Depth and complexity are core concepts for the successful design of a game. However, both of them have become overloaded in meaning and sometimes feel indistinguishable. This article goes over my current thinking on these two subjects and how to use them as a lens to evaluate design.
These are only relevant to games where decision making is key. It is critical to note that some games are better for keeping the decision making light and letting players focus on other parts of the game.
Complexity is the amount of knowledge a player must have to make a decision.
Complexity Changes With Player Experience
It is very important to note that this changes for a player over the course of a game. Early players can often completely ignore areas of the game’s rules as they can beat the challenges the game poses with only a fraction of the options the game gives them. Professional magic players on the other hand are expected to know not only the rules of the game, but all of the playable cards in a set and the decks that their opponents can be expected to play.
It is also worth noting that complexity is close to a one time event for a given set of data. Once players have internalized a game, they do not need to worry about the game’s complexity again until new data is created for them.
To evaluate complexity, simply look at the amount of data and rules that you expect a player to need to know in order to make a decision at that stage of the game. As noted above, calculating deltas is critical here. A game with good onboarding can get away with a lot more complexity than one that simply frontloads an infodump.
Examining Chess and Go
Chess and Go are games that we typically praise for their low complexity. Both of them have very few rules and very little content, so the raw amount of knowledge is very low and it is easy to go from nothing to being able to make legal moves. However, they both suffer from a large amount of analytical complexity immediately after the player learns the basic rules. In order to make a play, players must expend a lot of effort to derive the optimal solution. This is reduced by heuristics, but these heuristics are often hard to derive from the base ruleset and often hard to know and apply.
This is a more satisfying complexity for a player to encounter than arbitrary rules and interactions, but is still complexity. Essentially, for early players, all the information they need to play is the base rules as they are only concerned with making legal moves, but as they pass that level and look to start winning, their complexity increases very sharply as players try to figure out the best move by playing out the game in their heads without the aid of heuristics. While their complexity is very low when learning how to play and quite manageable when players have plenty of experience, the points in between are quite complex.
Depth is the ability of a player to make better decisions. This is of course only as important as a player thinks it is. If your player does not feel he has the ability to make better decisions then he will leave the game, no matter how true the belief is.
This definition is both binary and dependent on the player. We can measure depth more meaningfully by looking at the number of players who will feel that the game still has depth and the length for which they feel this to be true. Basically, you should look at the time you expect it to take until your players figure out a degenerate strategy, or even a strategy they feel is degenerate. Note that this time can be longer than the expected play time for many players and that all numbers greater than her play time are the same from the perspective of an individual player.
Interchangeability of Depth and Complexity
A key point to complexity is that if a player is not losing to a piece of the game, it is quite possible for the player to ignore it. When a player loses to a rule or piece he did not know about, that is a point where he can then make better decisions the next time. To play better, the player needs to do nothing more than keep a little additional information in mind. I feel that this is where a lot of the confusion between the two concepts comes from. Technically, this is depth as well as there is the scope for the player to make better decisions next time. It is just not the only way to ensure depth.
The benefit of depth through complexity is that the player is almost guaranteed to be able to improve. Knowing a little additional data is an actionable that most players are capable of. The flip side is that the player does not get to feel smart when improving. Addtitionally, if a player feels that she made better decisions than her opponent but lost due to the opponent knowing something more then the loss can feel cheap and given enough of these, she will start to feel that the game rewards mere knowledge instead of skill. This isn’t necessarily a bad thing, a solid game can be made with that principle, but it is worth keeping in mind.
A couple of cheap ways to control the amount of complexity in your game are:
- Converting data into rules is a starting point. The worst case here is that you make no change and people still remember the data that emerges from the rules instead of remembering the rules themselves.
- Showing the player data and keeping it accessible to the player is an important way to reduce complexity and also just player frustration.
I personally like to slide all of these as far as I can to low complexity and then add content or rules if I feel that more is required, but that can be quite expensive.
Depth and complexity are both concepts that I think every designer has some degree of intuitive feel for, but the exercise of laying out metrics and definitions always feels worth doing. As Sid Meier said, games are just a series of interesting decisions and hopefully this will help you keep your decisions interesting.