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| author | Thibaut Horel <thibaut.horel@gmail.com> | 2015-09-21 16:15:13 -0400 |
|---|---|---|
| committer | Thibaut Horel <thibaut.horel@gmail.com> | 2015-09-21 16:15:13 -0400 |
| commit | 8c2d3d070a3db1b469e3e32e3c20ef67bc274b3b (patch) | |
| tree | 2f65df75e18be3deaa497ba66237a27ea01e0b56 /supplements/main.tex | |
| parent | 2fd2ce34df46fca46b365d983a650069911e5558 (diff) | |
| download | criminal_cascades-8c2d3d070a3db1b469e3e32e3c20ef67bc274b3b.tar.gz | |
Supplements, section 3.1
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| -rw-r--r-- | supplements/main.tex | 31 |
1 files changed, 28 insertions, 3 deletions
diff --git a/supplements/main.tex b/supplements/main.tex index 8ba30bd..71bfcc4 100644 --- a/supplements/main.tex +++ b/supplements/main.tex @@ -295,9 +295,34 @@ the optimum: \section{Inferring the Patterns of Infections} -We can estimate if a person was primarily infected via peer contagion by comparing the contributions from the background rate and from his or her peers. +\subsection{Methodology} -We take this approach one step further to determine the person most responsible for infecting each of these 7,016 individuals infected by social contagion. +Given fitted values of the parameters of the Hawkes contagion model, it is then +possible to determine whether an infection event $(t, v)$ was primarily caused +by the exogenous process or endogenous peer contagion. For this, using formula +\eqref{eq:hawkes}, we simply compare the value of the exogenous intensity and +the sum of endogenous exciting functions at the time $t$ of infection, and +attribute the infection event to the largest of the two quantities. In other +words, for our contagion model we compare: +\begin{enumerate} + \item $\mu(t) = \mu(t) = \mu_0\left[1 + 0.43 \sin\left(\frac{2\pi}{365.24} + t + 4.36\right)\right]$ and +\item $\sum_{i:t_i< t} g_\alpha(u_i, v)\betae^{-\beta(t-t_i)}$ +\end{enumerate} + +It is possible to go one step further: for an infection event attributed to +peer contagion, we can single out a single past infection event as its cause. +This is achieved by choosing the event $i$ for which the value of the summand +in 2. is maximum. That is, the event $i$ for which the value of $g_\alpha(u_i, v)\beta +e^{-\beta(t-t_i)}$ is largest. + +We thus uncover the patterns of infections: each infection event is attributed +to either the exogenous intensity or a single past infection event. Drawing an +edge between infection events $(t_1, u_1)$ and $(t_2, u_2)$ if $(t_1, u_1)$ is +the cause of $(t_2, u_2)$, we obtain the forest of infections: tree roots are +the infection events attributed to exogenous intensity, and tree nodes are +events attributed to peer contagion and are connected to their cause. We note +that cycles are impossible since edges are directed forward in time. \begin{figure} \centering @@ -319,6 +344,6 @@ We also simulated contagions on the co-offending network. Since we are most inte \subsubsection{Results} -\section{Regarding causality [THIBAUT WRITE THIS SECTION]} +\subsection{Comments on Causality} \end{document} |
