Using OR to find more equitable solutions

By Eric DuBois

Anyone that knows me at all or has spent at least two minutes in my office during NCAA hockey season knows that I am a huge hockey fan- especially women’s hockey.  This is partly from self-interest.  One of my alma maters, Clarkson, won the NCAA championships back in 2014. Further, if you look at Wisconsin, the women’s hockey team here has been consistently outstanding of late, the men’s team, not so much.  Both Clarkson and Wisconsin are in the Frozen Four again this year and it should be a great weekend for hockey.

One of the consistent problems facing women’s hockey is the same problem facing a lot of other women’s sports: namely, it is not considered ‘as good’ as men’s hockey and therefore, gets little attention from the NCAA.  So it came as no great surprise this year when the tournament bracket came out and it was a pretty sad farce of a thing showing very little commitment from the NCAA.  Really I get to see Clarkson play Quinnipiac again?  Didn’t we just play them less than a week ago in the ECAC tournament?  And I’m sure Boston College was just as pleased to see that they get to play Northeastern for the umpteenth time.  Real crowd pleasers there…

This reminded me of Eli’s post earlier in the year about his role modeling for the Center for Athletic Scheduling at UW- Stevens Point.  One of the benefits of scheduling with a model is that you can maximize the fan’s enjoyment of new matchups and not have to resort to replaying the same old games.  More to the point, though, operations research allows us to get a more objective solution than anything that can be produced by hand.

I think this is a benefit that is often ignored.  Rather, we tend to focus on the faults that can originate from basing our system on finding the best or most efficient solution.  However, given the variety of different groups that can be affected by public policy, it is worth considering the benefit of having an objective solution to the problem, especially if that solution has been constrained to provide a reasonably equitable solution.

Whether we like it or not, we all have unintended biases that affect what we would consider to be a reasonable.  I personally recommend looking at Project Implicit if this interests you.  I have little doubt that if I were to make the NCAA bracket, I would be highly susceptible to team pairings that would annoy many people as well.

In a similar way, in planning disaster response, I am sure that personal biases would get in the way.  For proof of this in action, look no further than what happened to the original San Francisco Chinatown during the 1906 earthquake.  For those not aware, Chinatown originally looked the same as the rest of the city.  It was only after the earthquake and in a bid to win white approval, that they sought to make it look ‘Oriental.’  That was mainly because the white controlled emergency response decided Chinatown was worth losing in its entirety to save the affluent neighborhoods nearby.

So while we may be worried about the inequalities that arise from looking only at efficiency, humans are hardly good arbiters of fairness.  Do you think the objectivity of operations research gives it an edge in producing good public policy solutions?

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One thought on “Using OR to find more equitable solutions

  1. Good post. I thought of you when I saw the teams who made the Frozen Four. This issue is frustrating to me too as someone who enjoys women’s sports and doesn’t feel like I am missing out. Talk about a marginalized group. This marginalization can play out in other ways, too. For example, women are at higher risk than men for concussions, but much of the research focuses on men’s concussions from football (and for the most part women do not play football).

    In terms of OR, there was an interesting 538 article by Allison McCann about how there often isn’t good data for women’s sports, which makes it harder to do any modeling to improve the game or the fan experience.
    http://fivethirtyeight.com/datalab/hey-nate-there-is-no-rich-data-in-womens-sports/

    Like

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