Cognitive Science and Domain Stewards

What if the path to artificial general intelligence isn't through bigger language models, but through smarter structures around existing ones?

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Recent discussions around Domain Stewards have highlighted their potential to achieve domain-specific artificial general intelligence through specialized focus and structured knowledge implementation. Building on previous analyses of AI architecture evolution and the parallels with database wrappers, we can examine how cognitive science principles illuminate why Domain Stewards are particularly effective and how they can be optimized further.

Bootstrapping Intelligence Through Structure

Domain Stewards represent a novel approach to bootstrapping artificial intelligence by deliberately constraining its operational context while simultaneously enriching its environment with well-structured knowledge and clear action pathways. Rather than waiting for more powerful language models, this approach leverages existing LLM capabilities by reducing cognitive complexity through careful system design. Like training wheels on a bicycle, these constraints paradoxically enable greater capability by providing stability and structure.

Cognitive Load Theory in Practice

Cognitive Load Theory, originally developed to understand human learning and problem-solving, provides valuable insights into why Domain Stewards are so effective. By pre-organizing domain knowledge into well-structured formats, we dramatically reduce the extraneous cognitive load on the LLM. This structured approach allows the LLM to focus its computational resources on germane cognitive load – the actual problem-solving and decision-making required for the task at hand. The parallel with human cognition is striking: just as students learn better when provided with worked examples and clear frameworks, LLMs perform better when operating within well-defined knowledge structures and action boundaries.

Bounded Rationality and Decision Making

Herbert Simon's concept of bounded rationality explains how humans make decisions under constraints of limited information, cognitive capacity, and time. Domain Stewards implement this principle by design, creating what Simon called a "satisficing" environment – one where good decisions can be made without requiring perfect information or unlimited processing power.

Take the example of an AI system managing cloud infrastructure. Rather than considering every possible configuration option (which would be computationally intractable), a Domain Steward operates within pre-defined parameters for cost, performance, and reliability. This bounded environment, combined with clear success metrics, enables the system to make effective decisions despite the inherent complexity of cloud management.

Expert Systems Psychology

Research into human expertise reveals that experts don't simply know more than novices – they organize their knowledge differently. Domain experts excel through pattern recognition and structured understanding rather than raw processing power. Domain Stewards mirror this approach by embedding expert-level knowledge organization into their architecture.

Implications for AI Development

This cognitive science perspective suggests two key principles for developing effective Domain Stewards:

First, focus on knowledge organization over raw processing power. Well-structured domain knowledge, clear taxonomies, and explicit relationships between concepts can compensate for limitations in the underlying LLM.

Second, design clear action frameworks that mirror expert decision-making processes. Rather than allowing unlimited freedom, provide well-defined options based on industry best practices and expert heuristics.

Looking Forward

Understanding Domain Stewards through the lens of cognitive science reveals why this approach can achieve near-AGI performance in specific domains even with current LLM technology. By applying these principles thoughtfully, we can create AI systems that don't just process information but truly develop expertise within their domains. The future of AI may not lie in building bigger models, but in building smarter structures around the ones we have.