N}$ is a square matrix of size $N$ defined by: $$\label{def:socialLearningMat}
    \lambda_{ij} = \left\{ \begin{array}{ll}
        \frac{\gamma_{ij}c_{ij}}{\sum_{l=1}^N\gamma_{il}C_{il}} & \mbox{if } \sum_{l=1}^N\gamma_{il}C_{il} \neq 0,\\
        1/N & \mbox{otherwise.}\\
    \end{array}
    \right..$$

Dynamics
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Finally, we consider a dynamical, discrete time model: knowledge-like functions evolve over time, altogether with associated quantities such as individuals’ credibility. Let us denote by $k^t := (k_1^t,...,k_N^t)\in \mathcal{F}^N$ the state of the population at time $t >0$. As time evolves, individuals modify their conceptualization by the learning algorithm presented in [@CuckerSmale2001]: $$\label{eq:LearningAlg}
    S_i^t \mapsto k_i^{t+1},$$ which computes knowledge-like function from a sample $$\label{eq:sampling}
    S_i^t = \{(e_1^{i,t},c_1^{i,t}),...,(e_m^{i,t},c_m^{i,t})\}.$$ The sampling $S_i^t$ is done using the probability measure $\rho^{i,t}$ defined in . For each individual $i$, the components of $S_i^t$ represent the influences that will shape the knowledge of $i$ at the next step. The elements of $S_i^t$ can come from social or individual learning.

Let us denote by $\tau \geq 0$ the *proportion* of [individual learning]{}, fixed and independent on $i$. The *sampling* measure is defined by $$\rho^{i,t}= (1-\tau) \rho_\Lambda^{i,t} + \tau \rho_{\mathcal{I}}^{i,t},
        \label{rho}$$ where $\rho_\Lambda^{i,t}$ and $\rho_{\mathcal{I}}^{i,t}$ are two probability measures representing the effects of social and individual learning respectively, and defined below.

The probability measure $\rho_\Lambda^{i,t}$ representing *social learning* is 