  We study the voter model dynamics in the presence of confidence and bias. We
assume two types of voters. Unbiased voters whose confidence is indifferent to
the state of the voter and biased voters whose confidence is biased towards a
common fixed preferred state. We study the problem analytically on the complete
graph using mean field theory and on an Erd\H{o}s-R\'enyi random network
topology using the pair approximation, where we assume that the network of
interactions topology is independent of the type of voters. We find that for
the case of a random initial setup, and for sufficiently large number of voters
$N$, the time to consensus increases proportionally to $\log(N)/\gamma v$, with
$\gamma$ the fraction of biased voters and $v$ the parameter quantifying the
bias of the voters ($v=0$ no bias). We verify our analytical results through
numerical simulations. We study this model on a biased-dependent topology of
the network of interactions and examine two distinct, global average-degree
preserving strategies (model I and model II) to obtain such biased-dependent
random topologies starting from the biased-independent random topology case as
the initial setup. Keeping all other parameters constant, in model I,
$\mu_{BU}$, the average number of links among biased (B) and unbiased (U)
voters is varied at the expense of $\mu_{UU}$ and $\mu_{BB}$, i.e. the average
number of links among only unbiased and biased voters respectively. In model
II, $\mu_{BU}$ is kept constant, while $\mu_{BB}$ is varied at the expense of
$\mu_{UU}$. We find that if the agents follow the strategy described by model
II, they can achieve a significant reduction in the time to reach consensus as
well as an increment in the probability to reach consensus to the preferred
state.
