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Branch of the discipline of sociology
Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom up modeling of social interactions. 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 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 do not always have to be properties of the components that the system is made of. Alexander, Morgan, and Broad, classical emergentists, introduced the idea of emergence in the early 20th 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. 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, some scholars anticipated that similar computational approaches could "solve" and "prove" analogously formalized problems and theorems of social structures and dynamics.

Computational sociology, as with any field of study, faces a set of challenges. These challenges need to be handled meaningfully so as to make the maximum impact on society.

Each society that is formed tends to be in one level or the other and there exists tendencies of interactions between and across these levels. Levels need not only be micro level or macro level in nature. There can be intermediate levels in which a society exists say groups, networks, communities etc. The challenge with this process is that, it is difficult to identify when a set of entities will form a network. These networks may be of trust networks, co operation networks, dependence networks etc. There have been cases where heterogeneous set of entities have shown to form strong and meaningful networks among themselves. As discussed previously, societies fall into levels and in one such level, the individual level, a micro macro link refers to the interactions which create higher levels. There are a set of questions that needs to be answered regarding these Micro Macro links. How they are formed? When do they converge? What is the feedback pushed to the lower levels and how are they pushed? Another major challenge in this category concerns the validity of information and their sources. In recent years there has been a boom in information gathering and processing. However, little attention was paid to the spread of false information between the societies. Tracing back the sources and finding ownership of such information is difficult.

The evolution of the networks and levels in the society brings about cultural diversity. A thought which arises however is that, when people tend to interact and become more accepting of other cultures and beliefs, how is it that diversity still persists? Why is there no convergence? A major challenge is how to model these diversities. Are there external factors like mass media, locality of societies etc. which influence the evolution or persistence of cultural diversities?

Any study or modelling when combined with experimentation needs to be able to address the questions being asked. Computational social science deals with large scale data and the challenge becomes much more evident as the scale grows. How would one design informative simulations on a large scale? And even if a large scale simulation is brought up, how is the evaluation supposed to be performed?

Another challenge is identifying the models that would best fit the data and the complexities of these models. These models would help us predict how societies might evolve over time and provide possible explanations on how things work.

Generative models helps us to perform extensive qualitative analysis in a controlled fashion. A model proposed by Epstein, is the agent based simulation, which talks about identifying an initial set of heterogeneous entities (agents) and observe their evolution and growth based on simple local rules. But what are these local rules? How does one identify them for a set of heterogeneous agents? Evaluation and impact of these rules state a whole new set of difficulties.

Integrating simple models which perform better on individual tasks to form a Hybrid model is an approach that can be looked into. These models can offer better performance and understanding of the data. However the trade off of identifying and having a deep understanding of the interactions between these simple models arises when one needs to come up with one combined, well performing model. Also, coming up with tools and applications to help analyse and visualize the data based on these hybrid models is another added challenge.

Computational sociology can bring impacts to science, technology and society.

In order for the study of computational sociology to be effective, there has to be valuable innovations. These innovation can be of the form of new data analytics tools, better models and algorithms. The advent of such innovation will be a boom for the scientific community in large.

One of the major challenges of computational sociology is the modelling of social processes Various law and policy makers would be able to see efficient and effective paths to issue new guidelines and the mass in general would be able to evaluate and gain fair understanding of the options presented in front of them enabling an open and well balanced decision process. .

Bandung Fe Institute, Centre for Complexity in Surya University, Bandung, Indonesia.