Is science rewriting emptiness with the emerging field of complexity theory? What Buddhists can learn from ants, atoms, and physics
WE BUDDHISTS OFTEN speak of perceiving the “true nature of reality,” attempting to do so through devoted contemplative practice. Science can make the same claim: that through a multigenerational process of hypothesis formation, experimental testing, and revision of hypotheses, we create an ever more accurate description of reality. While the dialogue between Buddhism and science seems to fluctuate between love-in and turf war, it deepens every day. I found my own way into the fray while pursuing research with stem cells, by way of something called complexity theory, a result of efforts by mathematicians and practitioners of many “hard” and “soft” sciences to understand the rules governing behaviors of large groups of interacting individuals. Complexity theory posits a universe of emergent phenomena: simply put, the notion that things can arise out of the spontaneous self-organization of smaller things. This occurs, according to the theory, not only in spite of a chaotic universe and the lack of a central planner, but because of these conditions. For a Buddhist, scientifically-minded or not, complexity theory can provide a useful new approach to the often difficult Buddhist concept of shunyata, or emptiness, the idea that all things are devoid of inherent existence. As central as it is to the Buddhist approach to the universe, emptiness remains an abstraction for many practitioners, something that is difficult to grasp even intellectually. Complexity theory, in explaining the behaviors of many different things, potentially across all scales of observation—from the water in your glass to the neurons in your brain—breaks down cosmological ideas into practical, digestible units, perhaps illuminating both the whole and its parts in the process.
COMPLEXITY THEORY concerns adaptive systems of interacting individuals and how they self-organizeinto structures and behaviors neither planned nor predictable. A familiar example of a complex adaptive system is the ant colony. Ant colonies are elaborately structured societies with, for example, a dump for refuse, cemeteries for dead ants, lines of workers carrying food into the colony and taking refuse and corpses out. Some of this organization is certainly sophisticated enough to suggest intelligent planning. For example, the distances between anthill, cemetery, and refuse dump will always be maximized. It is a complex mathematical calculation and can be seen as a reasonable community desire. Yet a colony’s structure has no top-down central planner; it self-organizes from the bottom up. As members of a complex adaptive system, the ants self-organize into larger-scale emergent structures and behaviors, such as food lines and hill builders, which are ever shifting and adapting to changes in environmental conditions, allowing the colony to survive through many generations of ants.
The absence of overt planning for such organization has been confirmed by computer models of the behaviors of individual “virtual ants,” which also spontaneously self-organize, creating a “virtual colony” with all the same elaborate structures of natural colonies. The computer programmers do not program any organization of the virtual colony, only the behaviors of the individual virtual ants, which then self-organize, just like the real thing.
Ants are only one example of this emergent self-organization. It is present throughout the natural world where interacting individuals of any size—molecules, cells, individual animals and plants, social groups, cultures—fulfill certain simple criteria:
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