Debunking false beliefs requires tackling belief systems dartmouth NatureHumBehav
$${\Bbb B}\left[ {h{{{\mathrm{|}}}}W} \right] \propto \left} \right) \Pr \left[ h \right] \Pr \left[ {W{{{\mathrm{|}}}}h} \right]$$, the desirability bias becomes maximal in that every hypothesis is weighted exactly according to its utility \)Under the assumption that participants care about the true vote winner and not about fraud per se, we defined the utility of each hypothesis by the utility of the corresponding true vote winner,denotes the participant’s reported preference strength towards...
$$\begin{array}{l}{\Bbb B}\left[ {F=1{{{\mathrm{|}}}}W={{{\mathrm{B}}}}} \right] \\=\displaystyle\frac{{f\left + \alpha \left}}{{1 - v + vf - fc + \alpha \left}}\end{array}$$Another possible form of bias in belief updating comes from desirability of outcomes. Specifically, people have been shown to discount evidence from undesired outcomes
. We implemented discounting of evidence by shrinking the effective likelihood of the outcome toward a uniform distribution $${\Bbb B}\left[ {h{{{\mathrm{|}}}}W} \right] \propto \Pr \left[ h \right] \left \Pr \left[ {W{{{\mathrm{|}}}}h} \right] + \lambda /2} \right)$$, the outcome is completely disregarded, and the posterior belief is the same as the prior .
Desirability bias is implemented by assuming the degree of discounting depends negatively on the desirability of the observed outcome:We define the utility of the official winner in the same way as we define that of the true vote winner in the hypothesis desirability model:is the participant’s preference strength towards their preferred candidate , scaled to lie in [½, 1] . As before,
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