Librium – Persistent Checks

There are a lot of situations where a task is performed over a period of time rather than doing it and being done. While making a sandwich might only require one check, preparing a 5 course meal will take multiple. For the fact of it, even preparing a soup or stew might be enough to justify multiple checks. Librium has special rules for these situations.

These rules work exactly like combat rules. Whatever action is being performed will have a difficulty and a complexity. The complexity is how many total successes are necessary to complete the task. You can consider most simple tasks to have complexity 1; if you beat the DC and have +1 outcome then you’ve completed the task.

Task complexity determines how many successes you need

More complicated tasks have a higher complexity. They require more successes before they are complete. If a task is complexity 5 you’ll need 5 successes to complete it. You’re probably saying “That’s ridiculous! Against a DC 2 I’d need 7 success total to pass that! Even with my above average roll of 7 dice I’ll only succeed 7.85% of the time!”  Well you are right, ridiculously-good-math-person. It would be very difficult for you to succeed this in one check.  That is why you are going to do a persistent check

In a persistent check, each check takes a certain amount of time and you will add the total number of successes over all of the attempts. the DC is applied each time, however, which provides a consistent difficulty; whatever you are doing doesn’t suddenly get easier just because you kept doing it. Now you make that 7dS check repeatedly; You get 4 the first time, 3 the second time, 5 the third time, and 3 the fourth time. Keep track of your outcome as you go. That’s +2 outcome, +1 outcome, +3 outcome, and +1 outcome. After 4 checks (and at one minute per check, four minutes) you are successful! Wonderful!

This is great, but one problem. You can just keep doing this forever. Why stop? You can do anything if you have enough time. Hell, an art student could make a perfect replica of the Mona Lisa if you give them enough time.

Persistence slows progress

To prevent such a situation, every time you make a check you suffer a cumulative -1 outcome penalty to the check. This means -1 outcome on the first check, -2 on the second, -3 on the third, etc.  This gives a much more apparent upper limit to how complex of a task you can do. Now it is almost as difficult as the original, but not quite as bad.

Persistence can be stressful

This is optional to the player. Maybe you really want to do a great job and you’re willing to risk more for it. If you choose, you may suffer 5 stress to ignore -1 outcome penalty. You can suffer this stress as many times as you want per check. So if you are at -4 outcome, you can suffer 10 stress to reduce it to -2 outcome or even 20 stress to reduce is to -0 outcome.

The idea behind this is that certain tasks can be completed easier or faster if you are willing to put up a fight for them.

Tools make things easier

Just like weapons make it easier to dispatch your enemies, I see other tools being used to make these persistent checks easier. For example, a lockpick might have complexity 2 (or another term.) meaning each outcome is worth 2 successes after the DC is met. So with our original example of 7dS, (each check with outcomes of 4, 3, 5, 3), The complexity damage would be 4, 2, 6, 2. Only 3 checks this time, though that last one would be quick (improved by time since quality and cost can’t be improved).

Potential Problems

I see a few potential problems with this idea and thus is needs testing.

  • The stress may be too high. With my current math, a character has about 40 endurance of each value meaning they’ll likely not want to take too much stress on repeated checks before stopping.
  • The stress may be too low. Especially with things that are not under any pressure, there is no reason not to stress yourself to the maximum, even to the point of causing trauma, if it means you can get a better end result.

There are probably other problems.  We’ll have to play and find out!

Michael McElrath

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