Hi everyone,

On Thursday, March 6, 2014 12:54:22 AM UTC+1, puzzler wrote:
>
> The potential problem with modeling it as a knapsack problem is that it 
> assumes that grant-giving is an all-or-nothing affair.
>

Yes, exactly this. I realized I omitted this when I woke up in the middle 
of the night...
 

> Another reasonable way to model it is to assume that if I'm given, say $80 
> out of $100 requested, then I have an 80% chance of going.  With such a 
> model, let's say I have a score of 5 and a grant request of $100.  Then, if 
> I'm granted $100, I contribute 5 points of goodness to the overall value 
> I'm optimizing.  So $1 allocated to me contributes 0.05 points of expected 
> goodness to the overall value.
>

Yep. A big part of the issue is that the *real* function that maps grant 
fraction to probability of coming is of course unknowable. A linear 
approximation is an excellent start. Once I have some test data I'll be 
able to tell for sure, but I imagine a closer approximation might look more 
like x**2 than x over [0, 1] (e.g. when I give someone 50% of their grant, 
the odds they'll come are closer to 25% than to 50%). Are Choco/Loco 
limited to linear programming?

Here's a gist, illustrating how to do this in Loco:
> https://gist.github.com/Engelberg/9379157<https://www.google.com/url?q=https%3A%2F%2Fgist.github.com%2FEngelberg%2F9379157&sa=D&sntz=1&usg=AFQjCNH72EGcBLDO4Y8k93Urdn9DD13wbQ>
>

Wow! That's great; thanks. I guess I'll go produce some test data and see 
how the different code does :-) 

thanks again
lvh

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