.
I became aware of the Young Turks and their main man Cenk Uygur earlier this year. As the months have gone by and I have watched more of their YouTube clips, Uygur’s arrogance, ignorance and general thickheadedness has become more apparent.
One conversation that I found interesting is the one Uygur had with Sam Harris, particularly the following portion, as it involves discussion relevant to truthlikeness and probability:
In this discussion, Harris makes the point that Mormonism is slightly more improbable/absurd than other Christian faiths because it makes the more specific claim that Jesus will return to Jackson County, Missouri rather than the more general claim that he will return to somewhere on Earth.
Information and communication technology occupies a central place in the modern world, with society becoming increasingly dependent on it every day. It is therefore unsurprising that it has become a growing subject area in contemporary philosophy, which relies heavily on informational concepts. The Routledge Handbook of Philosophy of Information is an outstanding reference source to the key topics and debates in this exciting subject and is the first collection of its kind. Comprising over thirty chapters by a team of international contributors the Handbook is divided into four parts:
quantitative and formal aspects
natural and physical aspects
human and semantic aspects.
Within these sections central issues are examined, including probability, the logic of information, informational metaphysics, the philosophy of data and evidence, and the epistemic value of information.
The Brexit result showed an inverse relationship between the percentage of ‘remain’ voters and age group; the lower a voter’s age, the more likely they were to vote ‘remain’. The following image from the BBC provides a graphical breakdown:
One issue made of this outcome is that the winning decision to leave has been supported more by older people who will be less affected by the decision over time whilst the losing decision to remain has been supported more by younger people who will be more affected by the decision given their greater remaining lifespan.
This got me thinking of a voting system whereby the impact of an individual’s vote is adjusted by a weighting; the younger the voter the greater the weighting. I was going to write up an example of this idea applied to the Brexit vote, but just found the following article which espouses the same idea: Here’s what would have happened if Brexit vote was weighted by age.
Such a voting system would only apply to decisions with direct long term consequences. Some might claim it ageist, but I see it as a perfectly reasonable way to incorporate consideration of the impact a voter has on a decision and the impact the decision will have on them.
Some theoretical mulling: given the reports that some #Brexit leave voters regretted their decision, I’m wondering about the possibility of having a voting system whereby (1) people vote first round (2) the results are made public (3) people can change their vote in the second round with knowledge of the first round result. I say this with a general interest in voting procedures, not because I have a particular position in this referendum.
I am currently doing some text analysis with IBM’s Alchemy. I thought that it would be amusing and somewhat interesting to run some transcripts of Donald Trump speeches through the online demo, particularly to see the results of Alchemy’s emotion analysis: https://alchemy-language-demo.mybluemix.net.
Sure enough, out of the 5 emotions of anger, disgust, fear, joy and sadness, the negative emotions ‘trump’ the positive emotions, with anger and fear being the most prominent. Here is an example speech and its emotion scores http://www.p2016.org/photos15/summit/trump012415spt.html: