How Complexity Theory Affects Social Media, Streaming and Musicians
I have been fascinated with Complexity Theory and Complex Systems Science for some time now. The chemist Ilya Prigogine, a Nobel Laureate, is credited with pioneering research into this discipline and in 1971, based on his research, he applied his theories to vehicular traffic flow in his book Kinetic Theory of Vehicular Traffic. After the release of that book scientists and mathematicians began to take note of what was seen as a less deterministic approach to the science of human behavior. It has been just over forty years since the discipline took hold.
I am not yet fully immersed in Complex Systems Science so I haven't felt comfortable sharing my insights until now, so here I am taking a risk by presenting some decidedly, non-expert ideas in this forum. Feedback is very welcome.
Around the middle of 2013 after having written posts or rebutted articles about the complaints of musicians regarding the new models of music distribution, mainly the streaming of music, I realized that there were human behaviors being overlooked. New systems and structures were occurring in the models; one could see individuals behaving collectively, creating a societal shift. That shift was to "renting" music via streaming services, not owning it. It appeared that a complex system, or at least an evolving complex structure, had come into play.
This user behavior was not controlled by Spotify, Rhapsody, Rdio or any other streaming service. It would have been almost impossible to predict. This makes the musician's arguments against Spotify et al very difficult. The message to musicians is that in complex systems and structures that have already formed in new models, it's impossible to return to a system that existed in the past.
Musicians are now faced with different systems. The new systems shouldn't attempt to ape the old equilibrium (the recording industry model) and musicians should never hope for a return to the status quo, because there will be constant flux in the new systems; remember, humans are unpredictable.
For a while now my interest has been in how individuals behaving collectively when using streaming music services, affect new systems and structures that are different in feature but have a lot in common; in other words, large groups of people interacting with each other according to fairly specific rules constantly create new structures.
What, I thought, did that mean for the new companies trying to carve out sizable streaming music audiences in their attempt to reach scale and therefore profitability?
Another question that I wasn't certain had been answered was this: before they launched did any of these companies apply complexity theory studies to their business models, and if so, what were the results?
There are large amounts of commonalities between the streaming service models: almost identical music catalogues; monthly subscription plans or free access with advertising in the streams; the use of Facebook or Twitter for access to the service and for sharing.
Did these competing services discover, or simply assume, that people acting collectively would find equilibrium in one of the services and not use the others? (More on equilibrium in a moment...) Did they assume that all music fans want to access their music through almost identical systems?
The Internet has changed a lot of things. It is also a great example of a people-powered complex system and there are great tools available that help us try and understand how humans behave across the Social Web. This can lead to an understanding of what strategies can be applied based on actual user behavior.
While researching all of this “complexity” I was fortunate to come across a podcast. For his series In Our Time on BBC Radio 4, the host Melvyn Bragg had gathered together three professors to discuss Complexity: Ian Stewart, Emeritus Professor of Mathematics at the University of Warwick; Jeff Johnson, Professor of Complexity Science and Design at the Open University and Professor Eve Mitleton-Kelly, Director of the Complexity Research Group at the London School of Economics.
Here were people skilled in this discipline discussing it in depth. The podcast’s abstract was very clear:
"Complexity is a young discipline which can help us understand the world around us. When individuals come together and act in a group, they do so in complicated and unpredictable ways: societies often behave very differently from the people within them."
Does complexity and complicated mean the same thing? The answer is no.
Professor Mitleton-Kelly says in the podcast that complications are found in machine-type systems. For example a jet engine which has very many parts interacting with each other is a complicated system. The fact that those parts interact with each other doesn't make it complex, it makes it complicated. Therefore we can design a jet engine, predict its behavior and control its actions.
There has also been debate that complex systems cannot be designed, yet some can. Ilya Prigogine found that complex systems create new order, they create something new in a structure; a new way of working which complicated systems cannot do. For example cities are complex systems that are designed and never finished. Cities evolve. Cities are partially planned by people who live in them; buildings are assembled for us to use, we create roads. So structural systems are partly planned, partly evolved. We are always trying to design, but in the design of a complex system we must allow for a great deal of uncertainty, unpredictability and the system evolving.
This has important implications for streaming music companies if we accept that complex systems are rarely designed and human behavior is hard to predict. For streaming music companies, as with almost all digital products, the work will never be finished - at least not until users decide it's finished; and then of course they may move on to the next "new" thing.
Then there are social interaction networks. I don't mean only online social interaction but to stay true to this post I'd like to focus on user behavior in online social networks.
The structure of networks often make spread look easy: think corridors. We also assume that connectivity is the same over time, it is not. The quality and intensity of connectivity varies all the time even with the same individuals. If online social networks are the "corridors" of the web we must constantly evaluate what individuals are doing there. We cannot assume that because these individuals are grouped together in Facebook that they all act the same way at the same time.
For instance, brands often look in those online networks for positive and negative feedback. This is where the role of the social media community manager is meant to be of most use. The community managers are looking for equilibrium in the system - in an ideal world, positive brand feedback is the equilibrium point. The problem is that positive feedback may have multiple equilibria, whereas negative feedback is different - it's associated with mechanistic feedback and a single equilibrium point.
Professor Mitleton-Kelly gives an example: central heating systems are based on negative feedback. The temperature in a room drops so you feel cold. The thermostat tells the system to raise the temperature to your desired point, closing the gap between the actual and the desired temperature. That is a single equilibrium point. She also says: "We assume there are single equilibrium points in complex systems so we make wrong assumptions."
In my social media networks example, where Facebook is a gathering of individuals using a structure in a complex system - the Internet, community managers assume that all of those users act in the same way, whether giving positive or negative feedback. When faced with negativity they respond as if they are dealing with a central heating system, thinking that they only have to apply the right amount of correction at the correct time to reach equilibrium, i.e. a return to a positive brand result. Well that doesn't happen in complex systems.
The point here is that if a community manager is trying to re-establish the former position, e.g. positive feedback, she's in trouble, because the structure’s acts of evolving and co-evolving (through user activity) allow it to obtain different states. All those Facebook users create multiple equilibria, not a single point of equilibrium, therefore the system is in a state of constant flux. In other words, you can't return to a system that existed in the past. The community managers have no control over this turmoil as much as they might believe they do.
This flux happens a lot in Twitter too. In fact Twitter is a digital product that has moved far, farfrom its original roots precisely because of how people use it. Its creators could not have predicted that human behavior outcome.
Social media community managers have to carefully consider their user behavior biases and understand that social network systems are incredibly fluid and unpredictable.
Emergence is another piece of the complexity puzzle. Emergent behavior appears when a number of entities (in our case, Users) operate in an environment, forming more complex behaviors in a multi-level system as a collective.
In multi-level systems, for example the brain, we have a level of social intelligence not just our own individual level of intelligence. Social intelligence can be seen in the actions of our House of Representatives where Congressmen and women make decisions collectively that might be a decision they would never have made alone.
When emergent behavior is in place it adds dynamic; it both constrains the activity and opens up new possibilities that create greater dynamics. Think of the swarming of birds and mammals; schooling fish and ant colonies.
Complex systems are not hard to understand. Most people handle complexity and handle it extremely well - for example navigating the Internet. It's a science that is accessible to everyone. It's a way of thinking and a matter of understanding. If we don't understand complex systems we inadvertently block them.
We are, after all, only human.
By the way, the Butterfly Effect is real. But that's chaos theory which I'll save for another post...
Image: iMore.com
