Life is as diverse and complex as we are. Humans are multivalent beings, with varied capacities and skillsets. Our relationships, our careers, and our hobbies each draw from different kinds of intelligence – interpersonal, cognitive, and physical to name a few. Oftentimes we excel in some areas while lacking proficiency in others. This unevenness in our competencies is the price we pay for specialization and in many cases, it’s worth it. The alternative – as the saying goes is to be “ a jack of all trades, but a master of none”. We can achieve prominence by distinguishing ourselves as specialists in our chosen field, but the network effect of specialization for society has its downside.
The evolutionary trend of specialization has provided us with great advantages in life, but it also left us with some major challenges. Specializing in one area can mean that we achieve deep knowledge and skill there, but lack proficiency in other key areas of life. This can result in a lack of knowledge of how our specialization is related to its neighbors, or how it impacts society at scale. It’s rare, for example, for the farmer to also be an ecologist, or for the scientist to be well-read in metaphysics, yet each of these fields is significantly involved with the other. Industrial agriculture is profoundly impacting the environment and vice versa. The observed, what scientists measure, is evaluated by an observer who, in most cases, has not deeply examined his nature. It’s like an astronomer looking at Jupiter through a telescope but knowing nothing about the telescope or how it works. “The left hand doesn’t know what the right hand is doing”, as the idiom goes, so they can’t work together.
Compartmentalizing is common in corporate hierarchies, defense manufacturing, and academia, where people are encouraged to stay in their lanes. But ultimately this means people are siloed, cut off from the knowledge and resources of adjacent fields. The knowledge that might offer valuable insights that could bring their field forward is deemphasized. Moreover, compartmentalization often means that any harm that’s caused by an industry or technology is not apparent to the ones who share responsibility for it. A low-level Raytheon electronics manufacturer, for example, has no idea how the component they build is involved in the missile targeting systems of the drones they’re building. Besides reducing security risks, this allows lower-level workers to remain morally distant from the effects of their labor.
Fragmentation injures morality
Moral reasoning requires a broad comprehension of factors and the ability to track the relationships between them. This includes an ability to observe the incentives and intentions behind a thing as well as its unintended downstream effects. Downstream effects might mean a distance of place or time, like in the case of the cumulative effects of industrial fishing, for example, which devastated the biodiversity of the oceans, or the effects of industrial agriculture which ravaged soil health, negatively impacting the health of individuals, the ecosystem, and food security for everyone. Downstream effects might also mean a distance by demographics, like in the case of public policy disproportionately affecting those at the bottom of the economic ladder. When we compartmentalize we fail to see how our actions affect the future and the people outside our immediate communities.
In his book Synergetics, Buckminster Fuller argues that specialization will lead humanity to extinction and that becoming expert generalists is essential for people to generate the spontaneous social behaviors necessary to avoid it. Fragmented thinking is seeing separate parts independent of their relationships to the whole system. This has left us hamstrung in solving the world’s intractable problems. Hunger, climate change, economic inequality, social and political polarization, and war are the result of complex overlapping factors that impact each other in causal loops. Yet we approach each of them separately, making them impossible to solve and even creating bigger problems as a result of our fragmentary approach.
One of the more explicit examples of our fragmented thinking is visible in our response to the Covid crisis. Experts believed that lockdowns were essential for saving lives. A widely accepted simulation model by researchers at the Imperial College of London predicted that a suppression strategy based on a lockdown would reduce COVID-19 mortality by up to 98%. But as a recent Meta-Analysis of the Effects of Lockdowns On Covid 19 Mortality concluded, lockdowns have had little to no public health effects. 0.2% in-fact. Yet they have resulted in enormous economic and social costs. Unemployment, homelessness, domestic violence, addiction, and world hunger skyrocketed, contributing to chronic stressors that lowered the quality of life for billions.
Lockdowns shrunk the economy by 25% in just one month. While it was happening the common sentiment was, “it’s only money, but these are actual lives we are saving”. What most people failed to realize is that anytime the economy shrinks, the people barely hanging on to the poverty line fall off, 200 million globally with minorities, women and children disproportionately affected. This doesn’t just mean people are broke financially, it means their access to proper nutrition, medicine, education, and essential services is diminished, amplifying the stressors that contribute to chronic lifelong developmental issues, like addiction, crime and ultimately shortens life spans, not only for those directly exposed to the hardships but in many cases, for future generations as well.
