In 1955 the American psychologists Joseph Luft and Harrington Igham developed a technique to help individuals place themselves in context to the world and the people around them. This technique originated in the study of group dynamics and organizational behavior at the University of California and is a feedback/disclosure model of self-awareness. By combining the first names of the technique’s founders, this model came to be known as the Johari Window, represented by four quadrants.
Though the Johari Window displayed below shows the areas as of equal size, observation has shown that the size of each area varies by individual or groups of individuals. A very good overview of the Johari Window combined with Tuckman’s team development model can be found at USC.
My concern, however, is not confined to the use of the Johari Window to psychological and cognitive in-group or teaming relationships. My purpose is to determine how it can inform information theory and, for organizational behavior, how it informs the behavior and practical application of data for both in-group and out-group relationships.
A Digital Universe Seen through an Analog Lens
Recent advances in physics are demonstrating that there is a very high probability that everything in the universe can be broken into discrete elements, and, as outlandish as it sounds, the universe itself may be an immense digital computer. However, virtually everything appears to be continuous from our perspective. Even if this hypothesis is not proven to be entirely true—that the universe is some hybrid of digital and analog information that exists simultaneously (quanta behaving simultaneously as both particles and waves, for example)—it is still useful when looking at the practical application of data. The reason why this is the case is that everything that can be discretely broken into its constituent parts can be computed. This is known as digital physics.
Digital computing rests on three foundations: information theory, statistical mechanics, and quantum mechanics. These theoretical models allow us to not only push computing to its limits but also to determine what can be computed. For underlying our approach in computing is the belief—confirmed by observation and practical testing—that practically every phenomenon in general can be broken into its constituent parts to describe what is happening. This information can in turn be transmitted digitally so that the transmission can be repeated with a high degree of fidelity to the original source.
Our definition of what can be broken into its constituent parts has been expanding and is often referred to as big data. As I discussed in my last AITS article, big data is a relative term, dependent on both our ability to develop new and more powerful hardware and software able to leverage those greater capabilities. With these limitations in mind, we can still use the relational aspects of our ever expanding data sets to derive useful information.
For example, let us borrow from a popular apocryphal description of Hindu folk mythology, which states that the earth rests on the back of an elephant that rests on a tortoise. We have collected all kinds of data in this mythological world, and this data is categorized by specialty. We have data on skin and hair, on particular organs, on ivory, etc. We know a great deal about each of these areas, and our data can derive useful specific information about each of these areas. But our systems are limited, so when we try to put together just two or three of these categories, our information systems become taxed. Furthermore, disciplines have arisen that specialize in each of these areas. This is the state in which we currently find ourselves from the perspectives of social organization, computing, and information.
As our digital systems become more powerful, we find that not only can we now digitize other aspects of the universe—toes, nails, feet, tails, ears—but we can also more easily combine the data from these different categories. We may find that when we integrate certain parts of this data that we have an appendage that we call a trunk. As our digital systems develop we may very well properly integrate our data so that we can derive the conclusion that the earth is riding on the back of an elephant.
This describes the process of learning and expanding knowledge, and it is an accelerating process with the introduction of digital technology. But there is a problem with perception—the perspective of the observer—as this process plays out.
The human mind has evolved to determine continuities and to create a narrative. It is an analog brain. Discontinuities are viewed as being counterintuitive. For example, causality is not deterministic but rather probabilistic. This means that what is viewed as “common sense” and is foundational in many philosophies regarding the concept of free will is based on invalid assumptions. At the practical level this means that while the universe is deterministic, the trajectory of each discrete event is probabilistic and uncertain. Based on the perspective of the observer, the events being viewed can be seen as both continuous and discontinuous.
Perception Is Not Reality
So what we find is that our minds are programmed cinematically, especially when we access memory, and to look for discontinuities as anomalous. But augmented by our initial attempts at AI (which is what software programs really are), we know that we can break down these seemingly continuous events into their constituent parts, thus computing a number of attributes of these events—probabilities, tests of fitness, etc. We can use such insights into meaningfully processing data to inform us about how to hew closer to reality. That is, to overcome perception bias.
One major component of perception bias that needs to be addressed is known as loss aversion. This particularly applies to the use of data, the processing of that data into intelligence, and the construction of objects based on that information intended to inform the observer. Loss aversion is when we have a hard time making decisions that are in our long-term interests if they require sacrifice. This perception places too much emphasis on the status quo.
For example, software programs today are much more powerful than applications built just five years ago. Yet in a good many industries it is extremely hard to displace a well-entrenched incumbent, even though the software no longer meets all of the needs of the enterprise. I have come across a number of examples over the years in which the capabilities of a new software exceed the incumbent several times over and would address a good many of the deficiencies in the organization.
But a number of defenses are employed, such as the loss of a particular view, chart, or graph, that is used as a pretext to maintain the status quo, even though it provides much less information than is being offered in the newer software. There are others of course, such as those who do not recognize the concept of sunk cost.
The Danger of Ignorance
The purpose of processing data into intelligence is to acquire knowledge. Self-awareness is one of the core principles of psychology in our search for knowledge. It keeps us grounded and allows us to avoid the pitfalls of solipsism and narcissism.
When we seek knowledge outside of ourselves about the world, we are looking to find ways of both placing ourselves into context and navigating the world. Thus, both individually and collectively, processing of data and its conversion into intelligence is related to the expansion of self-awareness and openness. We experience both of these dynamics in the phenomenon of social media and the acceleration of knowledge.
The Johari Window, when applied to group dynamics and information management, allows us to understand the elements at play: Others seek to understand and gain knowledge about us. We, in turn, seek to know about others and ourselves. Our desire is to minimize both the Blind Area and the Unknown Area. For those who are part of the team, their desire is to minimize not only the Blind and Unknown Areas, but also the Hidden Area, whether the object of that area be another member of the team or the entire organization.
An organization that emphasizes its Hidden Area—just as an individual who is not forthcoming—fosters distrust if it becomes known that essential information has been withheld from the group. An organization that is blind of its relative position to others or of its weaknesses is vulnerable to exploitation or destruction. An organization with a large Unknown Area is like a pilot flying blind at night.
As our information systems become more powerful the Johari Window itself expands, and so we are bound up in an arms race against both our Blind and Unknown Areas, for now and the foreseeable future.
For more brilliant insights, check out Nick’s blog: Life, Project Management, and Everything