Introduction to Cindicator (CND) Part 12

Member motivation on the platform.

 

When does the “wisdom of the crowds” actually work?

Usually, people do not expect the “crowds” to be wise. This is because when thinking of a crowd, most people are imagining a crowd of tipsy concert goers or a crowd of people on the subway rushing to or from work, staring into their cellphones and listening to blasting music.

When it comes to the term “the wisdom of the crowd,” crowd in this term does not mean ignorant, chaotic gathering of random people. In the case of the Cindicator ecosystem, crowd means financial experts from all over the world.

For the solutions by the wisdom of the crowds to work, the process of answer gathering needs to be in agreement with the following three requirements. First, the answers need to be truly independent. The answerers also need to be providing their answers in full privacy. An example of an answer that is not independent is when all the analysts are watching the same news channel, get the same news at the same time and then start discussing the news and watch together what happens to the markets. In this instance, the answers by the analysts will be close to a group consensus and are likely to not be independent at all. Next, the number of people giving answers needs to be large. The larger the group, the better the final answer is going to be. Finally, there must be a way to measure the answers. For example, with the questions “what do you think the price of Bitcoin will be seven days from now” and “what do you think is the temperature in this room,” is it possible to measure the answers and calculate the median and the average. With a question such as “what is your favorite fruit?” measuring answers will not be possible.

 

“Wisdom of the crowds” on the Cindicator platform

The approach of Cindicator is that members of a group that answer the same question need to have different kinds of knowledge, backgrounds, expertise, and personal experiences. If the Cindicator system decides that there is a particular bias in a group that is answering questions on the platform, it will be incapable of creating a signal with a high probability of the signal being correct.

A group of answerers may have a lot of outliers, yet this is absolutely fine because the platform uses various mathematical distributions to eliminate the outliers, the Gaussian distribution being one of the examples.

Furthermore, the Cindicator platform does not allow for any communication or exchange of any kinds of information among the members of the same group to make sure that the answers are truly independent and unbiased.

 

Motivation of the group members on the Cindicator platform

The reason why people providing answers on the Cindicator platform are going to do their best in giving the right answer is that the platform has multi-level motivation.

The first type of motivation that the Cindicator platform uses is financial motivation. Each month, the ecosystem gives out rewards to members according to their standing in the rankings. The more accurate the predictions of a member, the higher he or she will be in the rankings and the more compensation the person will receive. Both errors and infrequent activity downgrade the position in the rankings.

The Cindicator platform has several types of rankings, including internal user scores and special nominations.

On the platform, users don’t just give predictions in the way they would do it on a typical prediction market. The users actually participate in mini-transactions, which increases the responsibility of users. Also, users do get feedback on the accuracy of their responses and suggestions on increasing their levels of knowledge, which is a part of the network effect on the Cindicator platform: the more users the platform has, the more accurately it will be able to tell users what they need to do to take their skills to the next level and make more accurate predictions in the future.

 

Cindicator’s “black box”

Once the Cindicator ecosystem collects answers from the members of a group, it runs them through an artificial intelligence system. However, this system is only the first stage of data processing on the Cindicator platform. Further stages include running data through the Cindicator “black box.” Black box on the platform defines the parameter of weight for each responding user. When identifying the parameter, the system takes into consideration personal track record of answers for the users. This record is broken down into segments such as types of answers, categories of answers, and so on. It also monitors the dynamic feedback that follows the answers of users, meaning losses or profits. Finally, the black box analyses the group with an attempt of identifying super forecasters who are right in an unusually high percentage of their answers.