The emerging field of Diverse Intelligence seeks to identify, formalize, and
understand commonalities in behavioral competencies across a wide range of
implementations. Especially interesting are simple systems that provide
unexpected examples of memory, decision-making, or problem-solving in
substrates that at first glance do not appear to be complex enough to implement
such capabilities. We seek to develop tools to help understand the minimal
requirements for such capabilities, and to learn to recognize and predict basal
forms of intelligence in unconventional substrates. Here, we apply novel
analyses to the behavior of classical sorting algorithms, short pieces of code
which have been studied for many decades. To study these sorting algorithms as
a model of biological morphogenesis and its competencies, we break two
formerly-ubiquitous assumptions: top-down control (instead, showing how each
element within a array of numbers can exert minimal agency and implement
sorting policies from the bottom up), and fully reliable hardware (instead,
allowing some of the elements to be “damaged” and fail to execute the
algorithm). We quantitatively characterize sorting activity as the traversal of
a problem space, showing that arrays of autonomous elements sort themselves
more reliably and robustly than traditional implementations in the presence of
errors. Moreover, we find the ability to temporarily reduce progress in order
to navigate around a defect, and unexpected clustering behavior among the
elements in chimeric arrays whose elements follow one of two different
algorithms. The discovery of emergent problem-solving capacities in simple,
familiar algorithms contributes a new perspective to the field of Diverse
Intelligence, showing how basal forms of intelligence can emerge in simple
systems without being explicitly encoded in their underlying mechanics.

The Emergence of Basal Forms of Intelligence in Simple Systems

The field of Diverse Intelligence has emerged as an intriguing interdisciplinary study that aims to identify and understand shared behavioral competencies across various implementations. One fascinating area of exploration involves investigating how memory, decision-making, and problem-solving capabilities can arise in seemingly uncomplicated substrates. Through this research, experts seek to develop tools that will help us comprehend the minimum requirements for these capabilities and enable us to recognize and predict intelligence in unconventional systems.

In a groundbreaking study, researchers have turned their attention to classical sorting algorithms, which have been extensively studied for many decades. By examining sorting algorithms as models of biological morphogenesis and their associated competencies, two long-held assumptions have been challenged. Firstly, the concept of top-down control has been reconsidered, highlighting how individual elements within an array of numbers can display minimal agency and implement sorting policies from the bottom up. Secondly, the notion of fully reliable hardware has been discarded, allowing for the inclusion of “damaged” elements that may fail to execute the algorithm.

This novel approach involves quantitatively characterizing sorting activity as the traversal of a problem space. Surprisingly, the results show that arrays of autonomous elements possess a higher degree of reliability and robustness compared to traditional implementations when errors are introduced. This finding suggests that simple systems with self-organizing elements can inherently possess inherent problem-solving capabilities.

Furthermore, the research uncovers two additional intriguing phenomena. It reveals the ability of these systems to temporarily reduce progress in order to navigate around defects, highlighting an adaptive decision-making process. Additionally, unexpected clustering behavior is observed within chimeric arrays – arrays composed of elements following one of two different algorithms. This finding demonstrates the emergence of collective problem-solving capacities within these systems.

The implications of these discoveries are profound and contribute a new perspective to the field of Diverse Intelligence. The study showcases how even basic systems can exhibit fundamental forms of intelligence without the need for explicit encoding in their underlying mechanics. This multidisciplinary approach merging insights from computer science, biology, and artificial intelligence has the potential to unlock a deeper understanding of intelligence across diverse systems.

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