From Machine Learning to clinical trials, illustrations of the powers and pitfalls of storytelling
In machine learning, there is a concept called dimensionality reduction. Dimensionality reduction is applied to a dataset that is too complex. Essentially, we reduce the data to a lower-dimensional representation in order to remove some complexity. For example, we represent a territory as a map, reducing a 3-D space to a 2-D plan, in order to navigate the territory more easily.
Storytelling, which in Sapiens, Yuval Noah Harari argues is the single most fundamental factor that empowered Homo sapiens to become masters of the world, follows a similar principle to dimensionality reduction. Reality is too complex to fully grasp, so we elevate the patterns we notice in our environment into stories and de-emphasize the rest, thereby reducing some complexity in an effort to more easily navigate the maze that is reality.
We are biologically predisposed to reduce complexity. Our senses are not made to pick up every single data point in our environment. Otherwise, we would get too overwhelmed. Thus, we create stories with the data we do have. However, as a result, a lot of the stories we create will be significantly incomplete stories, as demonstrated below. In other words, the parts that are left out would change the integral structure of the story, such as the meaning or the message, if they were incorporated.
Why is all this important? Because storytelling is ubiquitous and there can be major implications if the dimensionality reduction of a story’s source data is not carried out properly. In the words of Nobel Laureate, Ronald Coase, “If you torture data long enough, it will confess to anything.” Dimensionality reduction and storytelling are powerful tools, and as with any powerful tool, they need to be handled with great deliberation and responsibility, and not be misused.
During my tenure in health tech, I saw up-close the following example of dimensionality reduction that commonly occurs in healthcare and clinical trials in drug development. The field has known for a while that in-person visits only allow us to look at a snapshot of a patient’s symptoms and do not give us the full picture of a patient’s experience with a disease. Yet, it is common practice in standard doctor’s visits, as well as in those related to clinical trials, that the primary data collection method is to merely ask patients how they have felt over the last week. Indeed, there is an array of concerns with patient-reported data. For instance, one’s memory is weaker for events that happened further in the past, and it can even often be subject to recall bias², i.e. inaccuracy³ or incompleteness of recollections regarding past events.
The illustration below shows a year in the life of a patient with Parkinson’s Disease. Red represents the days patients experienced symptoms; the darker the shade of red, the stronger the symptoms, and green represents the days patients had a doctor’s visit. During a doctor’s visit, the patient is asked how she felt over the past week. While this gives doctors about a week’s worth of symptom data (for a total of two-weeks’ worth of data per year), they miss out on the rest of the picture (the other 50 weeks of the year). Those two weeks are likely not a representative sample of the rest of the year; thus, doctors get a significantly incomplete story about the patient’s disease experience.
This incomplete information, however, is largely what informs a doctor’s decision on the diagnosis and treatment of the patient. It is not difficult to imagine the implications of doctors making decisions based on incomplete information, ranging from misdiagnosis to treating the patient for the wrong disease stage.
The medical research field is aware of the pitfalls of significantly incomplete stories, and to address some of the consequences, continuous symptom monitoring (via wearables, sensors, or electronic patient reported outcome (ePROs)) has been emerging as a solution to gather more objective and contextual data.
Machine learning and drug development are not the only fields affected by significantly incomplete stories. Economics was in fact revamped as a field when economists realized they had been employing an erroneous dimensionality reduction of their own: discounting essential elements of human nature, such as biases and heuristics, when building economic models.
The fundamental premise of traditional, i.e. neoclassical, economics is that humans are consistently rational beings who know how to maximize utility. These humans were humorously named Homo economicus. Homo economicus is guided by logic and knows how to optimize his utility function to the greatest extent with the least possible cost.
In the 1960s, however, we saw the rise of Behavioral Economics, which sheds light on the fact that human decision-making is anything but rational. As humans, we are subject to cognitive biases, heuristics, influences, and emotions. We think and behave a lot less linearly and logically than traditional Economics assumed.
Neoclassical Economics had reduced the dimension of irrationality from human nature, leading to overly simplistic economic models, i.e. significantly incomplete stories, resulting in, at best, ineffective government and business policies.
Storytelling is part of our everyday life as well. Stories are all around us; we are shown them and we even create them. As a quick demo, consider the picture below. What does the shadow of the two people tell you about their relationship?
