Interactions in Science
“The aim of science is not things themselves, as the dogmatists in their simplicity imagine, but the relations among things; outside these relations, there is no reality knowable.”
—Henri Poincaré
Traditionally, science has been reductionist (Su, 2020), meaning that we isolate and simplify phenomena to predict them. The reductionist approach has been unreasonably effective (Wigner, 1960 ) in many areas, including the population, life expectancy, high-yield crops, atomic fission and fusion, space travel, digital computers, and the Internet. However, by isolating phenomena, reductionism tends to ignore relevant interactions (Gershenson, 2013; 2020).
Interactions have been studied scientifically through the lens of complexity (De Domenico, et al., 2019 ). The word originates in the Latin plexus, meaning intertwined, with a certain parallel to the Sanskrit tantra. The scientific study of complex systems became popular only in the 1980s when the appropriate tools to study them (digital computers) became available. Our brains and blackboards are limited to studying two—maybe three—variables at the same time. Computers become essential when we need to contemplate thousands or millions of elements interacting together.
“Thus one knows beyond any doubt that space is information, bliss, and active compassion and that it timelessly encompasses and unites all things.”
Lama Ole Nydahl, from The Great Seal
The “Game of Life,” developed by mathematician John H. Conway in the late 1960s, gives a good example of the relevance of interactions. It consists of an infinite chess-like board, where each square represents a cell and can take values of zero (dead, white) or one (alive, black). Then, these simple rules govern the game:
- At each time step (like a single move in a chess game), count how many of the eight surrounding neighbors of each observed cell are alive.
- If the observed cell is alive, it dies in the next time step if it has less than two or more than three living neighbors.
- If the observed cell is dead, it becomes alive in the next time step if it has precisely three living neighbors.
These simple rules lead to very complex dynamics as the game progresses. Structures emerge from the changing formations of the cells – When a structure repeats itself in the same space over many time steps, it is considered a “still life” (or a stable pattern). Other structures called “spaceships” move across the board as time progresses. The simplest moving structure is called a “glider” (See Figure). There are also “guns” that generate spaceships and “eaters” that destroy them. From these structures, logic gates can be built (so “information” can be processed), and even a “universal computer” (capable of computing any computable function). The possible configurations in the Game of Life are far greater than the number of elementary particles in our universe, and those who study it constantly discover new structures.
Even though the Game of Life is deterministic (meaning that rules are set, there is no randomness, there are no inputs, and there is no indeterminacy), it has “computational irreducibility”, meaning it is impossible to know beforehand where a given initial configuration will lead without going through all intermediate steps; there is no “shortcut” to the future because the relevant interactions produce the information required to determine all intermediate configurations. If we varied the rules of the Game of Life, even slightly, the only way of knowing whether some “interesting” behavior will be observed would be to compute it and see.
The scientific study of complexity has promoted the study of other concepts which can also be derived from studying interactive systems, as opposed to isolated systems. Emergence occurs when properties at one scale cannot be derived from another scale (Gershenson, 2023). For example, from the properties of gold atoms, we cannot derive the malleability, conductivity, or reflectivity of a gold bar. Similarly, carbon atoms can be arranged in different ways to produce charcoal, diamonds, nanotubes, buckyballs, etc. These are materials with very distinct properties that cannot be derived from the atomic properties of carbon simply because we need to consider their organization, how they are arranged, and how they interact in those arrangements.
Self-organization can be described when the properties of a system depend on the internal interactions of its components. For example, in bird flocks, fish schools, insect swarms, herds, crowds, and traffic, usually there is no leader telling everyone what to do. Each individual follows local rules that lead to global patterns or behaviors, just like in the “Mexican wave” in stadiums. The rules are simple: stand when people close to you already stood up, sit again after some time. The collective pattern can be described as a single wave, but its mechanism cannot be explained if one does not consider individual interactions. Self-organization can be useful in designing systems that can adapt to changing problems or solve problems that are unknown before they arise (Gershenson, 2007 ). It is impossible to study self-organization if we do not consider how components in a system exchange information through their interactions.
