CND and the sciences.
In science, decentralized approach could improve the quality of research by removing emotions and human sentiment similarly to how it would do so in finance and investment decision-making. It could also use the wisdom of the crowds to come up with new and unusual solutions to existing problems.
For example, in 1906 famous British researcher Francis Galton decided to survey the visitors of a rural farm fair by asking them to guess the weight of a bull that public could see on display during the fair. The organizers of the survey promised a prize to those survey participants that guessed close to the real weight. During the survey, Galton asked about 8000 people, some of whom were experienced farmers and some of whom had no experience with farming, animals and agriculture whatsoever. The average guess out of all the participants who tried to guess the weight right was 1197 pounds. The actual answer was 1198 pounds. This means that as a group that tried its best, the group was very close to the true answer.
The wisdom of the crowds in modern days
Similar numbers come from the TV show called Who Wants To Be a Millionaire. When participants of the show do not know the answer to the questions, they can call a friend or ask the audience. Stats show that calling a friend results in the correct answer in 65% of the outcomes while using the wisdom of the audience results in the correct outcome in 91% of cases.
Sociologist Heigl Knight conducted a similar experiment, asking students to make an assumption about the temperature in the room. The average of the guesses was 22.44 degrees Celsius, and the actual temperature in the room was 22.2C.
The studies about the wisdom of the crowds have been extremely popular for about four decades starting from 1920s.
James Surowiecki justified this idea and explained in detail how it works in his 2005 book under the same name, The Wisdom of The Crowds. In the book, Surowiecki pointed out several requirements that sometimes may be hard to accomplish in the modern society for the wisdom of the crowds to work. The first requirement is that people actually need to think about an answer that they are about to give and focus on the task. The wisdom of the crowds is not going to work if you take a crowd of people staring at their mobile phones and have each of these people give an answer while being shortly interrupted from whatever they are doing. The second requirement for the wisdom of the crowds to work is that the opinions of the people need to be independent, meaning that a person giving an opinion can’t know about what others have said and will not be punished if the answer is too far off.
For example, during a 2011 experiment at the Swiss Federal Institute of Technology the researchers have asked the participants to make guesses about the geography of crime in a region that the participants knew nothing about. The experiment offered participants a reward if the guess of the group was not too far off. The scientists who conducted the experiment have discovered that as the participants were learning about the answers of each other, the range of their own answers started to narrow down significantly. In other words, when the participants knew about the answers of other participants, the group started moving towards consensus, which is an answer that most of the participants in the group find acceptable, and a group consensus answer can be very different from individual answers that try to be as accurate as possible.
This discovery also challenges managerial approaches that believe in group consensus always and above everything else. Depending on the social dynamic that is occurring in the group, the group may end with a relatively arbitrary answer that makes the most influential members of the group happy.
Another study, conducted in 2004 by Scott Page of UMichigan and Lu Hong of Loyola University, showed that decisions by a diverse group of experts were on average better than the decisions by a small group of the best of the best in the field. Practically speaking, this means that a diverse group of average problem-solvers was accomplishing more than a small number of best performers.
All of this could allow scientists to use Cindicator platform to create truly independent data points and make decisions based on those data points. For instance, in a known experiment a group of participants received all the necessary tools they needed to build a molecule of protein. The results from such an experiment on the Cindicator platform could them be combined with computer modeling and machine learning technologies that would help scientists create new molecules and new medicines.