Quantifying Adaptation of Evolved Structures
Mark A. Bedau
mab@reed.edu


      Discerning and measuring progress of adaptation in evolving systems is a key challenge for those attempting to answer the deepest questions about evolution in biological and social contexts. This paper introduces and illustrates a method for visualizing and quantifying adaptive processes that applies to both
biological and cultural evolution, as reflected in data produced either by natural systems or agent-based models.
      Agent-based models provide a powerful new method for understanding adaptation in biological or social contexts. Indeed, those models might even capture an abstract enough representation of evolutionary dynamics to permit universal principles applying to both biological and cultural evolution to be identified. But extracting value from agent-based models also presents special challenges. Successfully addressing those challenges involves comparing the behavior of models with other models and with data produced by natural systems. Evolutionary activity statistics (Bedau and Packard 1992, Bedau et al. 1997, Bedau and Brown 1999) have been used to visualize and quantify evolutionary creativity as reflected in data produced by many different biological models and natural biological systems. An important part of the use of these statistics is normalizing them with independent measures of non-adaptive noise.
     Evolutionary activity statistics can also be used to visualize and quantify the dynamic creativity of cultural evolution. This is illustrated by measuring the evolutionary activity of technological innovations as reflected in patent records. Patents provide a practical context for investigating cultural evolution for a variety of reasons; important among them are that patented inventions have been certified as useful innovations and that patent records are readily available in electronic form. Preliminary investigation of
evolutionary activity in recent US patents confirms the independently supported conclusion that information technology is the most creative and innovative sector of recent technological innovation. The practical application of these statistics to agent-based models in the social sciences is discussed.