Consensus is (almost) never what you need

The Case for Probabilistic Thinking in Decentralised Systems: Moving Beyond Consensus

In the realm of decentralised networks and distributed systems, consensus has long been considered the gold standard for problem-solving. However, after years of research and practical experience in decentralised systems, I propose a paradigm shift: moving away from deterministic consensus models towards probabilistic approaches. This article argues that our current focus on consensus may be limiting innovation and presents a case for embracing probabilistic thinking.

Challenging the Consensus Paradigm

Two concepts that have consistently proven problematic in distributed systems are shared time synchronization and consensus. Both of these elements, while seemingly fundamental, often hinder rather than help in creating efficient, scalable systems.

The Time Synchronisation Challenge

Time synchronisation across multiple entities presents significant challenges:

1. It is practically impossible to achieve precise agreement on time across numerous entities.
2. Natural systems function effectively without a universally agreed-upon time.
3. Attempts to synchronise time across systems are inherently bounded and imprecise.

While accounting for durations and temporal flow is crucial, striving for exact time synchronisation across distributed systems is often counterproductive.

The Limitations of Consensus

The decentralised network industry has invested heavily in consensus mechanisms. However, this approach has several inherent limitations:

1. Natural systems operate without explicit consensus:
– Biological systems like ant colonies function without centralized agreement.
– Cellular automata and neural networks operate based on local interactions rather than global consensus.

2. Consensus algorithms provide an illusion of certainty:
– They often claim to guarantee correctness with a certain probability.
– This probabilistic guarantee is essentially equivalent to uncertainty.

3. Consensus mechanisms require bounded systems:
– They necessitate specific assumptions to guarantee outputs.
– This bounding inherently limits system flexibility and scalability.

The Probabilistic Alternative

Instead of pursuing perfect consensus, a shift towards probabilistic approaches offers several advantages:

Range-Based Search: A Probabilistic Approach

In decentralized networks, we can implement a range-based search mechanism:

1. Acknowledge the inevitable presence of malicious nodes.
2. Utilize distance-based metrics to query a range of nodes.
3. Monitor response consistency over time.
4. Naturally detect and invalidate conflicting or malicious data.

This approach enables:
– Higher concurrency
– Faster transaction processing
– Natural conflict resolution without global consensus

Dynamic Validation for Transaction Systems

For transaction-based systems, we can implement:
– Dynamic-depth validation of parent transactions based on complexity.
– Efficient detection and elimination of injected transaction chains without requiring global consensus.

Aligning with Natural Systems

Natural systems and evolutionary processes operate on probabilistic principles rather than deterministic ones. By aligning our approach with these natural models, we can create more robust, scalable, and efficient systems.

Conclusion: Embracing Probabilistic Models

The technology industry’s focus on consensus-based models may be hindering progress in decentralized systems. By adopting probabilistic thinking, we can:

1. Develop faster, more concurrent systems
2. Build resilient networks that do not rely on perfect node behavior
3. Create systems that are more aligned with natural processes

It is time to reconsider our approach to decentralized systems. By moving beyond the limitations of consensus and embracing probabilistic models, we can unlock new levels of innovation and create truly decentralized, robust, and scalable systems.

This paradigm shift invites further discussion and research. As we continue to explore and develop decentralised technologies, it is crucial to challenge established norms and consider alternative approaches that may better serve our goals of creating efficient, resilient, and scalable distributed systems.