‍Abstract:

This paper explores the applicability of the philosophy of “Unity of Knowledge and Action” in analyzing adaptive nonlinear complex systems and compares its two main interpretations. The traditional interpretation, “knowledge is the beginning of action, and action is the completion of knowledge,” emphasizes deterministic planning. In contrast, the exploratory interpretation, “action is the beginning of knowledge, and knowledge is the completion of action,” aligns more closely with the characteristics of complex systems. Using Bitcoin as an example, the paper demonstrates how it serves as a special case of an adaptive nonlinear complex system, explaining how the process of miners computing nonces to produce the longest-chain consensus mechanism embodies the exploratory logic of “unity of knowledge and action.”

Keywords: Unity of Knowledge and Action, Adaptive Nonlinear Complex Systems, Bitcoin, Emergence, Exploration, Determinism, Uncertainty

1. Introduction

Adaptive nonlinear complex systems are widespread in both nature and social sciences. Their core features include nonlinear interactions among elements, emergent collective behavior, and adaptability to environmental changes. Traditional analytical approaches, often based on linear models and reductionism, are frequently inadequate when dealing with such systems. This paper introduces the ancient Chinese philosophical concept of “Unity of Knowledge and Action” and explores its potential value in understanding and analyzing adaptive nonlinear complex systems.

2. Two Interpretations of “Unity of Knowledge and Action” and Their Philosophical Implications

“Unity of Knowledge and Action” is a key concept in Chinese philosophy, especially central in Wang Yangming’s Neo-Confucianism during the Ming Dynasty. However, there are two major interpretations regarding the sequence and interaction of “knowledge” and “action”:

2.1 “Knowledge Precedes Action”

This interpretation emphasizes the guiding role of knowledge, theory, or planning in action. Actions are carried out based on preexisting knowledge frameworks, aiming to verify, realize, or refine that knowledge. This view aligns with rationalism in Western philosophy and with the scientific method’s emphasis on theory building and experimental validation. It also corresponds with the logic of deterministic system analysis, which predicts outcomes based on clear rules and inputs.

2.2 “Action Precedes Knowledge”

This interpretation places action as the primary source of knowledge. Knowledge and understanding are not entirely a priori or predetermined but emerge and form gradually through practice, exploration, and trial-and-error. Actions generate data, experience, and feedback, which become sources of knowledge; the resulting knowledge then guides future actions, forming a dynamic feedback loop. This view better aligns with the recognition of uncertainty and complexity in systems, emphasizing the discovery of patterns through practice when prior knowledge is lacking.

3. Characteristics and Analytical Challenges of Adaptive Nonlinear Complex Systems

Adaptive nonlinear complex systems possess the following key features, which make them difficult to predict and control using traditional deterministic methods:

• ‍Nonlinearity: Interactions among elements are nonlinear, and small disturbances can cause dramatic changes in system states.‍
• Complexity: The system contains numerous interrelated components, and overall behavior results from complex interactions among them.‍
• Adaptability: The system can adjust and evolve based on internal states and external environmental changes, showing dynamic adaptation.‍
• Emergence: The system’s macroscopic behavior and characteristics are not a simple sum of its microscopic parts, but new patterns and structures spontaneously emerge through interactions.

These characteristics imply that analyzing adaptive nonlinear complex systems requires more than just prebuilt static models and fixed rules. Observing dynamic behavior and discovering patterns during system evolution is crucial.

4. Bitcoin: A Case Study of an Adaptive Nonlinear Complex System

As a decentralized digital cryptocurrency system, Bitcoin exhibits typical characteristics of an adaptive nonlinear complex system:

• ‍Nonlinearity:Bitcoin’s price fluctuates based on nonlinear factors such as market sentiment, speculative behavior, and regulatory policies. Network congestion and transaction fees also show nonlinear patterns.‍
• Complexity:The Bitcoin network comprises tens of thousands of nodes and miners worldwide, whose behaviors are influenced by economic incentives, technical constraints, and complex consensus rules.‍
• Adaptability:The Bitcoin protocol evolves through community consensus (albeit cautiously), mining difficulty adjusts dynamically with network hashrate, and user behavior adapts to market conditions and technological changes.‍
• Emergence:Bitcoin’s longest-chain consensus mechanism is not enforced by a centralized authority but emerges from independent miners computing proof-of-work and broadcasting results across the network.

5. “Unity of Knowledge and Action” in Bitcoin Mining: Emergence of Exploratory Knowledge

Bitcoin’s mining process, particularly miners repeatedly computing nonces to meet difficulty targets, perfectly illustrates the logic of “action is the beginning of knowledge, and knowledge is the completion of action”:

• ‍“Action is the beginning of knowledge”: When miners perform hash calculations, they do not know in advance which specific nonce will produce a hash that meets the difficulty requirement. They can only keep trying different nonce values through repeated computations (“action”). Each hash attempt is a random exploration of a massive solution space. At this stage, “action” is the only way to discover a potentially valid nonce—preceding any definitive “knowledge.”
• ‍“Knowledge is the completion of action”: When a miner successfully finds a nonce that meets the difficulty target and broadcasts the new block (containing transactions and nonce) to the network, the block is verified by other nodes and added to the blockchain. This addition marks an update and extension of the blockchain state—new “knowledge” is created. The collective history and consensus of the Bitcoin network is gradually built through such exploratory “actions” by miners. The longest-chain consensus rule was not perfectly known or designed in advance but emerged through continuous “action” and validation by network nodes. While miner behavior is driven by economic incentives (block rewards), the specific computations are random and exploratory, ultimately giving rise to a consensus mechanism that maintains network security and consistency.

6. Conclusion

When analyzing adaptive nonlinear complex systems, relying solely on predefined knowledge and deterministic rules is often insufficient. The philosophy of “action is the beginning of knowledge, and knowledge is the completion of action” offers a more adaptive analytical framework. It emphasizes acquiring knowledge through active practice and exploration in the face of uncertainty and complexity, and using emergent patterns from practice to guide system evolution. As a typical adaptive nonlinear complex system, Bitcoin vividly demonstrates the effectiveness of this exploratory “unity of knowledge and action” logic through its mining process and the formation of the longest-chain consensus mechanism. Future research could further explore how this philosophical approach can be integrated with modern methodologies in complex systems science to gain deeper insights into and control over the behavior of complex systems.