
Probabilistic model for measuring success - nurall

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nurall
I am sure there are a lot of management books out there that define SUCCESS,
and I am sure they have their respective models to describe the probability of
SUCCESS.

I was just thinking about modeling the probability from a startup perspective.
Following are the assumptions this model makes -

1\. It takes 'n' number of iterations before an idea is a HIT, where n is
greater or equal to 1

2\. There is a probablity of success associated with each iteration, which
implies that there is also a probablity of failure associated with each
iteration.. duh!!

Model 1: Using geometric distribution

This is the most conservative and simplistic approach to model success

p(HIT) = Probability of success per iteration

P(SUCCESS) = p(HIT) x (1 - p(HIT))^n

This is simple in the sense that each new iteration does not carry the
benefits from the previous iteration

Model 2: Bernoulli's trials based model

This probably is more realistic of the two models

p(HITn) = Probability of success in the nth iteration = P(HITn-1) +/- deltaP

Similarly,

p(~HITn) = Probability of NO success in the nth iteration = P(~HITn-1) +/-
(1-deltaP)

where deltaP = Probability of iteration (n-1) increasing/decreasing the
probability of success in the nth iteration

P(SUCCESS) = p(HIT0) x p(HIT1) x ... x p(HITn-1) x p(HITn) = 1 - p(~HIT0) x
p(~HIT1) x ... x p(~HITn-1) x p(~HITn)

where p(HITn) tends to 1 AND p(~HITn) tends to 0, We need to be deterministic
if we have to be optimistic... ;-)

Any thoughts on this??? Do you think this model is right/wrong?? Do you have
your own models??? Please do share!!!

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rfrey
P(S) = i/A + j(N/3) + k(1/Q)^2 + x((1/GT)^3)

A = Average age of founders

N = Number of founders

Q = Quality of idea, 0-1

G = Graham Quotient: How hard you'll work, 0-1

T = Taleb Quotient: How lucky you'll get

i, j, k, x: Adjust to fit the sample of 5 famous startups in the news this
week

Repeat as required

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nurall
Good one!!! Did you model this? :-).. interesting... you are saying that if
P(S) is greater than 1, P(S) doesn't apply to that particular startup.. its
meaningless.. and they should stop wasting their time.. is dat a valid
argument to why your metric could be greater than 1?

