As mentioned in the conceptual paper of a knowledge commons, when each step of a project is communicated and connected to its direct predecessors (e.g., predictions link back to the theories they derive from), we get a *Directed Acyclic Graph (DAG)*. A visual depiction may look as follows (chronological top to bottom, later nodes referring back to older nodes):

Analyses on DAGs can go many ways. This topic is for open discussion of what questions are interesting to ask, how to measure them, and anything else really. These can be both

- backward looking: evaluating the network as it exists
- forward looking: planning how new nodes might change the network

I provide some test R code for 9 conditions in the drop down for anyone to simulate various adjacency matrices that can be used to build and test such networks. Row depicts the origin node, and the column the destination node, which leads to a chronological ordering. The upper triangle of the matrix is empty because old nodes cannot refer to newer ones.

## R code

```
ns <- c(10, 100, 1000)
dens <- c(.2, .5, .8)
for (n in ns) {
for (den in dens) {
obj <- matrix(nrow = n, ncol = n)
sel <- lower.tri(obj)
adj <- rbinom(sum(sel), size = 1, prob = dens)
obj[sel] <- adj
write.csv(as.data.frame(obj), row.names = FALSE,
col.names = FALSE, file =
sprintf('n%s_dens%s.csv', n, den))
}
}
```