Reciprocal Determinism: Bohm and Malkus
I probably first encountered the concept of reciprocal determinism (at least explicitly) during graduate school while studying Albert Bandura’s work. Bandura, a renowned psychologist, introduced the concept of reciprocal determinism within his social learning theory, emphasizing that behavior, personal factors, and environmental influences continuously interact and shape one another. His ideas revolutionized how we understand learning and development, providing a framework that remains foundational in psychology. Bandura’s claim that behavior, personal factors, and environmental influences interact in a continuous feedback loop immediately struck me as true, in the way excellent thinking often does—putting into words ideas that feel like they were there all along. Over time, I’ve found analogs of reciprocal determinism in a range of places, from David Bohm’s theory of the implicate order to the chaotic elegance of the Malkus wheel, and many more. As I have I re-encountered the idea, each new facet deepens and enriches the original concept, while suggesting a profound truth that is unperturbed by disciplinary boundaries—which is, of course, my favorite kind.
Implicate Order
David Bohm’s implicate order describes a deeper, enfolded layer of reality from which all observable phenomena (the explicate order) emerge. In his view, systems and behaviors are not distinct entities but expressions of a unified process Bohm called the holomovement. Behaviors arise as manifestations of systemic forces, and over time, these behaviors feed back into the system, influencing its evolution.
Consider the dynamic between individual behaviors and cultural systems. Social norms (the system) shape how individuals act, yet the collective behaviors of individuals feed back into the culture, reinforcing or transforming its patterns. Bohm’s theory describes this reciprocal influence as a continuous cycle of unfolding (behaviors arising from systemic dynamics) and enfolding (behaviors shaping the system).
A Physical Analogy
As I learned about Bohm's ideas—specifically, the relationship between explicate and implicate order, I immediately thought of the Lorenz equations1 and the Malkus wheel. The Malkus wheel is an experimental device that illustrates the chaotic dynamics described by the Lorenz equations. The wheel has buckets attached to its rim, which fill with water from a steady flow, leak as they descend, and rotate due to gravity and angular momentum. The system’s motion becomes chaotic under certain conditions, oscillating unpredictably or even reversing direction.
The flow of water into the buckets (analogous to individual behaviors) emerges in response to systemic forces like gravity and flow rate. The changing weight distribution from filling and leaking buckets feeds back into the system, altering the wheel’s rotation and stability—a direct parallel to Bohm’s view of behaviors influencing the systems from which they arise. The wheel’s chaotic motion demonstrates how systems and behaviors are linked in a continuous cycle of influence, with each adjustment feeding into the other.
The unpredictable patterns of the Malkus wheel mirror the emergence of order within chaos, akin to Bohm’s view of explicate patterns arising from implicate dynamics. These patterns are not imposed externally but emerge through the system’s interactions. In both Bohm’s framework and the Malkus wheel, disruptions (like changes in water flow) illustrate how systems adapt and evolve. Stressors do not break the system but instead drive it toward new configurations, resilience, and transformation.
Implications
Reciprocal determinism suggests that systems and behaviors evolve together through continuous feedback, with neither existing in isolation. Bohm (and Bandura) see systems and behaviors as interconnected, dynamic processes rather than static entities. The Malkus wheel provides a physical analogy for this interplay, grounding the abstract ideas in tangible terms. Just as the Malkus wheel’s motion cannot be precisely predicted but reveals underlying order over time, Bohm’s implicate order suggests that systemic and behavioral evolution emerges organically rather than through top-down control.
As I suggested at the start, the concept of reciprocal determinism has revealed itself as a powerful framework across a wide range of contexts. Bohm’s theories only strength the idea, illustrating the deep interconnectedness of behaviors and systems, while the Malkus wheel provides a tangible analogy for how this dynamic unfolds in practice. Together, they strongly suggest that the evolution of behaviors and systems is neither linear nor entirely predictable, but a dynamic of influence and feedback, where patterns emerge organically from the interplay of uncountable forces.
Lorenz Equations: Developed by Edward Lorenz, this set of three non-linear differential equations originally modeled atmospheric convection but became foundational in chaos theory. The equations show how deterministic systems can produce complex, seemingly unpredictable behavior. They are often visualized as the Lorenz attractor, a structure that demonstrates how chaos is bounded by underlying order. Their principles are demonstrated physically in devices like the Malkus wheel, providing insight into the interplay of stability, chaos, and feedback in dynamic systems.
Further Reading
Albert Bandura - Social Learning Theory: Bandura’s foundational work introduces reciprocal determinism and explores the interplay of behavior, environment, and personal factors.
David Bohm - Wholeness and the Implicate Order: This book articulates Bohm’s philosophy of interconnectedness and his groundbreaking ideas about reality.
Edward Lorenz - The Essence of Chaos: A highly accessible exploration of chaos theory and its implications for understanding deterministic yet unpredictable systems.
James Gleick - Chaos: Making a New Science: A seminal work on chaos theory, providing context and applications across disciplines.
Ilya Prigogine - Order Out of Chaos: An exploration of how systems evolve through complexity and dynamic interaction, resonating with themes in Bohm’s and Lorenz’s work.