Ask The Whole World A Question, and Get One Answer. How? Like this:

Do you think you could ask Earth — all of the people — a question, and get one creative answer from the whole planet? If so, how long would that take?

We not only think it is possible, we think it is on the close horizon, and it wouldn’t take that long to do so. We would like to show you how it could be done.

---

(NOTE: This is a partial excerpt from our previous (longer) article titled HOW TO FIX CORRUPT GOVERNMENT IN 3 SIMPLE STEPS, breaking it down into smaller, more digestible chunks).

---

If you are brand new to the idea of collective intelligence, welcome! We highly recommend starting with THIS article first, and then maybe returning here so you at least have the theoretical basics down. In this article we are going to show what a Human Swarm Intelligence ecosystem would look like using graphics to help explain it.

But first, a few refresher definitions:

SWARM - a decentralized group of individuals who collaborate or coordinate their actions in a collective manner, often facilitated by technology, who are on the same problem solving mission together.

COLLECTIVE INTELLIGENCE - Sometimes referred to as “The Wisdom of Crowds,” or “Human Swarm Intelligence,” or just “swarming.” It is the shared group intelligence that emerges from the collaboration and combined efforts of individuals. It usually requires several conditions to optimize: 1) Decentralization 2) Diversity of Opinion - aka individualism 3) Coordination/Alignment (How do the individuals interact, what problem are they solving together, and do they understand the problem?) 4) Aggregation - a way to aggregate answers from the group.

A SWARM IN ACTION

Let’s examine the basics of how swarming within an ecosystem can work by forming an example. To start, let’s say someone(s) in the state of Nevada creates a new swarm with the mission statement: Making the systems in Nevada corruption resistant and more accountable to the people.

Like any system, trust matters. But we cover trust HERE and HERE. This article is more about how to extract one answer from large groups of people. So let’s assume the system we use is trustworthy, and the entire state of Nevada — 3 million people — joined the high-trust swarm system.

Next, a question would be chosen to ask those 3 million people as the first question to accomplish the mission. Let’s say it was something like:

What amendment should we write to our State’s Constitution to help us prevent corruption in our systems?

This is how a swarm ecosystem could work (we say “could” instead of “would” because there are many different ways to collect the wisdom from crowds, and new ones still being explored).

Participants would break up into smaller cells, or groups of 8-20 people or so. For our example here we chose the number 12 because it fits nicely into the design, but the perfect number is something we still need to test for - same thing with time limits, methods and more. For example, should the swarm answer in three minutes? Thirty minutes? A week? Should the answers be written? A video presentation? Audio? A PowerPoint presentation? In experiments, many methods seem to work well. But we won’t know which ones work better for what kinds of questions until more dynamic systems are built, and we test them thoroughly. What we do know is that many methods seems to work (including pen and paper), and nearly all swarming methods work better than individuals or even experts trying to answer the same question — as long as the participants understand the problem. In other words, humans solve problems better in groups. A group of two is better than a group of one, and a group of 10 is better than a group of two — IF the conditions are right. And the most important conditions to exist are trust between the people, trust in the system we choose to use to help us problem solve, and then correctly identifying and understanding the problem.

For this specific example, our swarm will use written answers, and each individual will have 5 minutes to complete their answer. The system would then take all 3 million people and divide them randomly into these smaller cells of 12 people each. Everyone’s names and identities would be hidden during the process to avoid the problems we listed in our previous article (cascading, groupthink, etc.). Each individual would then populate their own creative answers in that 5 minute window. The answers would populate in a circle and we wouldn’t know who answered what. Instead we would just see 12 random answers in a circle, like this:

If any of the answers were too similar or the same, members of this cell would then drag the answers together merging them into one. Then the system would randomly choose one of the wordings to represent any answers that were combined together. It is important to note here that answers that are different in logic should be left alone. So if this swarm was writing an amendment to Nevada’s State Constitution with the goal of fixing corruption, and we saw these three answers in our cell of 12 people:

1. Get the money out of politics

2. Remove money from politics

3. Take the money out of politics except all campaign contributions under $20 from personal non-business entities only.

We could see that #1 and #2 would be dragged together as they are basically the same answer; so we merge them into one. Then one of those two would be randomly selected by the system to represent them both. The merged answer would not hold any more weight or value than the other answers, it just wouldn’t be repeated twice and thus diluted in the process. But since the third answer is similar but different enough, we let it stand alone.

We illustrate this here with colors for the answers:

If this swarm answered like this, and the colors represented their answers, they would then merge the similar ones so they weren’t double or triple represented in the next phase:

So even though twelve people began the process, the number of answers were pared down to only nine after we combined the ones that the consensus of the cell thought were too similar to each other - the orange and the bright green answers. After the answers were pared down, the remaining nine answers would go in a circle and the twelve members of the cell could then use Dr. Rosenberg’s digital method, or a similar method (we have tested several that seem to work well) in order to choose which answer is going on to the next round.

