Playing with emergence is like gardening

It’s been a while since I finished reading Steven Berlin Johnson’s Emergence. I picked up the book because ever since I started thinking about what IxDs can learn from game design, the concept of emergence kept popping up.

Johnson’s book is a pleasant read, an easy-going introduction to the subject. I started and finished it over the course of a weekend. There were a few passages I marked as I went a long, and I’d like to quote them here and comment on them. In order, they are about:

  1. Principles that are required for emergence to happen
  2. How learning can be unconscious
  3. Unique skills of game players
  4. Gardening as a metaphor for using (and making) emergent systems

A cheat sheet

Let’s start with the principles.1

“If you’re building a system designed to learn from the ground level, a system where macrointelligence and adaptability derive from local knowledge, there are five fundamental principles you need to follow.”

These principles together form a useful crib sheet for designers working on social software, MMOGs, etc. I’ll summarise each of Johnson’s principles here.

“More is different.”

You need to have a sizeable amount of low-level elements interacting to get patterns emerging. Also, there is a difference between the behaviour you will observe on the microlevel, and on the macrolevel. You need to be aware of both.

“Ignorance is useful.”

The simple elements don’t have to be aware of the higher-level order. In fact, it’s best if they aren’t. Otherwise nasty feedback-loops might come into being.

“Encourage random encounters.”

You need chance happenings for the system to be able to learn and adapt.2

“Look for patterns in the signs.”

Simply put, the basic elements can have a simple vocabulary, but should be able to recognise patterns. So although you might be working with only one signal, things such as frequency and intensity should be used to make a range of meanings.

“Pay attention to your neighbours.”

There must be as much interaction between the components as possible. They should be made constantly aware of each other.

Now with these principles in mind look at systems that successfully leverage collective intelligence. Look at Flickr for instance. They are all present.

Chicken pox

I liked the following passage because it seems to offer a nice metaphor for what I think is the unique kind of learning that happens while playing. In a way, games and toys are like chicken pox.3

“[…] learning is not always contingent on consciousness. […] Most of us have developed immunity to the varicella-zoster virus—also known as chicken pox—based on our exposure to it early in childhood. The immunity is a learning process: the antibodies of our immune system learn to neutralize the antigens of the virus, and they remember those neutralization strategies for the rest of our lives. […] Those antibodies function as a “recognition system,” in Gerald Edelman’s phrase, successfully attacking the virus and storing the information about it, then recalling that information the next time the virus comes across the radar. […] the recognition unfolds purely on a cellular level: we are not aware of the varicella-zoster virus in any sense of the word, […] The body learns without consciousness, and so do cities, because learning is not just about being aware of information; it’s also about storing information and knowing where to find it. […] It’s about altering a system’s behaviour in response to those patterns in ways that make the system more successful at whatever goal it’s pursuing. The system need not be conscious to be capable of that kind of learning.

Emphasis on the last sentence mine, by the way.


Johnson writes about his impression of children playing video games:4

“[…] they are more tolerant of being out of control, more tolerant of that exploratory phase where the rules don’t all make sense, and where few goals have been clearly defined.”

This attitude is very valuable in today’s increasingly complex world. It should be fostered and leveraged in areas besides gaming too, IMHO. This point was at the core of my Playing With Complexity talk.


“Interacting with emergent software is already more like growing a garden than driving a car or reading a book.”5

Yet, we still tend to approach the design of systems like this from a tradition of making tools (cars) or media (books). I not only believe that the use of systems like this is like gardening, but also their creation. Perhaps they lie in each other’s extension, are part of one never-ending cycle? In any case, when designing complex systems, you need to work with it “live”. Plant some seeds, observe, prune, weed, plant some more, etc.

I am going to keep a garden (on my balcony). I’m pretty sure that will teach me more about interaction design than building cars or writing books.

