Designing Games When Your Processor is the Human Brain

Tricks for keeping the math simple enough for a board game

Computers are good with numbers. So in a computer game your numerical variables can span any continuous range (to quite high precision); you can easily utilize complex algebra; and track a huge number of variables simultaneously. But in a tabletop game, the players have to do the math themselves, and must track everything with physical pieces. This is especially tough in wargames, where there’s a large number of units.

This burden leads most game designs to simplify their mathematical models and to go granular: reduce the number of variables, and only allow a very small range of values. But this creates other ramifications. How do you maintain the mechanics you want, with very limited mathematical options?

As an example, consider hit points. In an RPG (or other game with few units), you might have 10 hit-points per unit. But in a miniatures game, you will scale this down to 1 HP for most units: each is either alive or dead. This eliminates any book-keeping for HP, except for a few, rare multi-HP units.

But this means damage must scale down too. In our imaginary RPG, a typical blow might do 1 damage, taking 10% of a unit’s HP. But this isn’t possible with single-HP minis. 10% of damage is 0.1; how can you apply that? If you round 0.1 up to 1, every attack destroys the defender. If you round down, attacks do nothing.

I see three main ways to address this, and still model a familiar kind of attack-damage dynamic.

Instead of doing 10% damage, you could have a 10% change of destroying a 1-HP unit. Easily implemented with dice usually: although you still have a limited range of values (only certain dice are manufactured), you buy roughly an order of magnitude smaller granularity.

Obviously, introducing randomness has major implications for how the game plays. If the number of units and actions is fairly low, singular lucky rolls may gain importance — and individual play-throughs will be determined more by luck than skill. Using dice also increases the “handling time” of actions — as the dice are physically collected, rolled, and counted — which also slows the game. But trading a tracking requirement for more handling time may be a trade you want to make.

Randomness and dice might be best (in terms of small numbers) at a sweet spot of game element complexity: not with a dozen units, and not with a hundred; rather, somewhere in between.

Here, an attribute of every unit limits what units can successfully attack one another. Or it could be an interaction of different attributes, like attack and defense types. There are two major sub-approaches:

The RPS approach is more flexible, and ensures that any unit can always be defeated. A strict hierarchy is trickier to pull off: there has to be some utility to lower-powered units, possibly some way for them to “punch above their weight.” Exceptions to their normal limitations, with special points or one of the other methods described here, could work. Extreme (exponential) expense for stronger units might also help.

A unit can only do damage in the right situation, which it creates with the right tactics. Two major examples:

Tactical choices become emphasized with this approach: setting up the right situation, and judging the enemy’s moves can’t be sacrificed for raw power. This could make the game harder for novices, and take longer to play out.

The above methods can obviously be combined too, sometimes very logically. For example, certain attacks are allowed only with the right position or unit interaction, while some others are possible only with the right random effect.

Unlike in a computer game, board game mechanics must be enacted by people. Small numbers are indispensable for making this feasible — but you may need every trick available to generate the kinds of dynamics you want, while still keeping things simple.

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