Mathias Brandewinder on .NET, F#, VSTO and Excel development, and quantitative analysis / machine learning.
by Mathias 14. January 2010 13:41

In the previous installment, we discussed the dynamics of a (very) simple network of queues, and showed how much extra capacity was required to accommodate the build-up of population inside the queue, based on two factors: the rate at which people enter and leave the queue.

Today, we will look at a related question. Last time we determined the expected queue size at equilibrium, given the flow of people into the queue. This time, we want to consider the reverse problem: if you knew how many people are in the queue at equilibrium, what population breakdown would you expect between the two queues?

The question may sound theoretical – it isn’t. If you knew the total size of a market, the relative preferences of consumers between the products, and how long it takes them to replace their product, then determining how many consumers would be using each product at any given time is equivalent to the question we are considering.

thechoice Let’s illustrate on a fictional example. Imagine there is a disease, which can be treated two ways – using a blue pill, or a red pill. Doctors prescribe the blue pill to 25% of the patients, and the red one to 75%. The blue pill treatment takes 5 weeks, and the red pill treatment 8 (which we convert to average rates of exit of 0.2 and 0.125 per week). Suppose you knew that currently, 1000 people were under treatment: how many patients would you expect to be treated with a blue pill?

(picture from www.hackthematrix.org)

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by Mathias 8. June 2008 06:47

One of my clients recently asked me to modify an Excel model, so that the adoption of products entering the market would follow a S-curve. After some digging and googling, I came across this excellent post by Juan C. Mendez, where he proposes a clean and very practical way to use the logistic function, and calibrate it through 3 input parameters: the peak value, and the time at which the curve reaches 10% and 90% of its peak value.

The beauty of his approach is that his function is compact so it can be typed in easily in a worksheet cell, and the input very understandable. However, I found it a bit restrictive: transforming it for values other than 10% and 90% requires some recalibration, and more importantly, it cannot accomodate values that are not "symmetrical" around 50%.

So I set to work through a generalized solution to the following problem: find a S-Curve that fits any arbitrary value, rather than just 10% and 90%.

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