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Changing Network Shapes

It is a good day for insights on networks today. I'm sitting towards the back of a women's group, a coffee morning at a school. When the group came into the room there were rows and rows of chairs set up. After thirty minutes, the room has been entirely reconfigured, and this has inspired me to think more deeply about networks, network structures, network shapes, and network nodes.

I created a little network spread diagram to illustrate. Each frame is a moment in the network, a snapshot of structure and behavior. In the diagram, the black curves are chairs, and the red dots are people, with the arms indicating the direction they are facing.

Chair network development

In this scenario, the chairs gradually get moved into new patterns, based on the actions of the people. This itself may be an interesting observation - the chairs are really 'infrastructure', inactive but necessary components of the network. They become engaged or active when a person sits in a chair.

Some positions are not conducive to spread. In the second frame, there are two groups who are not facing each other. This is quite unfriendly from a human perspective, and very uncomfortable if you have to keep turning round and stretching your neck muscles to listen and talk. This human need begins to change the shape of the network, both the positioning of the infrastructure of the network (the chairs face a new direction), and the network agents or people.

In terms of network nodes, the scenario illustrates the difference between the infrastructure of a node (in this case, a chair), and the active component of a node (in this case, a person). A chair cannot spread a message, but the chair creates the shape of the network. A person can exist outside of the node infrastructure, but that makes them less able - as a communication node - to participate in the action. For example, a seated group uses eye contact within the group, but a person standing to the side is less a part of the group and therefore can have less impact or involvement.

The scenario of the coffee morning provides a good example of prediction around network change, and also the risks in making predictions. When I made my original design, the last frame was a prediction of network development. As the three new visitors arrived, there seemed to be a logic that the circle would be the logical progression. And yet, while I had been face down drawing my diagram on my iPad, the network shape had changed:

Chair network development alternative

Instead of a single common circle, two groups had been created.

There are several reasons why this might be (and I would write more down but I have to concentrate a little on the discussion taking place in front of me). There are node preferences - the people know each other, and the separate group is more of a common group that is different to the other first group. It is harder to get to speak in a large group, and as people like to chat, there might be a good logic to split into smaller groups.

There is a lot more to this, and it is always tempting when you are writing (ok, when I am writing) to turn a blog post into a white paper or a chapter of a book. I'll resist temptation this time round... and stop.


Chinese Whispers - Message Transformation due to Spread

I whisper something to you, you to your neighbor, and so on, until the message returns to the start. And usually, the message is barely recognizable from the initial point. The act of spread has transformed the message.

This is going to be a big topic within 'spread'. Right now, as I develop some thoughts during a snow-bound coffee morning, early-stage concepts that spring to mind include:

  • Message Fidelity: Basic concept of the extent to which a message changes as it moves between nodes.
  • Message Morphing/Variability: The amount and type of change the message experiences as it spreads.
  • Message Amplification: The extent to which the message becomes 'more important' within the network, like a wave gaining amplitude.
  • Message Hijacking: An existing message propagating through a network could be taken over by another message. The original message is somehow perverted with the new message, accidentally or deliberately, and the new merged message continues to propagate, albeit with a new underlying message.

We have done work on this before, and I now need to hunt through the PDFs of scribbled notes to uncover our past ideas on this topic. In the meantime, at least the concept is exposed once more, and to a larger audience.


Environmental Conditions and Network Function

I was driving through the snow yesterday in Berlin, and as I was slowing ploughing through the traffic, my mind drifted back to speedier times, summer times, when the roads were all clear and the traffic sped through the streets with relative ease.

It occurred to me that the theory on networks and propagation through networks might be missing out on a core concept, that of the importance of environmental conditions on the network itself.

A road network is a network. Clearly a snow-bound road is not the best network state for propagation. The state of the network overall impacts the ability for the nodes (call them 'intersections' in the road case) to connect together, and for the 'packages' (call them ’cars’) to move between the nodes.

Some network conditions will be favorable to spread. For example, good visibility may allow the packages to see nodes more easily, faster, and possibly identify attractive nodes that are farther away and that may allow spread to take place more easily. Conversely poor visibility would slow down network propagation, reducing spread, and potentially grinding the network to a halt (anyone who has ever experienced London in an inch of snow will know what I mean, or O'Hare airport for that matter).

