Marco Polo, Rhinoceros, Unicorns and a Theory of Mind

Unicorns, Ontologies and How the Mind Organizes the World

On his journey home from China to Italy in 1291, Marco Polo was forced to spend five months on Sumatra, waiting for the monsoon winds to change direction so he could sail home. It is there that he accurately wrote that he saw unicorns in size “not all by any means less than an elephant.” which he described like this:

'Tis a passing ugly beast to look upon, and is not in the least like that which our stories tell of as being caught in the lap of a virgin; in fact, 'tis altogether different from what we fancied.'

Marco Polo, of course, was not really seeing unicorns. It was his first experience of Rhinoceros and though his physical description of the beast was accurate his categorization of it, clearly, was not. In using his own knowledge of his culture that believed in the legend of the Unicorn ensnared by a virgin maiden Marco Polo exhibited a justifiable, perhaps, law of mind that seeks to structure the knowledge of the world into ontologies in order for it to make sense.

Sensemaking is a tricky business. It requires individuals who are aware of their state of embodied cognition to analyze the situations they encounter and then attempt to categorize them into a rules-based system. This then becomes the foundation for the creation of the representational mental models we use to navigate the world.

It sounds logical (and this is what Marco Polo actually did) but there are two distinct problems with this approach: first, the very act of organizing information and then using it to better understand and perhaps take advantage of our environment, changes our perception of it.

Psychologists call this enactive cognition. They argue that instead of our being simply products of our environment who blindly respond to the stimuli it presents us with, our very sense of cognition arises out of the dynamic Venn diagram of our actions (that result from our decisions and choices) and the environment itself. This is the basis upon which our perception can change our reality leading to choices that deliver positive outcomes even when the odds are against us. Enactive cognition is taken a step further into what is known as 4E Cognition (embodied, embedded, enactive, and extended) that provides a deeper, more holistic approach to how we think. 

The second problem with sensemaking is even more obvious and even harder to solve: How does the mind know when to create a new category structure for the data it observes instead of using an existing one like Marco Polo did?

The Law of Mind and Structured Data

Harvard cognitive scientist Sam Gershman has a theory of mind whereby the brain discovers the world by creating representational states which are then structured according to observed similarities from which is deduced a latent (or unobservable) cause.

To go back to Marco Polo and his ill-advised quick judgement of how to classify the Rhinoceros, Marco’s brain, confronted with a reality he had not encountered before, was faced with a choice: A. Classify this into a familiar category or B. Ascribe it to an entirely new one. Both choices are important because, depending on which one Marco chose, each contributed to a representational state of the world from which could be inferred generalized latent causes that described how it works.

In this case Marco Polo chose to go with what he knew by extending the familiar (for him) category of the Unicorn and adding, in his observations, that it was beyond the expected description in general appearance and nature.

Marco’s choice reinforced a world of ideas where the Bible (and the Church) were authoritative sources of knowledge and wisdom. It was a safe, reassuring choice where something new was made to fit into something already known.

Had he, however, taken choice B and created a new category for the Rhinoceros he would have rewritten our understanding of 14th century zoology. In doing so the new structured data provided by this new category would have broadened our understanding of latent causes and propelled scientific thought much faster.

What Do We Learn From All This?

There are several things we understand from all this. First, inferred latent causes are largely unobservable ones that have observable effects and allow us to better understand the dynamic nature of the world we live in. As such, they contribute to the overall narrative structures we apply in order to help make sense of the observable reality.

Ravi S. Kudesia, who is an Assistant Professor of Human Resource Management at the Fox School of Business, Temple University, describes the impact of this at an organizational level like this:

This perspective calls attention to how members of organizations reach understandings of their environment through verbal and embodied behaviors, how these understandings both enable and constrain their subsequent behavior, and how this subsequent behavior changes the environment in ways that necessitate new understandings.

What he is saying is that we employ narrative not just in our personal and ongoing efforts of identity curation but also at an organizational level in order to establish motivation, rationale and a course of action for a business.

In creating fresh, ontologies, structured data in other words, from observable data we should:

  • Establish a state representation (i.e. functional mental model) of the world through interaction with the environment.
  • Experiences (and observations) are grouped according to perceived causes.
  • Generalizations are then made based on observable effects in order to begin to know what we cannot see (i.e. latent causes). As an aside to this, Democritus, with his knowledge of mathematics, used exactly this cognitive and neural approach to posit the existence of atoms over 2,200 years ago.
  • Latent causes are clustered according to their inferred similarity and determined by the brain using a Bayesian probabilistic calculation not dissimilar to that employed by machine learning algorithms that determine the likelihood of a particular event based on prior knowledge of conditions that lead to it.

Practical applications of latent causes are found in online and offline communities where the broken windows syndrome is in effect, predictive policing in many communities across the world, and Amazon’s anticipatory shipping model which, according to its patent, ships “the package to the destination geographical area without completely specifying the delivery address at time of shipment, and while the package is in transit, completely specifying the delivery address for the package.”

Summary

Observable effects often have unobservable causes (i.e. latent causes). We can infer latent causes by creating structured clusters of similar effects and looking at the commonalities that emerge. These commonalities further our understanding of how the world works and how we can then affect it.