‘Propaganda Turns Out to Be Useless for Victory in Information War,’ Russian Scholars Say

Paul Goble

            Staunton, November 8 – Four Russian scholars have delivered what to many may seem a counter-intuitive message about propaganda: they argue that the data show that “propaganda turns out to be useless for victory in information war” and that consequently those who rely on it alone are likely to be disappointed.

            Their research was originally published in English (A. P. Mikhailov et al, “A model of information warfare in a society under a periodic destabilizing effect,” Mathematical Models and Computer Simulation 9:5 (2017), 580-586) has now been translated partially into Russian (nplus1.ru/news/2017/11/07/info-warfare-formal-model).

            Information war, they write, “is a process directed at the achievement of political, military, economic and other societal goals by means of using information as the main weapon.” Defining victory is often problematic, but one measure that can be used is the number of people on both sides and changes in those numbers.

            The four Russian scholars offer a formal mathematical model which shows that the impact of information deployed as a weapon may be great initially but will dissipate quickly as people forget that information and gain data from other sources.  And that, they argue, means that information war is unlikely to work as well as its supporters suggest.

            Indeed, they write, “a short-term upsurge in propaganda from one of the participants will not influence the outcome of the conflict, but only if there is enough time for the uptick of information to be forgotten.”

            N+1’s Elizaveta Ivtushok asked Yegor Yureskul of the Higher School of Economics for a comment. He suggested that “such models have a greater theoretical than applied character” because they aid in research but may not guide behavior because in the nature of things they seldom can consider all the aspects of a problem.
            “As a rule,” Yureskul says, “the practical application of formal models and sometimes their empirical verification as well are connected with a whole range of problems, in the first instance, the lack of real change for measuring the key parameters of the model” and thus determining whether it is correct or not.