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The emerging field of Diverse Intelligence seeks to identify, formalize, andunderstand commonalities in behavioral competencies across a wide range ofimplementations. Especially interesting are simple systems that provideunexpected examples of memory, decision-making, or problem-solving insubstrates that at first glance do not appear to be complex enough to implementsuch capabilities. We seek to develop tools to help understand the minimalrequirements for such capabilities, and to learn to recognize and predict basalforms of intelligence in unconventional substrates. Here, we apply novelanalyses to the behavior of classical sorting algorithms, short pieces of codewhich have been studied for many decades. To study these sorting algorithms asa model of biological morphogenesis and its competencies, we break twoformerly-ubiquitous assumptions: top-down control (instead, showing how eachelement within a array of numbers can exert minimal agency and implementsorting policies from the bottom up), and fully reliable hardware (instead,allowing some of the elements to be "damaged" and fail to execute thealgorithm). We quantitatively characterize sorting activity as the traversal ofa problem space, showing that arrays of autonomous elements sort themselvesmore reliably and robustly than traditional implementations in the presence oferrors. Moreover, we find the ability to temporarily reduce progress in orderto navigate around a defect, and unexpected clustering behavior among theelements in chimeric arrays whose elements follow one of two differentalgorithms. The discovery of emergent problem-solving capacities in simple,familiar algorithms contributes a new perspective to the field of DiverseIntelligence, showing how basal forms of intelligence can emerge in simplesystems without being explicitly encoded in their underlying mechanics.

Classical Sorting Algorithms as a Model of Morphogenesis: self-sorting  arrays reveal unexpected competencies in a minimal model of basal  intelligence