Figure A1.

Exploration of the relationship between ‘agreement’ between an agent and one of its neighbours, the epistemic confirmation bias parameter $\gamma$, and the epistemic value of reading that neighbour’s tweet content. Here, the two marginal posteriors $Q\left(s^{\mathbf{Idea}}\right)$ and $Q\left(s^{\mathbf{MB}k}\right)$ are expressed as two Bernoulli distributions with respective parameters p and q, where ‘agreement’ is the case when $p=q$ and hence $(1-p)=(1-q)$. The top row shows heatmaps of the negative ambiguity $\mathcal{H}$, entropy $\mathbf{H}\left[Q\right(o\left)\right]$, and the full epistemic value $EV=\mathcal{H}+\mathbf{H}\left[Q\right(o\left)\right]$ for a fixed value of $\gamma =15.0$, under all possible values of p and q. The ‘epistemic confirmation bias‘ effect is seen in the negative ambiguity surface $\mathcal{H}$ (upper left plot), which is maximised when posterior beliefs about the validity of Idea 1, measured by p, are aligned with posterior beliefs about a neighbour’s meta-belief about Idea 1, q. The bottom row of plots shows a complementary perspective, demonstrating the effect of increasing $\gamma$ on the epistemic value and its components, for different settings of q when $p=0.0$. The subplot on furthest to the right of the bottom row shows that increasing $\gamma$ increases epistemic value most when q is on the same side of $0.5$ as p ($q=0.2,q=0.4$), and the effect of $\gamma$ on epistemic value deceases once q passes $0.5$. Note that the epistemic value is 0 when $p=q=0$, because although the negative ambiguity is maximised in this case, it is counteracted by the entropy term which is 0 since both posteriors are certain.