There are a number of ways in which games can be classified or described as systems. In regard to their composition, they can be seen as collections of inter-related parts; rules, mechanisms, physical components and so on. From another perspective, games can be seen as sharing many of the characteristics as other systemic entities. For example, you could describe the ‘magic circle’ of play as clearly marking a system boundary, or you can see the rules and order of play as comprising an ordered structure for the game system.
It is in looking at the behaviour of the game that it really comes into its own. In many ways, a game does not actually exist until it is being played. Although games designers often talk about designing experiences, in reality they cannot do this. They can use their domain skills and expertise to create and design system components, and characteristics, that they believe will elicit specific behaviour and experience (and of course, they should also have thoroughly tested these beliefs), but the experience of the game can only happen with the active participation of a player.
A system displays emergence when it can be seen that the system as a whole exhibits characteristics or behaviours which cannot be seen in any of its constituent parts. Life, for example, is considered to be emergent from the biological components and phenomena of plants and animals (as well as the physical and psychological). A single celled organism is ‘alive’ but the molecules which constitute it are not.
Inextricably linked with the concept of emergence is complexity. We can use a reductionist approach to understand the structure and function of a component, but this gives us no idea of how it will behave when combined with other components at greater levels of complexity. To give another example from biology, we can look at the components and function of a single cell within a body, and understand them very thoroughly, but the emergent properties of that cell when combined with other cells in a tissue, cannot be deduced by studying that cell in isolation. Similarly, we cannot look at the tissue and deduce how its component cells are arranged or how they function. How much less, therefore, could we understand the whole organism, of which these are tiny parts, or the societies into which those organisms gather?
To return to games – in both designing and playing them – this presents us with some interesting problems and opportunities – which can be usefully compared as opposite sides of the same coin, as below…
It makes games design a complex activity. When designing games, we need to iteratively and thoroughly test the experiences and behaviours that will emerge from the systems we are constructing. There is an additional burden of time and effort needed for player testing, as compared to the creation of other cultural artifacts such as written or cinematic stories, because much of the emergence derives from the active participation of a player, who is not simply consuming, but is co-creating the experience.
Conversely, this can make the creation of complex experiences much easier than it might be if one needed to fully create an experience to be consumed by another. Simple rules and characteristics can combine to create complex behaviours and narratives which do not therefore have to be themselves designed or created. An often-cited example of emergence is the behaviour of cellular automata in ‘The Game of Life’. A grid contains ‘cells’, squares which are either ‘live’ or ‘dead’ and which interact with their eight neighbours using the following rules:
- Any live cell with fewer than two live neighbours dies, as if by underpopulation.
- Any live cell with two or three live neighbours lives on to the next generation.
- Any live cell with more than three live neighbours dies, as if by overpopulation.
- Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.
An initial seed pattern is entered by a ‘player’ which then plays out, either dying completely after a few ‘generations’ or settling into a repeating pattern of entities which live and continue to reproduce until the game is stopped.
Although this is an excellent example of complex emergence from simple rules, it is effectively a zero-player game, and does not represent the kinds of experiences we wish to build for real human players. So better examples of emergent gameplay might be the relatively simple frameworks of character formation and actions, determined by dice throw which make up RPGs such as Dungeons and Dragons, or the open-ended play of ‘sandbox’ games such as Minecraft, where the only ‘win-states’ are those invented by the players themselves and which may not even have been imagined by the games’ designers.
Even under the most thorough testing protocols it is unlikely that all eventualities will emerge, and the more complex your game system, the more likely it will be that there will be unintentional emergence in play. Even if we disregard actual faults in the game (such as ‘glitches’, which could be seen as a type of unintentional emergence), there is often plenty of scope for players to change game objectives, or to use in-game objects in ways they were not intended to be used.
If you are unlucky, this can lead to the game getting a reputation for being ‘broken’. But more positively, it can lead to entire new genres of creativity or styles of play. Notable examples of this include ‘speed running’ which has maintained the cult status of some games for decades, and Machinima, where in-game action is recorded to make ‘movies’ with narrative that was not part of the original game.
From the perspective of games-based learning, emergence is one of the characteristics of games which makes them so suitable for learning about complex systems, systemic issues, and situations in which exact prediction and determinism are not possible. We can simulate, for example, the emergence of racial or economic neighbourhood segregation, from individual behaviour which would not necessarily be considered particularly ‘racist’ or ‘snobby’ – a simple ‘rule’ where cells in an automata game exhibit a ‘preference’ for being next to cells like themselves. In the above Excel simulation we can see the result of running nine generations of the game, where the cells began randomly spread, and each cell would be ‘satisfied’ so long as 50% of its neighbours were like itself – which also means it did not mind if the other 50% were unlike – not particularly prejudiced behaviour.
We can demonstrate the interdependency of a functioning ecosystem (and how easily that function can deteriorate into complete collapse, given human interference), using simple transfer of tokens from one element (player) to another.
Systems, simple and complex, are everywhere, and provide endless inspiration to create these simple ‘toy games’ and thought experiments as well as complex player-centred experiences
Sarah Le-Fevre is a learning professional who specialises in games-based learning and systems practice for learning design. She is also a Lego® Serious Play® facilitator. A real board games nerd, she is considering having her floors reinforced to support the ever increasing weight of the boxes. Sarah lives in Oxfordshire with her husband, younger daughter, a beautiful Bengal cat and two rats. Sarah is the editor of Ludogogy Magazine. Contact her at email@example.com