Mathias Brandewinder on .NET, F#, VSTO and Excel development, and quantitative analysis / machine learning.
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.

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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7. January 2010 17:13

I am currently prototyping an application, which brought up some fun modeling questions.

Imagine the following situation: there are 2 products on the market. Customers use either of them, but not both. In each time period (we consider a discrete time model), new customers come on the market, and select one of the 2 products, with probability p and (1-p). At the end of each period, some existing customers stop using their product, and leave the market, with a rate of exit specific to the product.

Suppose that you knew p and the rates of exit for each product. If the size of the total market size was stable, what market share would you expect to see for each product?

Before tackling that question, let’s start with an easier problem: if you knew how many new customers were coming in each period, what would you expect the product shares to be?

Let’s illustrate with an example. You want to open the Awesome Bar & Restaurant, an Awesome place with a large bar and dining room. You expect that 100 customers will show up at the door every hour. A large majority of the customers (70%) head straight to the bar, but one the other hand, people who come for dinner stay for much longer. How many seats should you have in the bar and the restaurant so that no one has to wait to be seated?

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12. October 2008 10:52

Via Twitter, an interesting NYT piece on political prediction markets. Just like real-life market, they are not immune to unscrupulous manipulation attempts. Apparently, in recent days, the McCain value has had odd fluctuations, possibly indicating agents trying to artificially boost its "price". But how can you recognize a regular fluctuation from an artificial manipulation? The New-York Times piece notes that:

The biggest difference between typical market movements and manipulation is that honest traders will usually try to minimize the impact of their trades on the market price; paying higher prices for an asset only cuts into profits. But a market manipulator, intent on buoying the market’s ratings of their preferred candidate, will work to maximize the impact of their trading on the price.

More on price manipulation and prediction markets here.

Edit, Oct 12, 18:43: and how the financial crisis could have been adopted with prediction markets here...

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