Something that I have found troubling in our study of networks is the lack of verification and gatekeeping in the networked system; suddenly all opinions can be shared equally throughout nodes, and therefore they can be repeated over and over again, such that the constant qualculation of popular opinion is swayed towards a widely held belief, regardless of its validity. This week, Moorcroft’s analysis of the model shows a different tradeoff: analysis of the validity of the model, rather than of the implications. This lecture drew back to another conversation this week, in which I overheard a professor trying to convince a student about the validity of the field of statistics. In order to persuade the student, she said that the field of statistics had moved away from attacking each other’s methods and moving to mixed methods, combining the strengths of multiple approaches, in order to draw the most accurate conclusions. I have less faith in this idea that fields have turned towards minimizing the noise about how the information is generated in order to maximize the signal of the information itself. Especially in the case of modeling, in which a model for the future could inspire an immediate call to action, changing the constants of the model and therefore making its prediction inaccurate, the tension between knowledge and its meaning becomes more pronounced. As such, my understanding of the relationship between scientific knowledge and its dissemination into the general public is well described as a “general amalgam of agents and conditions, reactions and counter-reactions, which brings social certainty and popularity to the concept of the system” (33).
What exactly does this adjustment mean, and how can we shift the volume and validity of information to engage with the information itself, rather than the package it is delivered in?