Difference between revisions of "Experimenting"

Revision as of 20:50, 16 August 2011

Exploratory actions to learn how the rules of cause and effect work in a game.

This pattern is a still a stub.

In all but the most simple of games, the complete consequences from actions performed are difficult to understand, but Experimenting can aid in understanding them. Actions may be tested for this reason simply because they have not been tested before if a player is willing to see a game session primarily as a learning period. Some games explicitly build experimenting into gameplay as puzzles. In these cases, the actions used to test possible solutions are usually reversible.

Linderoth^[1] points out that actions in games are either exploratory or performatory, where the former can be seen as a form of Experimenting.

Examples

Gameplay in Mastermind consists of one player guessing the correct combination of colored pegs. As the chance for guessing correctly based only on luck is very small, successful gameplay requires that the player combines the results from different guesses to draw conclusions and uses the guesses as experiments.

In Pontifex, players' goals are to build bridges and to learn how the physics model works. The players' have to experiment with how cable, joints, and metal beams interact.

The Incredible Machine game series lets players use a limit set of objects such as pipes, bowling balls, cats, candles, ropes, and balloons to try and reach goal states by making the objects interact with each other in certain ways.

The abstract game Zendo require players to set up arrangements of different colored pyramids to extrapolate the correct arrangement rules that a game master has decided to use for that particular game instance.

Creating potions in The Elder Scrolls III: Morrowind can be an experiment if the player is not an alchemist master, as not all effects of ingredients are known until one reaches that level.

Using the pattern

Experimenting relies on the presence of Gain Information or Gain Competence goals that players can try to complete by performing different variations of actions in a game, but this is not enough since harsh Penalties for failure may make players cautious.

When Gain Information goals lie behind the need for Experimenting, it also implies a presence of Imperfect Information. The Imperfect Information can be about the fundamental rules of cause and effect in computer games, about the long-term consequences of actions, or about the game state. For the first two cases, Predictable Consequences need to be combined with Limited Foresight for the Experimenting to be worthwhile while for the last case Experimenting can provide information about other players' Avatars, Units, Skills, Technology Trees, etc.

Gain Competence goals that are to support Experimenting have similar requirements as those for Gain Information goals but here it may also be that players need to experiment as part of learning their skills. However, the need for Experimenting can be mechanically encouraged by Combos - either because they are part of Hidden Rules (something sometimes done in Fighting Games) or to identify emergent effects of Construction.

The actual actions used in the Experimenting can be any; with Experimenting, players learnabout the actions by performing them. However, it is especially common with Experimenting when doing Puzzle Solving , finding possible Combos, or trying to find Achilles' Heels of Enemies.

In order to encourage Experimenting, the actions should not have severe consequences. In Quick Games, this may not be a problem if the outcome of the game has no Extra-Game Consequences, as a new game session can be started again. In other forms of games, Experimenting is encouraged by having Reversability of actions, either by allowing Save-Load Cycles or avoiding Irreversible Actions. This is typically easier to do in games with Dedicated Game Facilitators, as they can easily restore game states and not require players to do so. Actions that are closely related to Constructive Play, for example Construction, are better suited for Experimenting, because these actions are less likely to result in Competition. Providing Reversability in certain locations, which can be seen as a form of Safe Havens, allows for Smooth Learning Curves, as players can train and gain a certain level of Game Mastery before having to risk failure.

Competition puts players in situations where the effect of every action can have the potential of affecting the overall outcome, and this may work against players willingness to experiment. This can be compensated by making the Competitions contain Red Queen Dilemmas.

Can Instantiate

Randomness

Potentially Conflicting With

Randomness

Can Be Instantiated By

Arithmetic Progression, Safe Havens, Stimulated Planning, Testing Achievements

Enemies together with Achilles' Heels or Vulnerabilities

Single-Player Games together with Reversibility or Save-Load Cycles

Can Be Modulated By

Time Limits

Interface Aspects

While the exploratory actions in games described by Linderoth^[1] can be seen as Experimenting, some patterns can work against this since they remove the need for players to do these actions. Examples of this include Point of Interest Indications, Vision Modes, or Geospatial Game Widgets used for highlighting game objects or otherwise pointing out important game entities for players.

Consequences

The act of planning and Experimenting promotes Cognitive Engrossment in games, and both Casual and Challenging Gameplay can be modulated by how costly it is to do Experimenting. When Experimenting can have severe or costly consequences, they create Tension and can require Leaps of Faith but also encourage Stimulated Planning, especially if Non-Renewable Resources need to be used for the Experimenting. As the actions performed when Experimenting do not usually fulfill a goal in the game, the activity gives Illusionary Rewards, but these can be valuable for gameplay since they may let players discover the Strategic Knowledge which is possible to acquire.

The possibility of testing various ways of performing actions and trying to reach goals in games makes Experimenting support Smooth Learning Curves. Related to this, Experimenting in a game can aid in avoiding Analysis Paralysis, as players can try the effects and consequences of their ideas and plans rather than try to deduce the effects and consequences.

Rather obviously, Irreversible Events work against making players engage in Experimenting. H

Relations

Can Instantiate

Illusionary Rewards, Leaps of Faith, Randomness, Smooth Learning Curves, Stimulated Planning, Tension

Can Modulate

Casual Gameplay, Challenging Gameplay, Strategic Knowledge

Can Be Instantiated By

Arithmetic Progression, Gain Competence, Red Queen Dilemmas, Safe Havens, Stimulated Planning, Testing Achievements

Combos together with Construction or Hidden Rules

Enemies together with Achilles' Heels or Vulnerabilities

Gain Information together with Imperfect Information

Predictable Consequences together with Limited Foresight

Single-Player Games together with Reversibility or Save-Load Cycles

Can Be Modulated By

Time Limits

Possible Closure Effects

-

Potentially Conflicting With

Analysis Paralysis, Competition, Geospatial Game Widgets, Irreversible Events, Point of Interest Indications, Randomness, Vision Modes

History

An updated version of the pattern Experimenting that was part of the original collection in the book Patterns in Game Design^[2].

References

1. ↑ ^{1.0 1.1} Linderoth, J. (2010). Why gamers donʼt learn more - An ecological approach to games as learning environments. In proceedings of Nordic DiGRA 2010.
2. ↑ Björk, S. & Holopainen, J. (2004) Patterns in Game Design. Charles River Media. ISBN1-58450-354-8.

Acknowledgements

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