Strategic Reflectivism In Intelligent Systems

TL;DR

This paper introduces Strategic Reflectivism, a framework for intelligent systems that emphasizes pragmatic switching between intuitive and reflective thinking to optimize goal fulfillment, integrating historical ideas with recent experimental insights.

Contribution

It proposes Strategic Reflectivism as a new approach that combines old philosophical ideas with modern experimental results to improve AI and human-AI systems.

Findings

Strategic switching enhances goal achievement in AI systems.

Reflective thinking correlates with better problem-solving performance.

The framework applies to both individual and collective intelligence.

Abstract

By late 20th century, the rationality wars had launched debates about the nature and norms of intuitive and reflective thinking. Those debates drew from mid-20th century ideas such as bounded rationality, which challenged more idealized notions of rationality observed since the 19th century. Now that 21st century cognitive scientists are applying the resulting dual pro-cess theories to artificial intelligence, it is time to dust off some lessons from this history. So this paper synthesizes old ideas with recent results from experiments on humans and machines. The result is Strategic Reflec-tivism, the position that one key to intelligent systems (human or artificial) is pragmatic switching between intuitive and reflective inference to opti-mally fulfill competing goals. Strategic Reflectivism builds on American Pragmatism, transcends superficial indicators of reflective thinking such as model size or chains of thought, applies to both individual and collective intelligence systems (including human-AI teams), and becomes increasingly actionable as we learn more about the value of intuition and reflection.