I’ve been awfully quiet lately, and that’s due, for the most part, to the nature of my current line of work. I’m immersed in one of the many campaign teams working towards the upcoming 2016 national and local elections, and confidentiality is among the defining attributes of my particular position. When something takes up most of your time but you aren’t cleared to discuss that “something” in detail, there’s little left to write about.
That being said, here’s a thorny little problem that we’ve had to grapple with at work: scale.
In coming up with protocols and policies for our campaign team, we’ve frequently had to remind ourselves that our solutions must be viable not only for the situation at hand, but for any similar ones that might arise in future. One key aspect to consider with this kind of big-picture deliberation is the nature of any and all groups that might be involved in such scenarios: we might be troubleshooting the concerns of a small group of 5 volunteers today, but any lasting policies we set down will have to work even if the parties concerned balloon to 50, 500, or five thousand-plus in number.
As far as I can tell, it’s a common enough problem for any command structure, and perhaps more frequently in politics and government work.
In the recently-concluded final PiliPinas 2016 presidential debate, for example, Mayor Rodrigo Duterte and ex-DILG Secretary Mar Roxas had a notable exchange regarding PhilHealth coverage in Davao City. Duterte claimed that nobody in Davao had received any PhilHealth benefits, decrying Roxas’ trumpeting of the institution’s increased reach; in response, Roxas cited the available data regarding PhilHealth beneficiaries in Davao City. (PhilHealth itself, in answer to Duterte’s allegations, also took the time to publish details regarding the benefits awarded to citizens of Davao.)
The pomp and posturing of campaign debates aside, Duterte and Roxas present valid — if seemingly contradictory — perspectives that reveal how crucial a problem scale is in government work. Roxas, in citing the PhilHealth numbers, shows us the extent to which the government has addressed the challenge of offering affordable healthcare support for its citizens. Duterte, on the other hand, in drawing attention to the many more in Davao who have not received PhilHealth benefits, reminds us that these efforts, however impressive, might still stand to improve.
And how could they not? PhilHealth, by its very nature as a public healthcare program, comes with an implicit responsibility to cater to the Philippine population as a whole. That’s massive ground to cover for any entity, and certainly not a promise anyone can completely live up to anytime soon. That’s also how Roxas and Duterte are both right — because with PhilHealth working on such a scale, it’s inevitable that some will slip through the cracks, even if coverage has improved overall.
The problem of scale is a multifaceted thing.
From our campaign team’s own experience, one such facet has to do with universality, or at least of universal applicability: one must find a way to ensure that solutions work even for larger groups encountering similar problems, at present or in future. Larger groups introduce more variables to account for, as well as a host of new possible consequences to bear in mind; each new instance of a similar problem will occur under circumstances that are at least slightly different from the current ones. Successfully surmounting this sub-problem of scale ensures efficiency, because then your team won’t have to waste time poring over essentially the same problem as it recurs in its many variations.
Think of it this way: each problem is its own universe, encompassing a variety of disparate possible forms that all boil down to the same essential issue to be solved. A team saves time and energy when it can address that universe as a whole, instead of having to come up with solutions per planet.
As the Roxas-Duterte scuffle over PhilHealth shows, another facet has to do with inclusivity. As you cast your net out to cover more instances of the problem you’re trying to solve, you have to ensure that you can and do address as many of those instances as possible (if not all of them, which is admittedly an almost impossible ask in any scenario). This has less of a temporal dimension than universality; in fact, a big chunk of the difficulty here might stem from simultaneity. In attempting to cover so many (similar) problems at once, how does one ensure that nothing gets overlooked? Answering this question will ensure a thoroughness of method that reduces the possibility of (chronic, and costly) failure on your part.
Finally, some typical Election Day campaign team questions might shed light on a third aspect.
Throughout the campaign period, we have been providing food for our volunteers at each activity. This usually means each volunteer gets a packed meal, which was cooked and packaged by the kitchen team and then delivered to the venue by a sub-group of the logistics team. So far the system has worked out well, because there have only been at most 50-100 volunteers out on the field at any one time, and meals have had to be delivered to at most 2-3 venues at the same time.
This changes, however, on Election Day.
Our area houses around 200 clustered precincts, divided amongst roughly 60 polling places. Volunteers will be manning each of these polling places, and E-Day logistics will have to deliver meals to all of these volunteers across all these polling places by roughly the same set hour. It’s out of the question to ask one polling place to wait an hour while meals get delivered to a different polling place first; it’s also out of the question to let the kitchen team cut corners and possibly deliver subpar food.
So our third facet seems to be: consistency, or the problem of ensuring that the quality of one’s solutions doesn’t suffer as one scales up their application. Is it a valid solution if one variation of the problem gets the prescribed attention and another gets a slipshod bandaid? Is it a valid solution if solving one instance of a problem requires giving another instance up to massive failure? This is not an issue of what sacrifices are necessary or acceptable; it is an issue of ensuring that one’s solutions remain solutions–proper, viable ones–regardless of the circumstances in which one is asked to carry them out. It is, perhaps, futile to expect 100% consistency all the time; however, this should not prevent us from aiming for that outcome anyway. One tricky thing about the problem of scale, after all: often, there are far too many things depending on one’s performance to allow for any degree of recklessness.
So, to recap: scale has been a concept we have been struggling to deal with throughout the campaign, and I imagine this isn’t a novel problem for most other fields out there, either. From what I’ve seen so far, the problem of scale can be further broken down into three sub-questions: universality, inclusivity, consistency.
- Universality asks us to consider how solutions can be applied to each variation of the problem that’s likely to occur.
- Inclusivity then asks us to consider how those solutions can be delivered to all identified problem-variations to which they are applicable.
- Consistency asks us to consider how the quality of those solutions can be sustained over the course of multiple and/or frequent and/or simultaneous applications.
Each of these sub-questions must be successfully addressed if one is to solve problems on any large scale.
I haven’t delved into the nuances of scale itself (e.g., how scale in the form of increased concurrent numbers presents different complications versus scale in the form of increased frequency of problem-variations’ occurrence), since a) that would make for a much longer post, and b) I admit I haven’t given that dimension of the concept much thought yet. It seems to be a promising line of further inquiry, though, so I’ll see if I can write out some thoughts or even initial impressions in the future.