## 5 Discussion  
Under the generally uniform choices we imbued agents, we would not expect that messaging distribution outputs be skewed or heavy tailed. Importantly, that was precisely the conditions emerged shown in Figure 5C and what is evidenced from peer-reviewed literature in [PERSON] et al. (2019). [PERSON] and colleagues found a right-skewed distribution of message interactions that yield a match and this is also directly confirmed in [PERSON] et al. (2017). Additionally, our own finding that many agents received few matches while a minority received many matches is confirmed by the findings in [PERSON] et al. as well (2019). Furthermore, in [PERSON] et al. (2017) interaction data provides the relative proportion of likes and matches, showing that the number of likes compared to the number of matches is larger by 2 or 3 orders of magnitude, consistent with Tables 6 and 7, and Figure 5.  
Though we surmise that skewness--as a system property--likely depends on the scale of dating platforms (e.g., the number of active subscribers) it is likely that if the number of agents were to be larger by an order of magnitude or more, then by sheer chance alone, the _proportion_ of agents receiving no matches would be higher--as top agents would continually gather a larger share of all available matches. Nominally, whether the well-studied scale-free (power law degree distribution) property ([PERSON] et al., 2018) is an adage of our system is an interesting theoretical question that arises here. With ever-larger scale (Tinder is larger in membership count than Hinge), the skewness and consequently the tails of our like statistical distribution approach some extreme-value and heavy-tailed habitat that represents some biased social process. In the case of the classical scale-free property, the underlying social process is generally taken to be preferential attachment when the dataset under question is analyzed as a network. Though this question provokes a revision to our consideration of skewness as being a sufficiently reproducible stylized fact, \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Statistic** & **N** & \(\mu\) & \(\sigma\) & **0 th** & **25 th** & **75 th** & **100 th** \\ \hline ‘Like’ Count & 1000 & 11.365 & 3.900 & 1 & 9 & 14 & 26 \\ ‘Dislike’ Count & 1000 & 146.581 & 61.168 & 49 & 108 & 203.2 & 268 \\ ‘Match’ Count & 1000 & 12.810 & 5.558 & 0 & 9 & 16 & 32 \\ ‘Message’ Count & 1000 & 0.000 & 0.000 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: M1 sensitivity model run summary statistics including type of statistic, mean, standard deviation, and quartiles. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Statistic** & **N** & \(\mu\) & \(\sigma\) & **0 th** & **25 th** & **75 th** & **100 th** \\ \hline ‘Like’ Count & 1000 & 32.875 & 9.645 & 10 & 26 & 40 & 61 \\ ‘Dislike’ Count & 1000 & 140.707 & 56.232 & 50 & 103 & 200 & 251 \\ ‘Match’ Count & 1000 & 5.682 & 3.036 & 0 & 3 & 7 & 16 \\ ‘Message’ Count & 1000 & 2.841 & 2.068 & 0 & 1 & 4 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: M2 sensitivity model run summary statistics including type of statistic, mean, standard deviation, and quartiles. it is assumed that even under conditions of preferential attachment (that produce highly skewed, heavy tailed output distributions), there will exist some internal balancing act (algorithmic) by dating service providers to ensure that, for example, an entire city does not subsequently match with a single user and that no single user is to receive unlimited matches. Thus, our assumption of skewness without extending said assumption to require outputs to be of an extreme value nature seems realistic and sufficient in the absence of additional data. Figure 5 show that male agents received more likes than female agents while female agents received more matches and messages than male agents while sending less messages than male agents. This reproduced collection of facts is directly confirmed by [89].