Once a system has more than a few interactions (internal or external), changes propagate constantly, so complex systems rarely stabilize, leading to dynamics that are difficult to predict. This requires adaptation: the “beneficial” change in a system as a response to a change in its environment, in the sense that adaptation promotes the persistence/survival of the system.
The more interactions which occur in a system, the more chaotic it becomes and the less predictable the result is. Consequently, even small changes propagate through the system like a wildfire, making it fragile: almost any variation will change the system drastically.
The study of complexity is leading to the development of a new scientific paradigm focusing less on matter and energy and more on information (Gershenson, 2012; 2020) to understand our world. Scientific complexity, therefore, echoes the Buddhist view that space is information.
Interactions in Buddhism
“The world is given to me only once, not one existing and one perceived. Subject and object are only one. The barrier between them cannot be said to have broken down as a result of recent experience in the physical sciences, for this barrier does not exist.”
Erwin Schrödinger
Buddhism teaches that the distinction between perceived object, perceiving subject, and the perceptual action is artificial, meaning this distinction dissolves at the absolute level. At the relative level, it would be difficult to say that there is no definite object, subject, or action; at the same time, we can argue that their interactions are so interdependent that they cannot be studied in isolation unless a relative, limited context is agreed upon. The inseparability of object, subject, and action has also been discussed in the context of quantum mechanics (Rozenberg, 2019, Burkhart, 2019).
Karma is a Sanskrit word that means ‘action’ related to “interdependent origination” (pratitya-samutpada in Sanskrit, tendrel in Tibetan), which means that nothing arises by itself but is rather caused by relationships with other phenomena. From a Buddhist perspective, as H.H. the 17th Karmapa has taught, “Karma is useful for accepting the way things are” (Dorje, 2022).
Interactions can be seen as the mechanisms by which karma manifests; our actions produce imprints which, through the irreducibly complex web of interactions, create the conditions of our lives. Like in the Game of Life, we must go through all the intermediate stages to reach the future. There is no shortcut. Since all conditions are unique, we can try to guide our actions from previous experience and general teachings, but we can only know the precise consequences of our thoughts, words, and actions once we experience them.
“ Many otherwise well-meaning people block the fulfillment of their ambitions due to their inability to understand the effects of causes. If one “simply” relaxes in space, however, it is understood that the confusion was based on the mistaken perception of separation from the totality, and will eventually transform into intuition and clarity. Because this is only possible after many years of practice and meditation, one should, in the meantime, act wisely and in accordance with the laws of cause and effect. However, an unclear mind is not always able to do so. In this case, it is recommended to rely on what one would like others to do for oneself, and not remain stuck like an old horse which doesn’t know if it’s coming or going” (Nydahl, 2012a).
We may not know the exact chain of causes and conditions that led us to the present state, but we know our actions today create our future. Buddha advised avoiding actions that lead to negative/unhappy states of mind and to do their opposite to create positive/happy impressions. Some powerful Buddhist meditations remove (or “purify”) imprints of past negative interactions, while others create joyful impressions (or “accumulate merit”). In this way, Buddhist methods can promote positive interactions and inhibit negative ones.
While complexity theory and Buddhism may ultimately be different, there are certainly similarities in their approaches and views to the world. Interactions are a tenet of the Buddhist teachings, given through the concept of karma. Through the use of this concept (paired with meditation), the Buddhist teachings offer a means to help beings relate to interactions (or change) in a better way – That is, to go beyond the idea of there being any separate subject, object, and action within those interactions, and as a result, achieve lasting happiness. As Thaye Dorje, His Holiness the 17th Karmapa, wrote, “Enlightenment is the complete acceptance of change” (Dorje, 2022b). As interactions in our society, culture, and technology continue to increase on multiple scales, perhaps by using Buddhist methods, one may be able to understand the interactions which complexity explores – and achieve lasting happiness in the process.
Acknowledgments
This article greatly benefited from interactions with Sasha Rozenberg and Alex Rush.
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