For millennia, sociocultural complexity increased (and occasionally decreased) gradually over many human generations, as people inherited traditional knowledge within kin-based local communities. In these settings, where knowledge was shared within populations and across generations, selection was probably the key driver in norms of human adaptive behavior. In the 21st century, however, knowledge is transmitted across populations and within generations — and evolutionary patterns may resemble random drift more than selection in increasingly many settings. To span these different scales and modes of cultural evolution, computational approaches are useful, including a new parameterization for the transparency of payoffs in social learning. Using examples from computational social science, I will discuss how cultural evolution may have profoundly changed from the ancient past to present-day, and offers new way to consider the future effect of AI.

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