  1. The following quotes are taken from pages 77-79. []
  2. This reminds me of Nassim Nicholas Taleb’s The Black Swan, wherein he writes about maximising your chance of having serendipitous encounters. []
  3. Taken from pages 103-104. []
  4. Page 177. []
  5. Page 207. []

Playing With Complexity — slides and notes for my NLGD Festival of Games talk

When the NLGD Foundation invited me to speak at their anual Festival of Games I asked them what they would like me to discuss. “Anything you like,” was what they said, essentially. I decided to submit an abstract dealing with data visualization. I had been paying more and more attention to this field, but was unsuccessful in relating it the other themes running through my work, most notably play. So I thought I’d force myself to tackle this issue by promising to speak about it. Often a good strategy, I’ve found. If it worked out this time I leave for you to judge.

In brief, in the presentation I argue two things: one — that the more sophisticated applications of interactive data visualization resemble games and toys in many ways, and two — that game design can contribute to the solutions to several design issues I have detected in the field of data visualization.

Below are the notes for the talk, slightly edited, and with references included. The full deck of slides, which includes credits for all the images used, is up on SlideShare.

Hello everyone, my name is Kars Alfrink. I am a Dutch interaction designer and I work freelance. At the moment I work in Copenhagen, but pretty soon I will be back here in Utrecht, my lovely hometown.

In my work I focus on three areas: mobility, social interactions, and play. Here is an example of my work: These are storyboards that explore possible applications of multitouch technology in a gated community. Using these technologies I tried to compensate for the negative effects a gated community has on the build-up of social capital. I also tried to balance ‘being-in-the-screen’ with ‘being-in-the-world’ — multitouch technologies tend to be very attention-absorbing, but in built environments this is often not desirable.1

I am not going to talk about multitouch though. Today’s topic is data visualization and what opportunities there are for game designers in that field. My talk is roughly divided in three parts. First, I will briefly describe what I think data visualization is. Next, I will look at some applications beyond the very obvious. Third and last, I will discuss some design issues involved with data visualization. For each of these issues, I will show how game design can contribute.

Right, let’s get started.

Continue reading Playing With Complexity — slides and notes for my NLGD Festival of Games talk

  1. For more background on this project please see this older blog post. More examples of my recent work can be found in my portfolio. []

Learning about emergence from games

A game of Go

I’m still trying to get a grip on why I think games are such a good reference point for IAs and IxDs. I’ll try to take another stab at it in this post. Previously I wrote about how games might be a good way to ‘sell’ algorithmic architectures to your client. Even if you’re not actively pushing your clients to adopt ideas such as on-the-fly creation of site navigation, sooner or later I’m convinced you’ll find yourself confronted with a project where you’re not asked to develop a definitive information architecture. Instead you’ll be charged with the task to come up with mechanisms to generate these procedurally. When this is this case, you’re truly facing a second-order design problem. How can games help here?

One of the defining characteristics of games are their complexity. A few years ago Ben Cerveny gave a brilliant talk on play (MP3) at Reboot 7.0 and mentioned this specifically — that much of the pleasure derived from game-play is the result of the player coming to terms with complex patterns. This complexity is something different from pure randomness and most certainly different from a ‘mere’ state machine. In other words, games show emergence.

There are many examples of emergent systems. The Game of Life springs to mind. This system isn’t really a game but shows a remarkable richness in patterns, despite (or maybe because of) the fact that it is based on a set of deceptively simple rules (which apparently took its creator, John Conway, over 2 years to perfect!) The thing is though, The Game of Life is not interactive.

A wonderful example of a complex emergent system that is interactive is the real game Go. It has a set of very simple rules, but playing it well takes a huge amount of practice. The joy of playing Go for me (an absolute beginner) is largely due to discovering the many different permutations play can go through.

So getting back to my earlier remark: If you’re convinced you’ll need to get a better handle on solving the second-order design problems presented by the design of complex emergent systems, games are an excellent place to start learning. They are emergent first and interactive second, the perfect twin to the web environments we’ll be shaping in the future.