It involves the understanding of social agents, the interaction among these agents, and the effect of these interactions on the social aggregate. Although the subject matter and methodologies in social science differ from those in natural science or computer science, several of the approaches used in contemporary social simulation originated from fields such as physics and artificial intelligence. Some of the approaches that originated in this field have been imported into the natural sciences, such as measures of network centrality from the fields of social network analysis and network science.
In relevant literature, computational sociology is often related to the study of social complexity. Social complexity concepts such as complex systems, non-linear interconnection among macro and micro process, and emergence, have entered the vocabulary of computational sociology. A practical and well-known example is the construction of a computational model in the form of an "artificial society", by which researchers can analyze the structure of a social system.
In the past four decades, computational sociology has been introduced and gaining popularity[according to whom?]. This has been used primarily for modeling or building explanations of social processes and are depending on the emergence of complex behavior from simple activities. The idea behind emergence is that properties of any bigger system don't always have to be properties of the components that the system is made of. The people responsible for the introduction of the idea of emergence are Alexander, Morgan, and Broad, who were classical emergentists. The time at which these emergentists came up with this concept and method was during the time of the early twentieth century. The aim of this method was to find a good enough accommodation between two different and extreme ontologies, which were reductionist materialism and dualism.
While emergence has had a valuable and important role with the foundation of Computational Sociology, there are those who do not necessarily agree. One major leader in the field, Epstein, doubted the use because there were aspects that are unexplainable. Epstein put up a claim against emergentism, in which he says it "is precisely the generative sufficiency of the parts that constitutes the whole's explanation".
Agent-based models have had a historical influence on Computational Sociology. These models first came around in the 1960s, and were used to simulate control and feedback processes in organizations, cities, etc. During the 1970s, the application introduced the use of individuals as the main units for the analyses and used bottom-up strategies for modeling behaviors. The last wave occurred in the 1980s. At this time, the models were still bottom-up; the only difference is that the agents interact interdependently.
In the post-war era, Vannevar Bush's differential analyser, John von Neumann's cellular automata, Norbert Wiener's cybernetics, and Claude Shannon's information theory became influential paradigms for modeling and understanding complexity in technical systems. In response, scientists in disciplines such as physics, biology, electronics, and economics began to articulate a general theory of systems in which all natural and physical phenomena are manifestations of interrelated elements in a system that has common patterns and properties. Following Émile Durkheim's call to analyze complex modern society sui generis, post-war structural functionalist sociologists such as Talcott Parsons seized upon these theories of systematic and hierarchical interaction among constituent components to attempt to generate grand unified sociological theories, such as the AGIL paradigm. Sociologists such as George Homans argued that sociological theories should be formalized into hierarchical structures of propositions and precise terminology from which other propositions and hypotheses could be derived and operationalized into empirical studies. Because computer algorithms and programs had been used as early as 1956 to test and validate mathematical theorems, such as the four color theorem, social scientists and systems dynamicists anticipated that similar computational approaches could "solve" and "prove" analogously formalized problems and theorems of social structures and dynamics.
By the late 1960s and early 1970s, social scientists used increasingly available computing technology to perform macro-simulations of control and feedback processes in organizations, industries, cities, and global populations. These models used differential equations to predict population distributions as holistic functions of other systematic factors such as inventory control, urban traffic, migration, and disease transmission. Although simulations of social systems received substantial attention in the mid-1970s after the Club of Rome published reports predicting global environmental catastrophe based upon the predictions of global economy simulations, the inflammatory conclusions also temporarily discredited the nascent field by demonstrating the extent to which results of the models are highly sensitive to the specific quantitative assumptions (backed by little evidence, in the case of the Club of Rome) made about the model's parameters. As a result of increasing skepticism about employing computational tools to make predictions about macro-level social and economic behavior, social scientists turned their attention toward micro-simulation models to make forecasts and study policy effects by modeling aggregate changes in state of individual-level entities rather than the changes in distribution at the population level. However, these micro-simulation models did not permit individuals to interact or adapt and were not intended for basic theoretical research.