Is anyone aware of any prior work creating a computational model of Bieling, Beck, Brown (2000) ?
Here is something I mocked with weighted averages:
def adjustParams(sociotropy: Double, autonomy: Double) = DecisionParams( (param.selfParams._1, param.selfParams._2, param.selfParams._3 * autonomy), param.groupPreferences, param.groupWeights.map(weights => weights._1 -> weights._2 * sociotropy / groupSize) ) def calculate: Boolean = (selfWsum + groupWsum) / (groupWeightsSum + selfWeight) > 0.5 }
Tested out with my Abilene toy model, with the assumption that distribution of ‘yes votes’ would be a beta distribution with α = β = sociotropy:autonomy e.g. higher sociotropy would yield more likely decision consensus and vice versa.
Acceptance represents count of yes votes for a given group and count is the count of that number of votes out of 10000 simulated. (Left to right: α = β = 9999, α = β = 1, α = β = 1/9999)
Since the initial assumption checks out I am assuming the two high level components of the scale can be modeled with this approach.