➭ If the target type is "point", influencer \(i\) minimizes the distance from the target vector \(t_{i}\) to the equilibrium vector \(\hat{x}\), or \(\left\|\hat{x} - t_{i}\right\|_{2}^{2}\).

➭ If the target type is "direction", influencer \(i\) maximizes the projection of \(\hat{x}\) along the direction \(t_{i}\), or \(\left(\hat{x}\right)^\top t_{i}\).

➭ If the victim mechanism is "mean" with 0 prior weight, the victim computes \(\hat{x} = \dfrac{1}{2} \left(x_{1} + x_{2}\right)\) given the influencers' reports \(x_{1}, x_{2}\).

➭ If the victim mechanism is "mean" with positive prior weight \(w_{0}\), the victim computes \(\hat{x} = w_{0} x_{0} + \left(1 - w_{0}\right) \dfrac{1}{2} \left(x_{1} + x_{2}\right)\).

➭ If the victim mechanism is "threshold", the victim only average reports within some distance threshold, or \(\hat{x} = \text{mean} \left\{x_{i} : \left\|x_{i} - x_{0}\right\|_{2}^{2} < \sigma^{2}\right\}\) and \(x_{0}\) if the set is empty.

➭ If the victim mechanism is "Gaussian", the victim re-weights the reports based on \(w_{i} = \left(1 - w_{0}\right) \cdot \dfrac{c_{i}}{\displaystyle\sum_{j} c_{j}}\), where \(c_{i} = e^{\dfrac{-\left(\left\|x_{i} - x_{0}\right\|_{2}^{2}\right)}{2 \sigma_{i}^{2}}}\).