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Challenges
[edit]Computational Sociology, as with any field of study, faces a set of challenges[1]. These challenges need to be handled meaningfully so as to make the maximum impact on society.
Levels and their interactions
[edit]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[1].
The question however arises as to how to identify these levels and how they come into existence? And once they are in existence how do they interact within themselves and with other levels?
If we view entities(agents) as nodes and the connections between them as the edges, we see the formation of networks. The connections in these networks do not come about based on just objective relationships between the entities, rather they are decided upon by factors chosen by the participating entities[2]. 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[3][4].
As discussed previously, societies fall into levels and in one such level, the individual level, a Micro-Macro link[5] 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.
Culture Modelling
[edit]The evolution of the networks and levels in the society brings about Cultural diversity[6]. 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?
Experimentation and Evaluation
[edit]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?
Model Choice and Model Complexities
[edit]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.[7]
A clear understanding is required to make the best decisions while choosing the best models for the task at hand:
Generative Models
[edit]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[8].
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.
Heterogeneous or Ensemble models
[edit]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.
Proposed Impact of Computational Sociology
[edit]This section describes the expected impact that Computational Sociology can bring to science, technology and society[1].
Impact on Science
[edit]As discussed in the section on challenges, 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 boon for the scientific community in large. Social science in itself describes things at a conceptual level, the addition of effective computation to it will further help in establishing well researched and empirically verified concepts.
Impact on a Competitive Level
[edit]The possibility of a healthy competition towards the betterment of the field is to be considered here. As more complex data and innovations come up, the competition between participating organizations tend to take place. The European Union is quite active in the field[1], but involving other countries and regions would help establish a mutual growth as a result of this increased competitiveness.
Impact on Society
[edit]One of the major challenges of Computational Sociology is the modelling of social processes. However, with the advancement in the field and with more involvement, the impact would be visible on a global scale. 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. In due time, the perils of false information spread may also be reduced.
- ^ a b c d Conte, Rosaria, et al. "Manifesto of computational social science." The European Physical Journal Special Topics 214.1 (2012): 325-346.
- ^ Egu´ıluz, V. M.; Zimmermann, M. G.; Cela-Conde, C. J.; San Miguel, M. "American Journal of Sociology" (2005): 110, 977.
{{cite journal}}: Cite journal requires|journal=(help) - ^ Sichman, J. S.; Conte, R. "Computational & Mathematical Organization Theory" (2002): 8(2).
{{cite journal}}: Cite journal requires|journal=(help) - ^ Ehrhardt, G.; Marsili, M.; Vega-Redondo, F. "Physical Review E" (2006): 74(3).
{{cite journal}}: Cite journal requires|journal=(help) - ^ Billari, Francesco C. Agent-based computational modelling: applications in demography, social, economic and environmental sciences. Taylor & Francis, 2006.
- ^ Centola, D.; Gonz´alez-Avella, J. C.; Egu´ıluz, V. M.; San Miguel, M. "Journal of Conflict Resolution" (2007): 51.
{{cite journal}}: Cite journal requires|journal=(help) - ^ Weisberg, Michael. When less is more: Tradeoffs and idealization in model building. Diss. stanford university, 2003.
- ^ Epstein, Joshua M. Generative social science: Studies in agent-based computational modeling. Princeton University Press, 2006.