  We study a model of binary decision making when a certain population of
agents is initially seeded with two different opinions, `$+$' and `$-$', with
fractions $p_1$ and $p_2$ respectively, $p_1+p_2=1$. Individuals can reverse
their initial opinion only once based on this information exchange. We study
this model on a completely connected network, where any pair of agents can
exchange information, and a two-dimensional square lattice with periodic
boundary conditions, where information exchange is possible only between the
nearest neighbors. We propose a model in which each agent maintains two
counters of opposite opinions and accepts opinions of other agents with a power
law bias until a threshold is reached, when they fix their final opinion. Our
model is inspired by the study of negativity bias and positive-negative
asymmetry known in the psychology literature for a long time. Our model can
achieve stable intermediate mix of positive and negative opinions in a
population. In particular, we show that it is possible to achieve close to any
fraction $p_3, 0\leq p_3\leq 1$, of `$-$' opinion starting from an initial
fraction $p_1$ of `$-$' opinion by applying a bias through adjusting the power
law exponent of $p_3$.
