As I come to the end of my MBA career figuring and getting ready to potentially move out of Utah the time is coming to find somewhere suitable to live. This post will outline a few tools and communities that have proved useful for research:

Finding a Neighbourhood

So you know where you’ll be working but you don’t know where to live yet? There are often several choices but how do you narrow them down? I have found the city-data.com forums valuable for getting people’s opinions about areas where they live.

Getting people’s opinions is useful but there are a couple of other tools that I’d recommend to narrow down where you want to live. SpotCrime provides an easy way of viewing recent incidents reported to the police in a given area. Knowing that there are clusters of crimes on certain streets can help you to know that you should be avoiding particular areas. Another useful website is greatschools.org. This site lets you know which schools serve the areas you are looking into and how well they rate compared to each other.

Finding a Rental

So now you’ve found a neighborhood you like it’s time to find a place to live. I’m not planning on buying a house so I’ve been looking for somewhere to rent. Everyone already knows about craigslist but one tool I’ve found really useful is padmapper. As a visual thinker being able to view a map of the neighborhood with all the available rentals visible is really useful.

Organizing the Move

After figuring out where you are going it’s time to figure out how you’re going to get there. I’ve not found any good resources that help you to select moving companies to assist with the move. Are there any you would recommend?

I watched a great video today of customer talking about one of the products that I worked on at Mobysoft. One of the things I loved about working at an entrepreneurial company like Mobysoft, and now at a customer-obsessed company like Amazon.com, is delivering products and services that make a real difference for customers. Knowing that I am delivering value to a customer makes all the work worthwhile.

Here’s the video [1 min, 30 seconds] of Matthew Gardiner, Chief Executive of Trafford Housing Trust talking about working with Mobsyoft and the RentSense product.

In closing here’s a quote from Sam Walton, another visionary who understood the importance of serving customers:

There is only one boss. The customer.
And he can fire everybody in the company from the chairman on down,
simply by spending his money somewhere else.
—Sam Walton

As I was reading up on the Chainstore Paradox I came across an interesting point on the economy of decision effort which classified decision making effort into 3 levels

1. Routine
2. Imagination
3. Reasoning

At the Routine level decision making is requires little effort as decisions are typically arrived at by default in this situation. At the Imagination level the decision making requires more effort and the ability to see oneself in the opponents position, it is here that the majority of decision making in business is made due to the additional costs of moving to the next level of decision making, Reasoning. Interestingly moving from the imagination level to the reasoning level may not yield better results, especially if the computational complexity introduced is prone to error or bias.

Determining at what level to make a decision is a real skill. I have read a couple of contrasting books on this subject, Blink by Malcolm Gladwell and Think by Michael LeGault. It is the decision, whether to approach a decision with the intuitive thinking promoted by Gladwell or the critical thinking promoted by LeGault, that is of interest to me. In the end I guess it comes down to situational factors, what is the timeframe for the decision, the relative costs, and ability to change a decision when more information is known.

“If you believe that pleasant interviewing skills, a good handshake or the right outfit are a better predictor of future performance than what the person has actually shipped in the past, I think it’s worth pointing out that you’re nuts.” – Seth Godin

Well interview season is coming up for me as I look for an internship this summer. One thing I’ve learnt from my HR class last semester was that behavioral interviews were one of the most accurate way of assessing future performance. So I’ve been busy practicing to be a good interviewee, it’s a different mindset than I had approaching it from the other side of the table as a hiring manager!

The ridiculous fish blog recently posted an entry about using “random” large integers to calculate π. That post was in the context of getting blog commentators to enter two large integer values as a kind of CAPTCHA to keep out spammers automated commenting bots. The idea of calculating pi using just random sequences of integers was intriguing to me so I set about implementing it in JavaScript. I’ve included the functions used below:

First a fairly simple function that returns a random positive integer:

function randomPositiveInteger()
{
   return Math.round(Math.random() * (Math.pow(2, 31)-1));
}

Next a JavaScript implementation of the binary greatest common denominator algorithm followed by a simple coprime function that takes two positive integers and determines if they are coprime or not (by determining if the gcd is 1):

function gcd(u, v)
{
   var k = 0;
   while ((u & 1) == 0  &&  (v & 1) == 0)
   {
      u >>= 1;
      v >>= 1;
      k++;
   }
   do
   {
      if ((u & 1) == 0)
         u >>= 1;
      else if ((v & 1) == 0)
         v >>= 1;
      else if (u >= v)
         u = (u-v) >> 1;
      else
         v = (v-u) >> 1;
   } while (u > 0);
   return v << k;
}

function isCoprime(a, b)
{
   return gcd(a,b) == 1;
}
	

Finally the function that ties it all together; Note the assignment of properties to the function object so that the function can maintain a history of results without having to store variables outside its scope:

function nextPiEstimate()
{
   /* Initialise the properties used to hold the tallies if required. */
   if (nextPiEstimate.totalCoprimePairs == null)
   {
      nextPiEstimate.totalCoprimePairs = 0;
   }
   if (nextPiEstimate.totalPairs == null)
   {
      nextPiEstimate.totalPairs = 0;
   }

   /* Generate two random integers */
   var a = randomPositiveInteger();
   var b = randomPositiveInteger();
   nextPiEstimate.totalPairs++;

   /* Determine if we are dealing with a coprime pair or not */
   if (isCoprime(a,b))
   {
      nextPiEstimate.totalCoprimePairs++;
   }

   /* Determine the percentage of pairs that were prime and calculate Pi */
   var invertedPercentage = nextPiEstimate.totalPairs / nextPiEstimate.totalCoprimePairs;
   var pi = Math.sqrt(invertedPercentage * 6);

   return pi
}
	

See the code in action