Logistical planning

As I learned when searching Airbnb for somewhere to stay during the INFORMS Annual Meeting, the pope is visiting Philly this year. He will be there September 26-27 for the "World Meeting of Families Congress," and there has been an incredible amount of logistical planning to both ensure security and allow for the large numbers of people they expect to attend the pope's events.

This link covers a lot of the changes that have been planned which include: limited or no driving and parking (some of which started today!), reduced subway / trolley / bus routes, and extra security to get close to downtown at all. They also have planned for a lot of extra services including porta-poties, free wifi, and even 500 "teenage athletes" to help push around people in wheelchairs. And that doesn't even get into the "logistical nightmare" that would arise if a thunderstorm rolls in.

Everything should be long back to normal by the time I get there... But I am happy to take advantage of the extra capacity on Airbnb as a result of the visit.

Second-order bias in models

You may have heard about the controversy surrounding the SAT over the years since people of color have always done worse on the test. The test-writers have attempted more and more to avoid racial bias in the results. It is clear that the test used to have a significant racial bias. It is less clear if that this is still the case today.

This kind of question is studied in the field of "disparate impact." Salon.com just published this article talking about that field and the risks of having disparate impact especially when you trust algorithms to make the decisions. I would call the old SAT an example of first-order bias, and the remaining issues in the test mostly second-order bias.

The problem is clearly not simple to solve. However, hopefully with time the very data analysis techniques that currently lead to biased decisions can ultimately be used to avoid bias in outcomes.

On "Managerial Insight"

Professor Chris Tang asked the operations research community to come together and provide a definition of "Insight." In our field, most of the top journals ask authors to consider the potential managerial insights available as a result of their paper. Ostensibly clearly stating the insights will save the reader some effort in figuring out how they can use the results in the paper, and perhaps will also ensure that the paper has real-world implications.

When a fellow PhD student first introduced me to the idea of insight, I understood it to simply be the practical implications of your work. Since then I have learned a lot about how OR is used in practice. It seems that occasionally our models are directly implemented. Most of the time though, there is some reason decision makers would not want to directly implement the output of the model. Maybe the optimization problem is missing some important piece that keeps it from being directly implementable. Maybe the model is far too complex to be worth solving exactly. Or maybe the whole goal of the model is simply to provide the decision maker with a collection of options to decide between.

Given this understanding, I now think of insights as the results that can help get a better understanding of the problem even without actually implementing the model. For illustrations of insight, physics provides some good examples:

• Heavier objects do not fall faster just because they have more mass.
• Doors open more easily when you push near the handle instead of near the hinge.
• Going uphill takes more energy than going downhill.

Behind each of these examples are equations which we may or may not care about in any particular situation. But the insight provided is accessible and useful whether or not we need to make a quantitative decision.

One additional point. While insights may come from the results of optimization models, sometimes simply formulating the model in a clever way can help provide insights. To use an OR example, Michael Trick recently posted on the topic of complete enumeration as an argument for complexity. He points out that just because complete enumeration is one way to find the optimal solution does not mean the underlying problem is hard. In the same way, clever formulations of hard problems can bypass a lot of unnecessary complexity.

Dear EPA

Last week I got a letter which asked me to loan my car to the EPA for their research on emissions from privately owned vehicles. The car would be used between 1 and 4 weeks, assuming it met their criteria. They provided a couple of incentive options and also stipulated that my car would be returned with a full tank of gas after driving it up to 300 miles per day, along with being covered by their insurance.

• If I wanted a loaner car from them (which appears to be insured by them), I would receive $20 per day.
• If I did not receive a loaner car from them, I would receive $50 per day. 

Based on my post on the true cost of driving, I wondered how the offers compare to the cost to me. The costs I will experience if I participate in this (in approximate order of cost to me) include:

• Wear and tear on the car and tires at a rate of up to 300 miles per day (possibly excluding weekends, possibly not).
• The loss of use of my vehicle for the testing period if I take the $50 per day option
• Miles towards my next oil change.
• Time out of my day to do drop-off and pickup.
• The risk that something bad will happen to the car that may cost me additional time and money (which I would hopefully be appropriately compensated for).

The benefits I will experience (in approximate value for me) include:

• $50 or $20 per day for participating.
• A loaner car if I choose that option.
• A full tank of gas when I get my car back.
• If the loaner car is insured by them, I may avoid some risk due to accidents during the time of the study.

If I normally drove close to 300 miles a day, this problem would be easy to solve by choosing to get a loaner car (assuming there is no limit on the number of miles on it). Most days though, the car they would be using of mine drives 30 miles, so no easy out there. For me personally since we have two cars, I can quickly say that the loss of use of the vehicle is less than $30 a day, so I would go without if I participated at all.