Taking the concept further, it is possible that portions of a network experience different conditions from the norm. A network as a whole, or as an average, may be stable and 'average', but portions may be faster or slower. The road network of Germany may, as a whole, be fine, but local weather conditions may make a part of the network very slow. This would bring down the average, but give portions of the network a very different dynamic in terms of spread potential.

Concepts like these will help us better understand the real dynamics of networks, and help us develop new insights into how things spread. So maybe my ten minute extra delay due to snow was totally worthwhile. Perhaps (and this is giving me another idea for another day) slowing down spread can lead to higher quality outcomes. Maybe I'll work on that on my next journey ;)


The Spooky Maths of Coincidence

If there are thirty people in a room, what is the percentage likelihood that two or more people share a birthday?

Less than 5%? ... 10-25? ... 25-50%? ... over 50%?

If you are like half of the population (well, at least like the 130+ executives and investment professionals who gave this answer in our LinkedIn poll as shown below), you would vote for less than 10%. That is what our instinct tells us. If my birthday is the third of March (which it is), that's 1/365 odds, surely...

Orcasci Birthday Problem poll results - by answer

Orcasci Birthday Problem poll results - by job function

And yet, this ego-centric view of the world is actually completely and utterly wrong - by an order of magnitude. The correct answer is …71%! And you only need 23 people in a room to get a 50:50 chance of a match.

The Birthday Problem Logic

When we intuit our answer, we start from our own ego-centric view and extrapolate. This, though, is the wrong mathematical approach for solving the problem. Instead of it being a "small math" problem, it is a "large math" problem. We need to work out for each member of the group the probability of a match with the remaining members. Instead of dividing the odds, we need to add them up (for more info, check the Wikipedia entry on the Birthday Problem). And counter-intuitively we find that the spooky maths of coincidence takes hold at scale.

I have been intrigued for a long time by the weird effects of numbers at scale, particularly on the human level when lots of people gather together in some way. My work on Collective Intelligence and Idea Management at Imaginatik was based on the practical application of this insight. And at Orcasci we are taking it to the next level in its application in the Science of Spread™.

One of the techniques we have used to scale our activities, starting with personal, one-on-one connections, exploits the strange nature of coincidence. An example would be going to a conference and telling twenty strangers that you have a plan to "change the regime of North Korea for better".

What are the odds that any one of the twenty people would be able to help (as opposed to laughing or switching topics)? The odds of a match are astronomically low. The same is true if you ask a hundred people - low, low, low. You can boost it dramatically in certain ways, though, such as by going to a conference on North Korea. To some, that sounds like cheating the system. Well, so what? If your end goal is making a connection, then you change your tactics to "fish where the fish are".

The Coincidence Problem of 10 Conversation Domains

Let’s look at another example of coincidence. Imagine that each of us has a set of, say, ten "conversation domains". They might be e.g. where you were born, your job, your education, where you live, where you are about to travel to or have just come from, and what hobbies you have.

Within each domain, there will be hundreds and thousands of options. For example, there are over 200 countries. And there are many, many professions.

An individual will therefore have a "life set" of options, clustered into these ten domains. That could be several thousand total options, out of a total world set of millions.

The Coincidence Problem can now be stated in two versions:

  • The first version is: What are the odds that two people, selecting ten options from their life set each, find a match?
  • And the second version: In a group of, say, five people, each person talking to each of the other four, what are the odds that any pair match takes place?

It intuitively seems as though the odds of the first version being true should be low. I'm struggling with the actual probability maths on both of the problems, mainly because I am weak on some aspects of Quantitative Methods I seem to have skipped at university. (If you personally can help - or know someone who is strong in probability and would be willing to help ;) - can you please email me, Mark Turrell?)

It is not hard to recall your own experiences of going to a party or networking event and sharing ten such items. Admittedly you might not be listening to the other person, and some people have a slightly annoying habit of only sharing their own list, adding a bunch more, and then not listening to the other person. Still, in an evening you quite often come home amazed at the coincidences that emerge.

One important point is that many options and domains are connected. For example, a country might be your country of birth, where you studied, where you have just visited, where your partner is from. That makes "country" or locations in general a pretty easy option to key off. In this concept, one option opens up a new set of related options, all of which increase the odds of finding a match.

