The Attribute System

    This is a first draft for an attribute system.

    Attributes have a value range from 0 to "average" to "superhuman" and beyond. The scale is irrelevant, for instance we can say that the average is 50, so the scale for practical purposes would go from 0 to 100, being 50 the average value, and allowing attributes of more than 100 for special cases.

    For the examples of this system we will use the scale 0-10-20, typically found in roleplaying games. Scaling to another range should be immediate. For such a system the practical values are in the range of 3-18 (which is the range of the roll of 3d6) and the average is 10.

    The attributes have several uses, for instance strength can be used to determine the load a character can carry, and willpower can be used to resist hostile magic spells. We will define some of these uses but we'll left open the system so they can be used in other ways.

Interaction with the Skill System

    The Skill System determines the knowledge a character has in several fields, while attributes define statistics of the character, they have no "learning" component involved. Sure, you can train your strentgh and other attributes, but that is not "learning" per se, you simply stress those attributes and your body or mind adapts by improving. Attributes can be raised, but not in the same way that skills are (this is to be covered later).

    The relationship between skills and attributes should be that attributes modify the Effective Skill Value that is going to be used in an action The rationale behind this is that, given the same amount of learning and practical knowledge, a person with higher attributes has a natural easyness to perform tasks related to that attribute.

    Examples are: a dexterous person can play guitar fairly well with several lessons, while a non-dexterous one finds it harder, though he knows the same basic rules than the other. A more intelligent student needs to study less to pass an exam, because he can apply better his knowledge. A more perceptive person does not need to be sistematically paranoid: he can detect when something is wrong just because he is naturally more aware of his surroundings.

    To model this, the most simple mechanism is to apply an attribute modifier to the Effective Value of a Skill that is going to be used to perform an action. The simplest way is to consider that an attribute of "0" modifies the skill by reducing it to 0%, that the average attribute lets the character use 100% of its skill and that the superhuman attribute lets the character use 200% of its Effective Skill Value.

    Very Easy Example:

3d6 rangePercentile
00 %

    So, for instance if both Theo the Nimble and Don the Klutz start training lockpicking at the local thief guide, and Theo has dexterity 13.5 and Don has Dexterity 9.2, if both train enough to have 63.84 Skill Value in lockpicking, Theo would use its skill as if it were 86.18 and Don would only use it as if he had 58.73.

Cost of attributes:

    The cost of attribute increasing should be highly exponential, at the moment of character creation and also when increasing attibutes by gameplay. Logaritmical curves are fine for this, but they should be much more steep than for the skill learning process. High attributes should be a very very rare thing, and if so, the characters with a very high attribute should have disadvantages to compensate for it.

Increasing attributes:

    Attributes should increase when they are trained, i.e. when they are used intensively. It depends on the attribute, but some are easier to train, and that can be achieved by just repetitive actions (example: you can increase your strenght by lifting weight quite easily). Others should be harder to increase, and perhaps only by using abilities that train that attribute (example: by practicing and improving skills based on dexterity you can become very dextrous).

Attributes increased by direct actions:

    Some attributes, like strength or stamina, can be increased by direct action, like lifting weight or running for long periods of time. In that case, the appropiate action should give "attributeXP" to the attribute which are directly converted into attribute value using a logarithmical formula with a steep progression (immediately or, better, over time).

    In this case the attribute behaves exactly as if it were one skill that had no parent. It gets all the experience for itself.

Attributes increased by skill development:

    Attributes like intelligence or dexterity can be increased by development of skills that use them (because there is no easy way to put your dexterity or your intelligence to a "pure" stress test like you can do with strength). Furthermore, some of these attributes can have several sub-attributes like "music intelligence", "mathematical intelligence" or "nimbleness" vs "manual dexterity". In this case, to increase an attribute it should be the Skill System who gives the Attribute System the attributeXP that will increase it, based on the experience gained on the relevant skills.

    It is left for the Skill System the actual way to do this, but here we propose one: if the Skill System implementation is modelled as a tree, you can "abstract" experience from the upper nodes (1st level nodes) related to an attribute, and give that experience to the attribute as attributeXP, but without removing that experience from the skill.

    For instance, if the 1st-level skill of "craft" is related to dexterity, and it gains 10 xp, it can give 1 xp to dexterity, but it does not leave craft with 9 xp, craft gets its whole 10 xp and dexterity gets 1.

    The reasonale behind this is that skill experience represents learnt knowledge, and attribute xp is not knowledge but instead represents your body or mind adapting to improve, so it is an additional improvement, apart from the learnt one.

    How much xp does these skills give to attributes is left to the Skill System, but it is recommended that it be an algorithm similar to xp abstraction for parent skills, having in mind that attribute progression should be much more slower than skill progression, so either the amount given is smaller or the logarithmic curve for improvement is steeper.