It’s well known that a frame of reference can bias your thinking. For example, an expensive wine in the context of a very expensive wine looks like a better deal. An idea presented in a positive context is more readily accepted than one presented in a negative light. While I won’t get into details about the many cognitive biases that we undergo with every breath of every day, I’ve been thinking about a particular kind of bias that influences research software.

The Disney Fairytale Bias

Research Software is subject to the disney fairytale bias, or a frame of reference bias that is experienced by the creators of research software to not fully value or acknowledge the full life cycle of software. Let’s break this down into components.

Frame of Reference

When I say “frame of reference” I’m referring to your expectations about the world, but specifically, how your culture, life experience, and social norms influence your perception of time and order. Easy examples are the concept of a work week being 5 days, a romance starting with a first date and ending happily ever when the courtship ends in a wedding, or a trip to the supermarket starting with grabbing a cart, and ending with checkout. None of these temporal expectations are wrong, however they point our attention to the events that happen within the span of a well known “start” and an “end,” after which some other experience is happening.

Lifecycle

A true lifecycle is the actual period of time from birth to death of a thing, whether it be a living entity or a real or tangible object in the world. In the case that a entity lifecycle overlaps completely with an individual’s frame of reference, then there is no Disney Fairytale bias, because the entire start to finish is seen. However, in the case that we are biased to only appreciate or value a subset of the lifecycle (the frame of reference) then we experience the bias.

Examples

Now you should understand enough to dive into specific examples! We will start with the context from where the name derives, and finish with the topic for this post - research software. For each of these examples, take note of what the true lifecycle is, and what frame of reference we are biased to see. For each, you can imagine looking at an entire scene or story, and then putting a paper tower tube up to your eye and having tunnel vision.

The Disney Fairytale

In a Disney movie, the plot proceeds very predictably.

  1. we are introduced to a main character, typically an underdog that has some under-appreciated value or attribute.
  2. the character interacts with a conflict, usually involving one or more evil entities to eventually overcome the conflict.
  3. the ending ceremony is some kind of wedding or message to signal the conflict resolution.
  4. the underlooked value is appreciated fully, and the main character (with some love interest) live happily ever after.


The End.

Wait, is that the end?

I hope you see the issue here. We’ve watched a movie about fictional characters, and due to this repeated theme of living happily ever after at the end of the courtship, we don’t think beyond that. We don’t typically think about how the newly happy couple might spend the holidays, resolve conflict together, have children, or grow old together. Since these are well known parts of a lifecycle, we aren’t being exposed to the whole picture. This colors our own expectations about romance, because we are influenced by the media that produces these stories, and we consequently place higher attention on the courtship through marriage phase. Everything after that, although it is real and alive, doesn’t tend to show up in the movies.

The Disney Fairytale Bias for Academic Software

Guess what - academia is no different! When you think of the entire lifecycle of a research problem, it starts perhaps with the first person that ever asked the question. The story then progresses with inquiry, synthesis, and conclusions. With each new scientist that gets interested, the question is tested in a different way or context, a publication is written, and our accumulated knowledge about the research problem accumulates. The story might have a definitive start, but if you think about it, there isn’t really an ending. Knowledge is continually changing and growing, and perhaps only when our entire species is completely gone might be consider this an end. But on the other hand, maybe another life form would then find our knowledge, understand it, and pick up where we left off?

The example goes a bit far, but I hope that you are anticipating what I’m about to say. The real lifecycle of a research question is infinitely long. And in fact, we might more realistically say that the lifecycle of an inquiry by a specific scientist starts when the question is asked, and ends when the outputs of that scientist (papers, data, and software) are no longer needed or used. Yes, this is a much more realistic scope of interest, because we have to take the selfish interests of the scientist into account. In this context, the true ending of the story is not about the publication, but rather the lifecycle of the data and research software. This is where the Disney Fairytale Bias hits the academic - because his or her end of story is commonly the publication. Let’s walk through these stages:

  1. We start with a main character, an academic student, staff, or faculty at an academic institution
  2. the character interacts with a scientific problem, involving many challenges to solve
  3. the ending ceremony is the acceptance of the publication, the person or group is successful and...


The End

Oh no! We’re here again. The academic moves on to the next thing, but here we still have software and data to support. It’s primarily pushes for reproducibility and mandates by publishers that first encouraged academics to act more reproducibly, and now it’s becoming more a cultural thing - you might even be looked down upon if you don’t take measures for reproducibility of your work. But does this really change the academic story, or incentive, when you think about it? I don’t think so - the push to further one’s career is still about publication. When a publication is successful, you need to start working on the next to survive (and likely you have many in the kettle).

In summary, the lifecycle of an academic’s work is really the full span from the start of asking a research question through the longer term support and data of software, but due to incentives for success in the field, the frame of reference typically ends at publication.

What about Industry?

This might be a point of discussion, but I find the frame of reference for typical academic software development to be very different than industry software development. Although both might have a similar staring point to address a specific problem, the academic story ends with a publication, while the industry story includes details for the longevity of the software, and minimally, criteria for choosing to keep supporting or abandoning it. It’s not that industry cares about scientific reproducibility per say, but they care about providing a product that will result in profits. In industry, the software has life beyond one initial use case or analysis, because something that is useful to the community and brings in profits is obviously valuable to sustain. The average scientist developing software most likely cares about it for his or her own use, and then isn’t as incentivized to keep it going. This isn’t globally true, but my gut says that it’s generally true. In industry there are even business models for how to commercialize open source. The business model for closed source commercial software is of course more obvious - the users must purchase it to use it.

Why should I care?

Here is why I think this general concept is important to think about. In all of our lives, well beyond writing papers or code for software, our perspective of an event, and what instances of time are indicative of a “start” or “stop” are biased. And it means big things not just for what we pay attention to, but also how we experience time, and form expectations around life experience. So, if you want to be a generally more introspective person, or are just interested to think about behavior more deeply, you might start to question these tiny events.

So how do you even go about doing this? I like to think about it like a game. You can take any moment in time and just stop. Ask yourself, What am I doing? Do I place a label on this thing that I’m doing now - is it a small event? A larger event? What other people are involved here? Then once you’ve identified that thing, think about what your expectations are for it. Think about what you might define the start and end to be, and how that might bias your perceptions. Once you’ve done that, think about what it would mean if you or the people around you thought about a different start or a different end to be the “right” start, or the right end. Would it change your expectations of the experience or the experience itself? I find this to be a really fun thing to do because you can do it at any point of any day, and it really gives you pause to stop and think about some of these biases or expectations that we have that are so familiar to us that we entirely stopped thinking about them.

You’ll start seeing this Disney Fairytale Bias in almost any kind of event, interaction, or definition that involves cultural norms, people, and expectations. I happened to think of research software because it’s something near and dear to me, but there are many other examples to parse over. More importantly, once you identify that you have a Disney Fairtale frame of reference bias, try to think of if you should consider changing your perspective, and how you might go about it.

For me, I realized that to some extent, the success of research software is dependent on being able to change this frame of reference for the average researcher. If a researcher is going to take some time to think about what steps will be taken for archive of their data or sustainability of their software, this requires changing their basic story to have a later ending. It comes down to a shuffling around of incentives to put higher value on the future potential for the software to further the science, and in turn, still support the reputation of the researcher. In other words, I’ve realized that in order to improve research software sustainability, I need to understand how it fits into the story of a current researcher, and what I need to do to extend that story. Writing this post is just a start. I hope that you’ll join me in thinking about these stories, and how they can be used to influence people, and consequently, the world.