Wisdom of the Crowd
Do the many know better than the few?
The wisdom of the crowd is the theory that the collective opinion of a larger group of laypeople can sometimes outperform the opinion of a single expert. The earliest written record of the theory dates to over 2,000 years ago and has occupied philosophers and mathematicians since.
Setting The Scene
In 1906, in the English port city of Plymouth, the polymath Francis Galton found himself at a county fair where that hosted a competition to guess the weight of a bull. Nearly 800 people entered the competition and Galton observed that the median guess was within 0.8% of the actual measured weight. By comparison, individual slaughtermen, butchers and farmers, the people he expected to be better at estimating the weight of a bull, submitted guesses with a greater degree of error.
This observation fascinated Galton, who proceeded to write up his findings and submit them to Nature journal in March 1907 under the title Vox Populi. Galton plotted the distribution of guesses and noted that, in this particular experiment, the median of the guesses submitted by the crowd were more accurate than the guesses by individual experts. Galton observed that this was evidence of the “trustworthiness of a democratic judgment.”
Examples of Wisdom of the Crowd
Perhaps the most familiar example of harnessing crowd wisdom is the jury trial, the concept that a group of men and women selected for impartiality is more likely to accurately consider evidence and pass fair judgement than an individual tasked with the same responsibility. A group of people is also less easily manipulated, corrupted or bribed than an individual and if they discuss and debate in order to reach a verdict. It is likely that the idiosyncratic noise of an individual outlier opinion would be averaged out through regression towards the mean (another of Galton’s theories).
Another familiar mechanism that uses wisdom of the crowd is the concept of democracy and elections. In an election, the entire population of citizens of a country are asked their opinion of who would do the best job as leader on the understanding that this process is more likely to lead to the fair and uncorrupted election of the most capable candidate to do the job.
Crowd Wisdom in the Internet Age
In Galton’s county fair example, the crowd in his experiment was a relatively small one, drawn from the local area. Does the internet create opportunities to harness the power of crowd wisdom? In order to answer this, it is necessary to explore what makes a capable crowd.
Independence
The opinions of people should not be determined by the people around them.
Decentralization
People should be able to draw on personal knowledge and specialize in it.
Opinion Diversity
Everybody must be enabled to contribute an opinion without judgement, to encourage diversity of opinion.
Trust
Participants in the crowd must have trust that the process is fair and scientific.
Aggregation
There must be a mechanism for aggregation of opinions in the form of a scientific process.
The advent of the internet and diverse social networks has contributed to an enhancement to all the above measures to differing degrees however the partisan nature of social networks has also reduced the ability of an individual to express an opinion without judgement. This can be mitigated by allowing members of the crowd to submit opinions anonymously.
Some notable examples of the application of crowd wisdom on the internet are Wikipedia, Reddit, Quora, IMDB and TripAdvisor.
Strengths & Weaknesses
What is the best film ever made? If you ask IMDB (and its 83 million registered users), the answer is Shawshank Redemption. Is this an unarguable scientific fact? Of course not. Shawshank Redemption is simply the most popular opinion of the crowd.
Crowd sourced wisdom is rarely appropriate for answering complex and specific questions, and is often more suited to measuring opinions and sentiments. For example, advertising agencies have experienced great success in polling a crowd of people to ask whether a particular type of advert makes them feel positive or negative. Using this information, they can better tailor advertising campaigns to certain groups and therefore improve the outcomes of their advertising campaigns.
Wisdom of the crowd is adept at averaging out individual noise but cannot be relied upon to normalise systematic biases such as cognitive bias. Scott E. Page, professor of complex systems, political science, and economics at the University of Michigan, noted the following “The squared error of the collective prediction equals the average squared error minus the predictive diversity”. To put it into simpler words, the greater the diversity of the crowd, the better the results.
Crowd Wisdom, Sentiment & The Financial Markets
Lets return to Galton. In one of his wisdom of the crowd experiments he noticed that a crowd comprising of people who have to pay an entrance fee to submit their prediction is more likely to be accurate than a crowd of individuals who can submit their prediction for free. He theorised that by having something (the entrance fee) at stake, people were more likely to give thought to their prediction and therefore increase the quality of participation.
