I'd like to associate modifiers with keywords.

Easier to explain with an example: listing keyword 'friend' in the
'Attributes' column applies +20% likelihood to the 'Invite to Dinner'
column. Let's say keyword 'wife_dislikes' is *also* listed in the
'Attributes' column, so then a separate -30% modifier is applied to
'Invite to Dinner'. When I calculate the table, the calculation looks
at the list of attributes, and applies the appropriate modifiers to
the appropriate columns. (And it's all cumulative, so the cumulative
modifier in our example is -10%.)

Which part are you having a problem with ?
You might look at vlookup as a start...

Tim

wrote in message
...
I'd like to associate modifiers with keywords.

Easier to explain with an example: listing keyword 'friend' in the
'Attributes' column applies +20% likelihood to the 'Invite to Dinner'
column. Let's say keyword 'wife_dislikes' is *also* listed in the
'Attributes' column, so then a separate -30% modifier is applied to
'Invite to Dinner'. When I calculate the table, the calculation looks
at the list of attributes, and applies the appropriate modifiers to
the appropriate columns. (And it's all cumulative, so the cumulative
modifier in our example is -10%.)

Thanks Tim. Yes, that's exactly what I use. In particular, something
like this:

=C2+(C2*VLOOKUP(B2, G1:H3, 2, FALSE)/100)

(Column B is the "attribute name" column, and C is my "base value"
column. G1:H3 is my modifiers table: G says the modifier name, H says
the percentage bonus.)

My question: I want to be able to name more than one attribute in an
attribute cell. Is it possible for me to "tokenize" a comma-separated
list in a cell, and iterate over each of the modifier names, applying
each effect cumulatively? (My way above only allows one attribute name
per cell in the B column.)


On Dec 24, 5:55*pm, "Tim Williams" <timjwilliams at gmail dot com
wrote:
Which part are you having a problem with ?
You might look at vlookup as a start...

Tim

wrote in message

...

I'd like to associate modifiers with keywords.

Easier to explain with an example: listing keyword 'friend' in the
'Attributes' column applies +20% likelihood to the 'Invite to Dinner'
column. Let's say keyword 'wife_dislikes' is *also* listed in the
'Attributes' column, so then a separate -30% modifier is applied to
'Invite to Dinner'. When I calculate the table, the calculation looks
at the list of attributes, and applies the appropriate modifiers to
the appropriate columns. (And it's all cumulative, so the cumulative
modifier in our example is -10%.)