Local Search and the Evolution of World Models

An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily incremental, involving the generation and selection among random local mutations and recombinations of (parts of) one's current model. We argue that, by narrowing and guiding exploration, this feature of cognitive search is what allows human learners to discover better theories, without ever grappling directly with the problem of finding a global optimum, or best possible world model. We suggest this aspect of cognitive processing works analogously to how blind variation and selection mechanisms drive biological evolution. We propose algorithms developed for program synthesis provide candidate mechanisms for how human minds might achieve this. We discuss objections and implications of this perspective, finally suggesting that a better process-level understanding of how humans incrementally explore compositional theory spaces can shed light on how we think, and provide explanatory traction on fundamental cognitive biases, including anchoring, probability matching, and confirmation bias. We argue that genuine conceptual innovation is necessarily blind and incremental, involving selection among random local mutations and recombinations of (parts of) one's current belief system. We relate this idea to Universal Darwinism, and propose that algorithms developed for program induction can help explain how human minds innovate.

Automated summary

This article discusses the role of evolutionary mechanisms in the development of models of the world. It argues that innovation in developing a global world model is necessarily incremental, involving the generation and selection of random local mutations and recombinations. The article suggests that algorithms developed for program synthesis can provide candidate mechanisms for how human minds achieve this.