Making Better Mental Models
Our models are only as good as the information going into them. While our intention was correct, our model was lacking crucial information – information that was available but deemed irrelevant by the model’s designers. To the extent that relevant data is missing from a model the predictions it makes will be wrong. This can be said of mathematical models as well as our models of reality. Our biases lead us to omit all those things that exist outside the framework of our paradigm. In the case of minimizing harm during a pandemic, geographic, political, economic, social, and psychological factors are as important to include as epidemiological ones. And not just as a list of ingredients to add to the cake batter but as a live feed that allows the model to adapt to real-time changes on the ground. It needs to be fluid. But harm reduction as we seem to have conceived of it included the risk of illness, and death alone – and only in the near term. Certainly, these are major factors, but those with a vivid imagination and knowledge of history could attest that there are potential realities to endure that are worse even than death. But even as heavy a burden as the immediate demands of the moment might be, harm reduction should always include the consideration of downstream effects for future generations.
In our preference for having a static, mechanistic, and simplistic approach to things, we overestimate our ability to accurately map out real-world interactions. We live in a complex adaptive system which means the models we’ve generated from our past knowledge and experience might not apply evenly to the shifting topology of real-world situations. It’s comforting to have a small number of criteria to optimize for because it’s tidier that way, but in reality, every moment is like surfing a wave, everything is moving, everything is changing all the time and the waves of change are affecting everything else. If we are to surf the waves with any kind of grace we need to be responsive to the moment and profoundly inclusive of the subtleties that contribute to it.
When our models are shown to be incorrect, as was the case only 7 weeks into the pandemic, our paradigm blindness prevented health officials and policymakers from recalibrating. Once the result of the model from Imperial came in, it was like the cake had been baked. As Fuller puts it, “Specialization tends to shut off the wide-band tuning searches and thus to preclude further discovery.” The information providing us the best feedback is not always found in the aggregates of fragmented data, as data science believes, it’s found in the synthesis of all aggregates. It’s in the whole, not the parts. This view is illustrated in the contrast between analytical reductionism (the primary MO of the scientific method) and methodological Holism, a systems approach that holds that parts are best understood in context and relation to both one another and the larger whole.
Examining systems as a coherent whole brings “synergetic integrity” as Buckminster Fuller puts it. This type of thinking involves combining and synthesizing data and insights from traditionally separate domains of knowledge and modes of knowing. Scientists have only recently begun to apply synergetic thinking to better understand complex adaptive systems. Things like social economics, ecology, and geopolitics are benefited greatly by this approach, but it’s not at all new. The brain has been adopting this approach in its development for millions of years.
The brain does a vast number of things, processing images, sounds, and sensations making lightning-fast calculations of what things mean and how to respond to stimuli based on prior knowledge. It does this through the activity of three layers of the brain which are organized hierarchically. The oldest part of the brain, the reptilian brain governs our primal instincts, the newer mammalian brain governs emotion, and the neocortex governs rational thought. Each of these parts of the brain evolved to specialize for particular tasks, yet they must also function in an integrated way sharing responsibilities and resources between them.
Each new layer includes and modulates the activity of its predecessor. Together they make up the distributed matrix of functions which must be coordinated into a unified whole. Gamma oscillations play a central role in their coordination. The gamma neural oscillations between 25Hz -140Hz have been correlated with large-scale network activity helping, in essence, to turn a bunch of different types of data, coming from different parts of the brain, into one unified perceptual experience.
Gamma is synergetic, combining and synthesizing diverse activities. In other words, gamma is a generalist integrating the specialized aspects of the brain. More gamma means deeper integration and improved coherent function. Increases in gamma synchronization and prevalence throughout the brain are thought to be involved in increased conscious access to automatic functions and implicit cognition, enhanced sensory acuity, perception accuracy and nuance, volition, moral reasoning, logical coherence, and even impacts the self-system and the way we feel and understand our identity.
When the brain is highly connected with gamma frequencies, functioning coherently at all levels of activity we are better able to think synergetically. We have greater access to the quality of attention that allows us to identify the key factors that belong in our mental models. We are more cognitively limber, making us better able to include new information when it becomes available and respond in new ways to real-time situations. With more integral awareness, our effectiveness, accuracy, and moral goodness are also enhanced since we’re less like to do things that have unintended results.
Imagine a world where synergetic or holistic thinking is not just happening for the rare few but is a core competency for everyone. A world where humans are really good at harm reduction and improve the life conditions very rapidly. With a majority of people thinking and acting in this way, the world will become a more just and abundant place. In the past, this developmental capacity has been present in those exceptional individuals who have spent decades with a meditation practice. Now we can make rapid growth in this direction simply by valuing it. In seeking to understand and actualize this capacity in our personal lives, we will begin to rewire our brains and expand our capacities for navigating complexities in an integral way.