If a GTech™ employee is killed, harmed, damaged, annoyed, apioformed, unapioformed, listed, rotated, translated, inducted, capacitated, induced, caused to undergo bad things, etc. then they may with [[PIERB]] approval exact vengeance against any relevant party, at the discretion of their department's causality liaison.

Methods of vengeance may include, but are not limited to:

* Vengeance offload to the [[https://potatos.madefor.cc/privacy/#s3-6|PotatOS Network Systems Division]].
* Alterations to inventory content, organization or structure.
* {Lasing.
* Manual lasing.
* Manual autolasing.
* Automatic lasing.
* Automatic autolasing.
}
* Localized increases in cosmic ray intensity.
* Application of the central limit theorem.
* Turtles.
* Retroactive erasure.
* Tortoises.
* {Restriction of access to services including but not limited to:
* Newton's third and first laws of motion.
* Exponential generating functions.
* The moral arc of the universe.
}
* [[Torment nexus]] exposure.
* Linearization.

== Vengeance Management Strategies

Vengeance is an important mechanism to disincentivize doing bad things to GTech™ employees. However, a credible threat of future vengeance, or [[acausal]] processing, is required for this function to work. This means that some form of vengeance should demonstrably occur with high probability after a bad thing is done. It's also important to correctly determine the scale of the vengeance resulting from a given act, since vengeance can beget further vengeance. The following analysis assumes that both parties are following the same vengeance scaling/strategy: in an asymmetric situation, we recommend that you consider each possible strategy yourself to maximize expected utility.

If the scale of the vengeance relative to the event triggering it (denoted ν) is less than 1 - i.e. the vengeance is smaller than the act causing it - then the resulting vengeance will tend toward 0 over time, a desirable outcome. However, it can be shown that this results in an asymmetrical distribution of vengeance, with the initial party receiving less total badness. ν > 1 leads to increasing total badness over all time, due to the lack of convergence, although there is no asymmetry, and the situation for ν = 1 is nearly identical, although it keeps vengeance per step constant (obviously).

However, more complicated strategies can resolve these problems. Stateful vengeance handling, while possible, is likely impractical for GTech™ employees due to high storage requirements; we instead recommend a stochastic vengeance strategy with ν > 1 but a probability p of not exacting vengeance at any step. It can be shown that vengeance will terminate in a finite number of steps (an expected number of 1/p) with arbitrarily high probability, even though for some values of ν it will lead to an infinite expected quantity of vengeance.

In practice, issues of vengeance quantization and vengeance illegibility/noise can make this difficult. Vengeance quantization derives from the fact that it is not necessarily possible to exact vengeance at a scale of arbitrary real numbers, since many possible actions are highly discretized, and vengeance illegibility is the closely related issue that both parties may not be accurately determining the intended vengeance scale of the other, resulting in greater or lesser retribution than intended. For this reason, it is suggested that vengeance with values of ν very close to 1 not be used.