We can quickly see that they are offering a minimum of 17 cents per mile driven if they drive it the full 300 miles every day they pay me for. From the information available from AAA, we can try to figure out if that leaves any extra money as an incentive for me to participate. Tires and maintenance (including oil changes) add up to 6 cents per mile. But what about the loss in value due to additional miles?

AAA estimates depreciation at 24 cents per mile based on numbers that are close to true for me, so there's definitely a risk of wiping out all the potential profit. But, how much of that has to do with the age vs. the actual miles driven? I decided to use Edmunds and Kelley Blue Book to estimate what the change in value to my car would be purely based on the miles driven. I tested both for the present, and since I didn't entirely trust the numbers, I also tried projecting out 2 years to when I might end up selling the car (I did this by checking the present value of a car 2 years older than mine with estimated mileage numbers). When I initially tried entering a change of 4000 miles to the odometer reading (a guess of the total miles that would be added), Edmunds gave me no change in value of the car at all. So instead I am basing the numbers in the table on a jump of 10,000 to the odometer just to avoid any weird artificial cutoffs.

	Edmunds	Kelley Blue Book
Effect on current value (cents / mile)	4.09	4.43
Effect on future value (cents / mile)	1.79	4.02

In short, that gives an upper bound of 5 cents per mile for depreciation due only to mileage. Therefore, we are left 6 cents a mile (or almost $20 a day) for participating. That once again seems like more than the value of having the second car to me, so I guess I should get the form in the mail!

Why do kids under 2 fly free?

If you look online for tips on flying with kids, you will inevitably find the nearly-universal advice to buy a ticket for children of any age. Even the FAA tells you in no uncertain terms that holding your child in your lap is not safe in the case of an accident. So why is it still an option?

In the FAA statement 10 years ago, they explained that this is a public policy issue. Their analysis concluded that if parents had to buy an extra plane ticket, more families would choose to drive rather than fly which would result in additional fatalities. While many people decry the FAA's decision for putting additional children at risk, I applaud that they work with the reality of how people make decisions (and used data to make that choice).

Beyond the lap-child issue, I have wondered if the drive to the airport actually carries a higher risk than the flight itself. According to the data here, in 2010 the risk was 1.1 deaths per 100 million miles driven. Flight data is somewhat harder to extract. The number cited in the link covers private airplanes separately (reasonable) but also terrorism and suicide (which we would probably want to include). That link suggests a fatality rate of 0.07 per billion passenger miles, which works out to 158 miles flown has the same risk as 1 mile driven. If we instead look at the National Travel Safety Board data we see an even more encouraging picture since many years do not have any accidents with fatalities among airliners (the scheduled commercial flights most of us are taking).

All in all, I think most people do not worry about safety when deciding if they will fly or drive to their destination. It is well known that our brains are not well suited for assessing modern risks. And so it is important for public policy to carefully include how people respond to policies in their decision making.

Conflict Minerals

One of the blogs I frequent just had a piece on companies trying to trace their supply chain all the way to the source. This initiative is because of changes in the law which require them to determine if they are using conflict minerals. The authors mentioned that the total costs of trying to be compliant with the new law has been about 709 million dollars and 6 million staff hours (and that only 24% of the companies are actually fully compliant so far).

709 million dollars is a lot of money, and I wondered if a) it might have been more effective to put it directly into aid, and b) how that number compared to the actual value of conflict minerals. From this link though, it looks like the reforms have been a lot more effective than I initially expected. The "enough project" estimated that in 2008 (prior to the new law) 185 million dollars went to armed groups via conflict minerals. They also attribute a lot of the progress since then to the law. This source estimates that revenues have decreased by 65%. They do think that some of the violent groups have shifted focus to gold since conflict gold hasn't suffered in value nearly as much as the other conflict minerals.

In short, while tracing an entire supply chain is incredibly costly, the law has been pretty effective at improving the situation.

OR and Analytics

In May of this year INFORMS (the primary professional society for Operations Research) had a vibrant conversation on their discussion board questioning what the field of Analytics is and how it does or does not overlap with OR. My favorite piece from this exchange came from Patrick Noonan who suggested that decision makers should answer two central questions:

1. "What should I do, given what I believe?" 
2. "What should I believe, given what I observe?

While you will frequently need to iterate between the two questions (and this combination is in fact what Professor Noonan describes as Analytics), I do think it is useful to think of decision making as being made up of these two pieces. The first question is a really succinct description of OR, and the second question sums up what statistics is good for.

Despite the overwhelming popularity of Analytics currently, I do think there is significant value in looking at these two questions in isolation as well as together. A big emphasis in Lean is data collection to understand your current state before you implement changes. Articulating what you wish you knew can be a useful exercise before even stating your problem. At the same time, examining your data carefully is critical to doing anything useful with it at all. Otherwise you might conclude that flashlight apps have the most valuable ads.

Hopefully this explanation helps any of my friends whose eyes glazed over when I said I do Operations Research!