Modelling the Effects of Self-learning and Social Influence on the Diversity of Knowledge

Abstract

This paper presents a computational model of acquiring new knowledge through self-learning (e.g. a Wikipedia “rabbit hole”) or social influence (e.g. recommendations through friends). This is set up in a bipartite network between a static social network (agents) and a static knowledge network (topics). For simplicity, the learning process is singly parameterized by $\alpha$ as the probability of self-learning, leaving $1-\alpha$ as the socially-influenced discovery probability. Numerical simulations show a tradeoff of $\alpha$ on the diversity of knowledge when examined at the population level (e.g. number of distinct topics) and at the individual level (e.g. the average distance between topics for an agent), consistent across different intralayer configurations. In particular, higher values of $\alpha$, or increased self-learning tendency, lead to higher population diversity and robustness. However, lower values of $\alpha$, where learning/discovery is more easily influenced by social inputs, expand individual knowledge diversity more readily. These numerical results might provide some basic insights into how social influences can affect the diversity of human knowledge, especially in the age of information and social media.

Supplementary Figures

Fig. 4.

Variations of nonblock intralayer models. (a) Set up of nonblock models. PA: preferential attachment, ER: Erdős–Rényi, WS: Watts–Strogatz (Sect. 2.1) (b) Changes of diversity indices for these models as a function $\alpha$ (Sect. 2.2)

Fig. 5.

Different initialization strategies for nonblock models (a) based on the topic intralayer degrees and (b) effects on population and individual diversity indices as a function of $\alpha$. See Fig. 4a for names and illustrations of the different models.

Fig. 6.

Changes of population diversity indices (a) , group diversity indices (b) and individual diversity indices (c) for the stochastic block intralayer models due to $\alpha$.