Creating a Consensus and Serving This by a Robust and Reliable Engine, That’s peerguess.

How will you get more people in?

As mentioned in many questions, we have four main features in the peerguess platform; which are Cumulative Guess Analytics, Recommendation Engine, Auto-Trade and Stigmergic Quantitative Data. Our ICO investors will be the only beneficiaries of those modules for free, and we do not plan to introduce paid membership program within the two years after the ICO period in order to protect our investors.

We know that for accurate results, we will need a large population of daily active users, and we need them to become a part of the decisionmaking process in our search to a basic question: “Will Bitcoin increase or decrease in the next 24 hours?”. To facilitate this answer, we are bringing some of the gamification principles into the project and add Gems and Tokens to the system.

We are also planning to introduce FX (Foreign Exchange) and DEX (Decentralized Exchange) modules to attract more users. Allowing our users to create mixed coupons of cryptocurrencies, tokens and FX assets will be a unique feature, and will give us a competitive advantage over the market once completed…

By following the gamification principles, we know that, in time, many users will enjoy competing with friends, family and with each other, and competition will attract more users via word of mouth advertisement. However, we are aware that we will need to dedicate a large budget for a targeted segment marketing to acquire innovators and early adopters at the beginning. Our plan is to recruit marketing and user acquisition experts following the ICO sales, right after incorporation.

What will you do to make data more reliable?

The idea of collective intelligence follows the idea of collective wisdom, which simply says “many heads are in general better than one”. Our unique data engine will collect data, analyze and report to form “Consensus Clustering”.

The idea of collective intelligence can be traced back to Aristotle and the philosophers of antiquity. One theoretical basis for collective wisdom can be derived from Condorcet’s Jury Theorem.

Condorcet’s jury theorem is a political science theorem about the relative probability of a given group of individuals arriving at a correct decision. The assumptions of the simplest version of the theorem are that a group wishes to reach a decision by majority vote. One of the two outcomes of the vote is correct, and each voter has an independent probability 𝑝 of voting for the correct decision. The theorem asks how many voters we should include in the group.

Condorcet’s jury theorem calculates the probability, 𝑃𝑁 , that a jury gives the correct answer, given:

𝑁 = the number of jurors

𝑝 = the probability of an individual juror being right

𝑚 = the number of jurors required for a majority

Condorcet’s jury theorem in its simplest form has the following formula based around the cumulative binomial distribution:

Condorcet’s jury theorem assumes that all jurors are independent and with the same probability of being right. Condorcet’s jury theorem could be applied to our case as guessing theory, given the assumption that there is a correct guess. When this is done, jurors are replaced with guessers.

The graphs suggest that when an electorate is large enough, and individuals in that electorate are right more often than they are wrong, elections would act as a “truth tracker” which in our case would mean that if we have enough population to guess the currencies’ in the next 24h, our platform will provide the best predictions possible to use with our Recommendation Engine, Auto-Trade and Stigmergic Quantitative Data modules.

We will be also using the theories and principles from Smarm Intelligence and Stigmergy, which is a consensus social network mechanism of indirect coordination through the environment (our application) between agents or actions (Gems and guessing). The principle states that the trace left in the environment by an action stimulates the performance of the next action, by the same or a different agent. Consequently, subsequent actions tend to reinforce and build on each other, leading to the spontaneous emergence of coherent, apparently systematic activity. This is also where the blockchain architectures obtain their power from, and where we will get our power from.

Derived from the Jury Theorem, the Swarm Intelligence and the Stigmergy; we will be largely relying our back end, our data engine on Unanimous AI project, created by MIT. There are numerous reports and results about the project, which can be found on their website but let’s dig one example as a point of reference;

Experts at the New York Times made predictions for the 2015 Academy Awards. These experts possessed far deeper knowledge than the novice members of the study. Still, the New York Times only showed a 55% success rate. Whereas a group of 7 novices, functioning as a social swarm, made predictions that surpassed industry experts, with 73%. Although not conclusive, this result suggests that social swarming may provide a means of achieving expert-level insights from groups of non-experts.

Our application will be even more than that as we will be creating a melting pot of novices and experts trying to guess the future. Meanwhile, they will create swarm intelligence, and at some point, the end result of these guesses will create a consensus and thus a robust and reliable engine to serve all who have access rights.

Stay tuned with us for the next blog posts!

Peerguess Token Sale Starts on 28 October, 2017 07:00 AM PDT

Official Links of peerguess


Bitcointalk ANN Thread:



White Paper:

Peerguess Bounty:



Until next time,