Algorithmic model of social processes

The development of the social sciences needs to rely on precise methods. The nomological model of explanation adopted in the natural sciences is ill-suited for the social sciences. An algorithmic model of society can be a promising solution to existing problems. In its most general form, an algorithm is a generally understood prescription for what actions to perform and in what order to achieve the desired result. Any algorithm can be represented as a set of rules of the form «If A, do D to get P». People are the bearers of this kind of rules that apply in different areas of their activities. The rules are subject to change based on personal and collective experience. There is a special mathematical discipline that studies the laws of evolution of such rules. This discipline is called genetic (evolutionary) programming. Contrary to the threatening name, the algorithmic model does not imply the deprivation of a person’s right to free choice, but it needs this right as a necessary condition for the evolution of social algorithms. These algorithms allow us to give a non-causal, but law-like explanation of many well-known social phenomena, as well as to effectively model the future, which is critically important today. A retrospective look at the evolution of social algorithms shows that the current global crisis of human society is associated with the approach to the point of singularity in their evolution. This is due to the fact that there is no need for direct human participation in the implementation of social algorithms, which is reflected in a fundamental change in the sphere of employment and less need for further development of the sciences.

Why Sociology Matters to Race and Biosocial Science

Recent developments in genetics and neuroscience have led to increasing interest in biosocial approaches to social life. While today's biosocial paradigms seek to examine more fully the inextricable relationships between the biological and the social, they have also renewed concerns about the scientific study of race. Our review describes the innovative ways sociologists have designed biosocial models to capture embodied impacts of racism, but also analyzes the potential for these models normatively to reinforce existing racial inequities. First, we examine how concepts and measurements of difference in the postgenomic era have affected scientific knowledges and social practices of racial identity. Next, we assess sociological investigations of racial inequality in the biosocial era, including the implications of the biological disciplines’ move to embrace the social. We conclude with a discussion of the growing interest in social algorithms and their potential to embed past racial injustices in their predictions of the future.