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.<p>I was just thinking about modeling the probability from a startup perspective. Following are the assumptions this model makes -<p>1. It takes 'n' number of iterations before an idea is a HIT, where n is greater or equal to 1<p>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!!<p>Model 1: Using geometric distribution<p>This is the most conservative and simplistic approach to model success<p>p(HIT) = Probability of success per iteration<p>P(SUCCESS) = p(HIT) x (1 - p(HIT))^n<p>This is simple in the sense that each new iteration does not carry the benefits from the previous iteration<p>Model 2: Bernoulli's trials based model<p>This probably is more realistic of the two models<p>p(HITn) = Probability of success in the nth iteration = P(HITn-1) +/- deltaP<p>Similarly,<p>p(~HITn) = Probability of NO success in the nth iteration = P(~HITn-1) +/- (1-deltaP)<p>where deltaP = Probability of iteration (n-1) increasing/decreasing the probability of success in the nth iteration<p>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)<p>where p(HITn) tends to 1 AND p(~HITn) tends to 0, We need to be deterministic if we have to be optimistic... ;-)<p>Any thoughts on this??? Do you think this model is right/wrong?? Do you have your own models??? Please do share!!!
P(S) = i/A + j(N/3) + k(1/Q)^2 + x((1/GT)^3)<p>A = Average age of founders<p>N = Number of founders<p>Q = Quality of idea, 0-1<p>G = Graham Quotient: How hard you'll work, 0-1<p>T = Taleb Quotient: How lucky you'll get<p>
i, j, k, x: Adjust to fit the sample of 5 famous startups in the news this week<p>Repeat as required