If you thought it showed a couple about to kiss, you likely fell prey to creating a significantly incomplete story. The full and uncropped picture below is yet another example showing that we must apply real caution when performing dimensionality reduction. Reducing the 3-D space in which two strangers are walking by each other into a 2-D plane (the wall where their shadows are projected) tells a misleading story in which the strangers appear as if they are about to exchange a kiss, when in reality, they are not. Optics, the way in which an event or action is perceived, can be as fictive as optical illusions, as illustrated in the picture above.
In fact, incomplete storytelling is a natural byproduct of our biology. Science has long known that our biology allows us to only perceive a fraction of reality. In his famous TED Talk, “Can we create new senses for humans?”, world-renowned neuroscientist David Eagleman points out that we are “trapped in a thin slice of reality.” We are neither able to see the microscopic level made of molecules and atoms nor the cosmos made of stars and planets; we only see a tiny sliver in the middle. Likewise, visible light constitutes only 1/10 trillionth of the entire electromagnetic spectrum available. Consequently, our perception is, by nature, limited to a few “pixels’’ instead of the full picture of reality.
However, despite not being able to see a lot of what is out there, such as most of the electromagnetic spectrum, we have evolved to think that we do. We tend to believe we see the full picture, even though we don’t. Furthermore, amusingly, sometimes our penchant for storytelling makes us think we see what is actually not there at all. One of the ways in which this tendency manifests is through our propensity to anthropomorphize⁵ inanimate objects.
In a fascinating experiment conducted by Heider and Simmel⁶ in 1944, people were asked to interpret a short film in which the below geometrical figures — a rectangle, a large triangle, a small triangle, and a circle — were shown moving in various directions and at various speeds. Even though there were no voice-overs or any other human elements involved, the researchers found that people perceived the film in terms of animated beings. People attributed the geometrical figures with personal qualities and used words such as man, girl, rage, frustration, and fight to describe them and their interactions. Undoubtedly, attributing people and situations with a narrative, intentions, and meaning can range from being inaccurate to being wrongful and dangerous.
The real world applicability of this flavor of storytelling, anthropomorphization, is far-reaching and multifarious, but here is an example for just a taste.
In 2006, University of Newcastle was using an honesty box to collect money for drinks in one of the coffee rooms. Instructions for payment were on a notice placed above the counter where the box and the coffee machine were located. The layout of the room made it easy for someone to get away with not paying if they didn’t want to.
For 10 weeks, researchers placed an image on this notice, alternating each week between different types of flowers and different sets of eyes.
They found that people paid roughly three times as much for their drinks when the eyes were displayed on the notice versus when the flowers were displayed. People significantly modified their behavior after simply seeing images that remotely resembled real eyes, making them instinctually “believe” the story that they were likely being watched by real eyes.
In summary, for better or for worse, perception is not reality. The gap between perception (or storytelling) and reality can be a sea of information we are not privy to. The map is not the territory.
I propose that acknowledging there is a gap is the necessary initial step to then seeking more information and getting closer to truly grasping reality. Counterintuitively, there is a lot of power in acknowledging that we don’t know. For one, it is a more accurate representation of the world; it is more likely that we don’t know something than that we do. Furthermore, when we accept that we don’t know, we are less likely to fall into the traps of biases, heuristics, or other influences. And most importantly, we open the doors to more learning. And more learning brings us a step closer to the truth.
From the examples above, a mandate emerges: to question the validity of the stories we are told, especially of the ones we tell ourselves. It is critical to acknowledge that despite our best efforts, we may still have a significantly incomplete story. Therefore, I acknowledge that the moral of this article may be a significantly incomplete story in itself. And I invite you to add to our collective data points so that we may together get a step closer to the truth. Because I may be wrong. I don’t know.
¹Image cropped from original by design. u/AlphaPlutonium, “The shadows of two strangers,” March 20, 2017, reddit.com
²Althubaiti A, “Information bias in health research: definition, pitfalls, and adjustment methods,” Journal of Multidisciplinary Healthcare (2016): 9, 211–217.
³Lacy, J. W., & Stark, C., “The neuroscience of memory: implications for the courtroom,” Nature Reviews Neuroscience (2013): 14(9), 649–658.
⁴David Eagleman, The Brain, pg. 79
⁵Scholl BJ, Tremoulet PD. Perceptual causality and animacy. Trends Cogn Sci. 2000 Aug;4(8):299–309. doi: 10.1016/s1364–6613(00)01506–0. PMID: 10904254.
⁶Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley.
⁷Melissa Bateson, Daniel Nettle, and Gilbert Roberts, Cues of being watched enhance cooperation in a real-world setting, 2006