Hybrid social learning in human-algorithm cultural transmission

Abstract

Humans are impressive social learners. Researchers of cultural evolution have studied the many biases shaping cultural transmission by selecting who we copy from and what we copy. One hypothesis is that with the advent of superhuman algorithms a hybrid type of cultural transmission, namely from algorithms to humans, may have long-lasting effects on human culture. We suggest that algorithms might show (either by learning or by design) different behaviours, biases and problem-solving abilities than their human counterparts. In turn, algorithmic-human hybrid problem solving could foster better decisions in environments where diversity in problem-solving strategies is beneficial. This study asks whether algorithms with complementary biases to humans can boost performance in a carefully controlled planning task, and whether humans further transmit algorithmic behaviours to other humans. We conducted a large behavioural study and an agent-based simulation to test the performance of transmission chains with human and algorithmic players. We show that the algorithm boosts the performance of immediately following participants but this gain is quickly lost for participants further down the chain. Our findings suggest that algorithms can improve performance, but human bias may hinder algorithmic solutions from being preserved. This article is part of the theme issue ‘Emergent phenomena in complex physical and socio-technical systems: from cells to societies’.

Simple autonomous agents can enhance creative semantic discovery by human groups

“…Hence, it is essential to carefully consider the situations in which such agents can enhance human welfare. There is increasing evidence that simple (and complex) forms of AI can be added to hybrid systems of humans and machines in a beneficial way 4 – 6 , 29 , 42 . We emphasize that, in this experiment, it is the humans who are being creative, not the bots; the bots simply help the humans to help themselves.…”

Cultural reinforcement learning: a framework for modeling cumulative culture on a limited channel

“…Experimental studies of culture have generally used a transmission chain design, where participants work on a task and pass messages to each other in an iterated manner, to identify the properties that enable cumulative culture (Beppu & Griffiths, 2009;Derex et al, 2013;Tessler et al, 2021;Brinkmann et al, 2022;B. Thompson et al, 2022).…”

On the improvement, innovation and inheritance of stage makeup styling in opera under the background of big data

“…The current stage art has slowly changed from a wide range of props to show the realism of life to a three-dimensional sense of space on the stage with an emphasis on writing to express the mood while showing the coexistence of reality and mood and a variety of styles and forms of scenery [18][19][20][21]. In the current digital information era, some traditional culture, such as opera culture, has been gradually replaced by online culture, and our young people are gradually unfamiliar with opera, so how to integrate this ancient traditional culture with the more active stage art now and to have a new understanding of traditional culture and pass it on is an urgent task now [22][23][24][25].…”