We admittingly like Dr. Rosenberg’s method a lot. He uses a digital magnet and algorithm to form a human swarm, and then the members push and pull on the clear hockey puck in the middle using their digital magnets until one answer is settled on. The full process in a cell of twelve people would look like this, after a question is asked, and their answers populated:

In this case, the light blue answer would be the one chosen by the cell, and the eleven other answers would be eliminated and the light blue one would go onto the next round. Using this method we can populate a creative, well thought-out answer from an entire population of 3 million people in only 7 iterations of swarming. It would look like this:

Once we arrived at the final twelve answers, the entire three million people could use their magnets (or whatever other method we chose) and we would come to a final decision as a swarm. From three million people, to one answer in seven iterations.

A few final things on a swarming ecosystem process:

• Even though coming up with one unified answer from a swarm is possible, for some problems it might be wiser to generate the top five answers. Or top two. Then, experiment several answers against each other to see which one gets the best results. Remember every decision by a swarm should be results-driven, so the swarm knows when to make changes or reverse course. Whenever possible, metrics should be assigned to the mission statement or goal of the swarm.

• How a question is worded to a swarm matters. For problems that require imagination, the more creative the swarm can be, the better the results seem to be. While Human Swarm Intelligence is one thing, we think what is even more exciting is what we call Human Swarm Imagination. This is not possible with Dr. Rosenburg’s current system and would be possible with an entire ecosystem like this, or high-trust Network State. While we have only done this on smaller swarms of less than 550 people so far, we have found that sometimes broad or open questions are the best like “What would you do if you were in charge of this system/business/Network State/etc?” Or “What do you see as our biggest weakness and how would you solve it?” From what we have witnessed with businesses that swarm, these are the types of questions that have turned into the most fruitful solutions.

• Rather than having one person decide what question to ask the swarm, you can have the swarm decide what questions to ask itself. This is a way to optimize self-organization in a decentralized group. The swarm can ask itself stock questions like “What question should we ask ourselves today to help us achieve our mission?” Or “What problems should the swarm work on today?” Or “What part of our Swarm Society needs the most help right now, and how could we help them?”

• Confidence ratings can be added to swarms, so answers can have confidence scores attached to them by the decentralized individual members for more in-depth evaluations of answers.

• You can run the same question through a swarm many times. Each time the swarm learns, and each time the answer has the opportunity to become refined, or evolved, or even changed.

• Individuals in swarms should be properly educated before participating in live swarms. Just like driving a car or flying a plane or using electricity. In a decentralized group we are all driving the direction of it, and that is a big responsibility — and we all know that groups have dangers — so we need education to help be the systemic way we make sure the groups optimizes and avoids those perils (such as www.SwarmAcademy.ai).

• Setting our own culture is wise. Instead of letting top-down entities define our culture we should have our own code of conduct. If this is a Network State that will be on the application. Keep in mind that the iterated prisoner’s dilemma suggests that the more trustworthy our society or system is, the better outcomes it will get. Culture matters for results.

• We have debated having the “losers” of the first round of a live swarm form another fresh swarm, to see what happens with them, to make sure that some good ideas weren’t running into bad luck in the first round, and new knowledge or a clever answer wasn’t being accidentally overlooked. But we cannot test this hypothesis without first having the ecosystems built to run larger swarms. Running questions through a swarm more than once is another possible remedy to this potential problem.

In the final diagram for this section we would like to show how we could swarm almost twice the size of Earth in just nine iterations if we had twelve people in each cell. From 14 billion down to one answer like this:

And that’s it.

This is an example of a system where we could get one answer from the entire planet Earth.

---

In closing, if our epistemology is correct, then we should be optimistic. Why? Because all problems are solvable, and people solve problems better in groups.

All of the major systems have been corrupted on Earth - government, media, academia, food, and even science. So we need something bold to fix this corruption. Network States seem like a great place to start, but how do we optimize those? With transparency, decentralization, trust, and with what we believe is our new secret weapon: Collective Intelligence.

Thanks for reading!

---

Check out the progress on: SwarmAcademy.ai

As always the entire purpose of The Society of Problem Solvers is to connect with other solutions-minded results-driven people like yourself!

Thank you to all of the writers that make this possible. We are always accepting guest writers, so if you have an idea for a solution, please reach out. Let’s solve it all, together.

For over 3 billion years on this planet there were only single-celled organisms. Then one day they somehow learned to work together and make complex multi-celled creatures . Right now we are like those single-celled organisms. Our next evolution is finding how to work together, better… (like we wrote about here).

#SwarmAcademy #ResultsMatterMost #DecentralizeEverything #DemandTransparency #Uncorruptors

COMMENTS ARE FOR EVERYONE AS A PLACE TO THINK-TANK SOLUTIONS. They will never be for paid-only subscribers and we will never charge a subscription.