Solving expected value problem with logistic function

AI Thread Summary
The discussion centers on solving an expected value problem involving a logistic function, where the goal is to determine the variable x that achieves a desired expected value z. The expected value is calculated using a probability of success based on the logistic function, leading to a complex equation that combines rewards and losses. Attempts to find an explicit solution in Matlab were unsuccessful, indicating the need for alternative methods. Participants are exploring simplification strategies and potential solutions for the probability function p(x). The conversation emphasizes the challenge of deriving a solution for the expected value using the logistic function framework.
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I have an expected value problem where z is a desired expected value and I want to reach and x is an amount I can vary.

There is a probabilty of success based on a logistic function ρ(x) with a reward of λx and failure with a probability of (1-ρ(x)) and loss of x. I am trying to solve for the correct value of x to reach an expected value z.

So:

z = p(x) \lambda x - (1-p(x)) x

z = \frac{\lambda x}{1+ e^{-a-bx} } + \frac{x}{1+ e^{-a-bx} } -x


I tried solving in Matlab but it says there is no explicit solution and I haven't been able to solve by hand.

What would be the next course of action to solve this? Is there a way to simplify?
 
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It looks as if ##p(x)=\dfrac{1}{1+e^{-a-bx}}## is a solution.
 
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