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A Circular Model of Gameplay
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A Circular Model of Gameplay

by Tom Heaton []

February 23, 2006 Article Start Page 1 of 2 Next


One problem facing game designers today is that there is no commonly held theoretical basis to their craft. This has not stopped great games from being created: designers don't need theory to design games any more than children need a knowledge of grammar in order to talk. But a theoretical base would have considerable advantages: communication would be easier, common mistakes could be avoided and designers would have an analytical framework and set of tools at their disposal. There is no consensus on even the most basic theoretical concepts, such as game and gameplay.

The term “gameplay” is ubiquitous throughout the games industry: everyone uses it on a daily basis. The term also has considerable reach beyond developer jargon: hardcore and even casual gamers are increasingly familiar with the term through game reviews. Most people could agree on a rough definition along the lines of “the gamey bit of the game”. But disagreement will quickly arise as to what gameplay actually is, what its elements are, why one feature contributes gameplay and another doesn't.

Rather than working towards a definition, this article develops a theoretical model of how gameplay works in practice. The model is abstract and aims to be universal – applicable to any type game – not just video games. Its starting point is that gameplay is a property of all games and is a set of interactions between the player and the game. Although gameplay is something that is normally ascribed to a game (“Game X has great gameplay”), the model is focussed on the player and the player's actions. If a game remains boxed, then there really is no gameplay: only when it is linked up to a human agent is there gameplay.

The model contains only two components – the player and the game. The player is a human who has elected to play. The game is a system that the player interacts with - crudely speaking, everything that is not the player is part of the game, including other players. All information about the game is conveyed to the player through clearly defined output channels and all the player's actions in the game are carried through clearly defined input channels.

Gameplay occurs when the player interacts with the game. But in this model, the interaction is not random, it is a flow of information from the game to the player and from the player back to the game. The interaction is circular – the flow is always in the same direction and no stage can be missed. The simplest graphical formulation of the model is shown in Fig 1.

Figure 1

Even when applied to a multiplayer game, the model uses only one player and the game. The game can be seen as a system that encompasses a large number of internal processes and states, some of which may be human. If a full analysis of a multiplayer game is required then it may be necessary to do multiple applications of the model to the same game, something which is quite intuitive: a goalkeeper's gameplay experience differs significantly from a midfielder's, for example (or a pitcher's to a batter's, if you like).

The Player and the Game

Our model is really too simple to be of use to anyone in its current state. We need to start filling in the detail.

Three things must happen in order for there to be gameplay:

  • The player must get information about the state of the game.
  • The player must be able to affect the game, creating new game states.
  • New game states must be communicated to the player prompting further actions

In addition in almost all types of game:

  • The game creates new states without the player's input.
The player observes.

To engage in gameplay, the player must be able to observe the game. Observation informs the player's actions. Without observation the actions are meaningless. Imagine playing a video game with the vision and sound switched off. The player can still interact with the game: the game has no awareness of the player's sensory deprivation and will continue as normal. New game states will be arrived at, the player may make decisions which affect the final score, but clearly there is no gameplay. The player has no idea what is going on in the game, any actions they make are uninformed and effectively meaningless.

The player can affect the game.

The player's actions must affect the state of the game. If the user of a system is able to observe but not to act then they are observing a system from the outside – they are watching a film, or microbes under a microscope. There is no interaction.

Snakes and Ladders is only suitable for toddlers because the player cannot influence the game in any way - there is no gameplay, only an imitation of interaction. The player appears to act by rolling the dice but the result is random and the player is compelled to act on it. At no point does the player make a decision. It's possible and not uncommon for players to take goes for each other without any affect on the game.

Snakes and Ladders could easily be improved as a game for adults (I dimly remember that it's quite exciting for toddlers as is). We could introduce a second die, for example. The player rolls both and chooses one as a move. This seems like a trivial choice but in fact with the right balance of snakes, ladders and empty squares this could provide quite compelling gameplay as the players play the odds. With this single change in the rules, player influence is introduced and there is gameplay.

New game states restart the cycle.

The model cycles. Gameplay is not a one-off action – it is an activity. The experience is one of repeated interactions by the player. Usually there are a relatively small set of interactions that are repeated many times, each time slightly differently or combined in a different way. In a racing game, we repeatedly steer, accelerate and brake. Tennis players repeatedly hit the ball with a racket. In a platformer, players walk, run, jump, shoot, collect.

The changing state of the game constantly prompts new actions from the player. The gameplay typically goes through many cycles of observation and action until an arbitrary endpoint is reached – a fixed time, a certain score, an objective reached. (Games are an activity designed to take place over time: therefore the end points of all games are always arbitrary – we could always play for longer, to a different score, reach another objective.)

When the cycle breaks down, there is no gameplay.

Why does the cycle sometimes break down? It could happen at any stage of the cycle and the model prompts some areas which can be examined. But the most common is that the new game states are not sufficiently interesting to keep the player's attention – the player has been in similar situations before, or the game is too easy or dauntingly difficult. The skills to capture and maintain a player's interest are well out of the scope of this article. It's enough to say for now that unless we can hold attention then there will be little gameplay and the player will stop playing.

The game creates its own states.

It's very typical for new states to be created by the game itself. This can sound counter-intuitive for some games until we remember that this model forces a single viewpoint: other players are part of the game. The internal system by which chess creates its new state might be a second human player (who can have the same model of gameplay applied to them) or it might be game AI choosing the move. From the player's point of view – in terms of the gameplay – this is irrelevant. What is relevant is that a new state is created. (In terms other than gameplay, whether the internal system is human or machine is highly relevant).

If the game doesn't create states, if new states are only brought about by the player – then what we have is perhaps more properly a puzzle than a game. The player is entirely in control of events, though the consequences of actions may not be immediately apparent.

As well as through other players, new states can also come about through random number generation– machine generated, the throw of a dice, the spin of a bottle. Or through a pre-ordained order – as when a new card is drawn, or in alphabet based games (“I love my love…”) for example.

As a generalization, the new states generated by the game will offer a greater incentive to act than the new states created by the player. The confrontational nature of most 2-player games is set up to force the players to repeatedly act, each trying to create a state of advantage to themselves.

Better Model

We can now create a new diagram showing this level of detail (Figure 2). Note that the player and the game are separated by an interface layer consisting of an input and an output. This is very easy to understand with video games where the input and outputs are objectified. The interface is less easy to conceptualize in board games, and much more difficult in games like football. It's important to show the interface in the model because often gameplay problems (of the player not observing the game properly or of the player's actions not affecting the game state) are problems of the interface rather than the game mechanic. We need to recognize that the interface is part of the cycle, and is not unproblematic.

Figure 2

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