this is for holding javascript data
Yoav Ram added Mandates forecasting.md
about 9 years ago
Commit id: 205d9829cfc00e3184792f82354f43fd321074de
deletions | additions
diff --git a/Mandates forecasting.md b/Mandates forecasting.md
new file mode 100644
index 0000000..2d1c3cf
--- /dev/null
+++ b/Mandates forecasting.md
...
## Mandates forecasting
We developed several mandates calculation algorithms. Here, we will only describe our latest algorithm with some variations, as we believe that this is our best attempt at mandates forecasting.
The basic problem is how to standardize our voters sample, which, although consisting of over 7,000 voters, can be biased due to several factors such as age, socio-economical status, and party activist propaganda. Our current approach to control the sample biases was designed together with Omri Dor.
We started asking users for their 2013 elections choices on X. We use this information, together with the 2013 elections [official results](http://www.votes-19.gov.il/nationalresults).
First, we take only the latest vote for each device id, both from the 2013 and the 2015 datasets. Next, we generate a counts matrix $C$ with rows for 2015 parties, columns for 2013 parties and values for the number of votes in each row-column combination. Thus, $C_{i,j}$ is the number of voters that voted for party $j$ in 2013 and will vote for party $i$ in 2015.
Next, we take the vector $v$ of the 2013 results which contains only the counts for parties for which we have information (that is, some of our users voted for in 2013). In our basic approach we entirely disregard users that didn't vote in 2013. We then normalize $v$ so that its sum will be 1.
Now we multiply the counts matrix by the results vector to get the forecast vector $f = C \cdot v$. The forecast vector $f$ now describes our prediction of the number of votes each party will get in the 2015 elections.