Some options are clustered together. In this, if you do one thing, you probably do several other related things. Again, this boosts the odds of a coincidence match.

A high proportion of people are psychologically programmed to like making connections, and most people go out of their way to find associations. This encourages people to find a match, even if the link between options might seem slight.

One can imagine developing this theory further to consider strong and weak matches. A strong match would be a 100% connection between what is expressed by the parties as their option, such as "Did you do your MBA at XYZ school?" - "Yes! That's amazing!" A weak match is less content-specific, such as "I also did post-graduate studies". It is easier to find a match with weakly defined options, and deeply spooky on the occasions when you meet someone with a strong match.

Sales people and consultants often get training on how to prompt this matching effect. As people prefer doing business with people they can associate with, these connections become very important for potential business, and the faster and easier one can make connections the better.

In my personal experience (and I get out and talk to people a lot) the matching rate is about 35%, that being finding a match in 1 in 3 interactions. I am sure that my stats are skewed, because I like coming up with near-random-seeming options that provoke, startle and help me stand out from the crowd (it's a handy tactic). More often people stick to the basics of communication, covering classic topics of job, birth place, holidays, etc. On balance, without more research, I'd be comfortable thinking that the odds of any two people finding a match are between 15-25%.

In terms of the second version of the Coincidence Problem, which looks at a group of five people, this involves taking the probability of the first version, and doing the "birthday problem maths". The odds of any pair match being created are very high, so much so that a group of people cross-sharing will find a large number of matches.

Where this then becomes very interesting is trying to force very weird connections being made. What are the odds that the person you are talking to has produced a movie, has dissected a brain or knows Chinese?

What you learn for your next networking event, and it is a sure-fire tactic, is that you need to share ten things with lots and lots of people. And encourage others in a group to start sharing.

The Orchid Model: Helpers

A final thought - and there is a lot more detail we could get into, but for the sake of brevity we'll chop it off here. Some people are naturally inclined to be connectors. In my work at Imaginatik on the importance of personality styles in innovation, the model I developed, called the Orchid Model, recognized that around a quarter of the population are "Helpers", social connectors.

To boost the odds of having connections made, connectors are fabulous as they open up their network of remembered contacts and possible connections for you, without requiring the effort of having to meet the non-connected folk.


Redesigning Democracy

As part of our mission to change the world for better, we have looked at ways to improve one of the most popular ways that we as human organize ourselves: democracy. In our view, people should aim to be happy and limit being sad. In order to achieve this, people should have the right to choose. That could be the right to choose where people live, the jobs they do, what they can study, what they can read. We are not deterministic in our view point - we just think that having the freedom to choose is a very good thing.

Democracy is one of those handy systems where the people get to express themselves, casting their vote, on how they are governed. We won't go into the topic, and in any case we'd quickly go to Wikipedia to pull out the core concepts anyway. Suffice it to say that democracy looks like a mostly good thing, and that it would be useful to have it work better and deployed more widely.

Therefore part of our work at Orcasci will be finding ways of improving the workings of democracy, in particular taking advantage of new social and communication technologies and, of course, applying our advances in the field of the Science of Spread™.

The area that most appeals to us right now is citizen election monitoring. We hope to take advantage of the mass of citizens, located in all the places one would expect to vote; enabled and empowered with devices to record and instantly share activities on the ground; augmented with collective intelligence and crowdsourcing tools to aggregate results; and supported by global communication hubs to accelerate news flow.

We have advised on a number of democracy projects, with some good early success. Key elements we have found useful to design these programs include:

  • the power of the masses (abundance)
  • leveraging pre-existing networks of people
  • the power of visual evidence
  • spontaneous transfer of information
  • accelerating the spread of results via communication hubs

We have looked in depth at how the crowd can be used to provide input and guidance to the process of engaging the citizenry. Our first project was at Imaginatik, the company our founders created in 1994, pioneering the use of collective intelligence and idea management for all types of organization. We spent a lot of time, more than a decade ago, investigating the use of early adopters of the technology, and then later putting in place citizen engagement systems directly.

There is a lot still to be learned in the areas of democracy and citizen engagement. We look forward to continuing our research - and helping groups put straightforward solutions into practice.

So, if you have any projects you'd like another pair of eyes on, just let us know. And if you come across any good case studies or resources, we'd be interested and deeply grateful to hear from you.