Communities interested in predicting financial markets often have their own money at stake in the form of their investments so tend to provide, if afforded independence and freedom from judgement, more accurate opinions and predictions than a community with nothing at stake.
What makes markets move? What triggers or sustains a bull run? What causes a bull market to flip to a bear market? Do the opinions of social media users influence crypto markets in the same way that the opinions of professional traders? There are an enormous number of factors that influence financial markets but one of them is mass psychology.
A good example of mass psychology being a major impact in the movement of a market, is the GameStop short squeeze in 2021. In this example a Reddit subgroup (r/wallstreetbets) performed the role of the crowd by forming the sentiment that GameStop was significantly undervalued. The ability to capture, measure and quickly identify the sentiment of a group of people towards a certain security or asset can be invaluable.
Intrade — Accurately Harnessing The Crowd
Intrade was a a hybrid betting and trading exchange that allowed people to trade “contracts” with each other on the likelihood of certain measurable events happening in the future.
Intrade allowed bets on a variety of issues: election and referendum results , climate change in particular areas, current events, predicting Oscar winners, finance (DJIA, S&P 500, NASDAQ-100, gold price), but did not predict individual stocks.
Instead of asking people for detailed predictions, Intrade focused on sentiment. For example, instead of being asked to predict the price of gold on a certain date, people were encouraged to predict whether the price of gold would increase or decrease in a certain time period. Are you bullish or bearish on the price of Gold?
The results were startling. Intrade proved to be more accurate at predicting a wide range of types of event than traditional methods. One of the starkest examples of this was when Intrade predicted the 2008 US election to within one electoral vote for Barack Obama. This was no fluke. Intrade prediction markets also proved exceptionally accurate at predicting financial market and commodity price trends.
In reality, identifying all of the network effects and social influences that led to the high accuracy that Intrade enjoyed is difficult but it is clear that being able to harness opinions and sentiment within an environment designed to facilitate the five measures of crowd quality (defined above) was a major part of their success.
Blockchain Tokens & Capturing Sentiment.
In my article titled “Introducing Data NFTs” I argue that any data can be tokenised on a blockchain, and opinions and sentiment are no different.
Through careful design, a blockchain can be tailored to provide the tools to capture the wisdom, opinions and sentiments of the crowd in much the same way that Survey Monkey provides tools to individuals and businesses to capture opinions. However, Survey Monkey omits a key item of functionality; the ability for crowd sourced information to be owned by the contributor, monetised and utilised using smart contracts.
Sourcing good quality customer opinion data is a problem for the retail industry. We are all accustomed to the numerous email surveys we receive inviting us to provide feedback and opinions after recently using a service. How many of those do we actually click and contribute to? Would you be more likely to participate in a survey if you would be rewarded proportionally for how useful your data is to the company requesting it?
From a company perspective, one of the trickier elements of customer opinion canvassing is due to the fact that somebody who is unhappy with your service is much more likely to express an opinion than somebody who is happy with it. Identifying strengths is every bit as important to a successful as identifying weaknesses. Providing incentives to share your opinion is one of the solutions to this problem. A smart contract enabled blockchain is ideal for this purpose.
Wisdom of the Crowd, GNY and LML
GNY is creating a suite of market intelligence tools using neural networks to assist those who trade and invest in cryptocurrencies. Crowd wisdom and sentiment is a valuable datapoint that will be added to those tools.
LML will be a toolset that facilitates the aggregation and monetisation of data including the gathering of crowd wisdom not only for cryptocurrency markets but any real-world application where crowd sourced data is useful. In the case of cryptocurrency market sentiment, LML will feed the GNY neural networks to attempt to identify market movements before traditional trading indicators pick them up.
In my next GNY Labs Medium article, I will dig deeper into the tools that GNY and LML will provide in the future, how they relate to each other and the opportunities that they will unlock.
