Mutual benefits of social learning and algorithmic mediation for cumulative culture

TL;DR

This paper explores how social learning and algorithmic mediation jointly influence the development of complex human culture, highlighting the importance of their interaction especially in less connected social networks.

Contribution

It introduces a model analyzing the combined effects of social learning and algorithmic mediation on cultural evolution, emphasizing their synergistic impact.

Findings

Algorithmic mediation affects cultural accumulation significantly.

Less dense social networks amplify the impact of algorithmic mediation.

Combining social learning and algorithms optimizes cultural growth.

Abstract

The remarkable ecological success of humans is often attributed to our ability to develop complex cultural artefacts that enable us to cope with environmental challenges. The evolution of complex culture (cumulative cultural evolution) is usually modelled as a collective process in which individuals invent new artefacts (innovation) and copy information from others (social learning). This classic picture overlooks the growing role of intelligent algorithms in the digital age (e.g. search engines, recommender systems and large language models) in mediating information between humans, with potential consequences for cumulative cultural evolution. Building on a previous model, we investigate the combined effects of network-based social learning and a simplistic version of algorithmic mediation on cultural accumulation. We find that algorithmic mediation significantly impacts cultural accumulation and that this impact grows as social networks become less densely connected. Cultural accumulation is most effective when social learning and algorithmic mediation are combined, and the optimal ratio depends on the network's density. This work is an initial step towards formalizing the impact of intelligent algorithms on cumulative cultural evolution within an established framework. Models like ours provide insights into mechanisms of human-machine interaction in cultural contexts, guiding hypotheses for future experimental testing.