If Social Science is the study of human society and the relationships between these people (using surveys and interview data, among other methods), what is Computational Social Science?
Computational Social Science has the same intent as Social Science – understanding human society and relationships. But this field has shifted analysis and prediction from abstract concepts to precise data points. Using digital data and tools as its means of collecting data, Computational Social Science has the ability to analyze complexity at scale, obtaining information that is more detailed and accurate.
The data available for the practice of Computational Social Science is ever-growing as humans go about their business in a world that has almost completely metamorphosed from an analogue to a digital one. Every action taken by an individual in the digital world leaves behind a trail of evidence. Consider Person A, who does a number of things on an average day that will leave a digital trace: putting in fuel at a particular service station, buying a coffee and a doughnut, and then loading his shopping basket with a double pack of chocolate because there’s a two-for-one offer that day.
Person A’s day-to-day activities may appear mundane, but are replete with insights for institutions. With Computational Social Science, scientists can analyse his current behaviour, predicting his future behaviour and establishing trends and patterns. This information can then be used to understand not only the how, what, where and when of Person A’s day to day life, but can assist in determining the why behind future actions. Why might person A suddenly make a large withdrawal from his bank account? With an established pattern of impulsive purchases, with high levels of reception to flash sales, an institution may be able to pre-empt an action like this, prompting person A to direct his experiential and enthusiastic spending style towards a partner company, for example. Additionally, with this information, organizations can significantly improve the service they deliver for individual customers. Human beings, in the digital era, have become data collection and consumption machines that can be understood by means of intelligent algorithms.
With great power comes the need for intelligent tools
Of course, understanding the data provided by Person A, and millions others like him – as well as the complex web of relationships between these people – would be impossible without vast amounts of processing power. A key driver of the field of Computational Social Science, therefore, has been the exponential growth in computational power. McKinsey reported that, by 2030, a total of $6.7 trillion worldwide will be required by data centers to keep up with the demand for compute power.
The ecosystem.Ai Prediction Platform provides the architecture needed to perform high-compute, real-time predictions, on premises, via cloud or hybrid. Our architecture ensures uninterrupted operations, mitigating loss due to downtime. Additionally, the platform operates through a worker architecture, allowing you to scale operations horizontally across multiple nodes rather than being restricted by the size of one unit.
The use of personal data understandably raises concerns about privacy. Companies such as ecosystem.Ai, which work with regulated institutions to help them understand their clients better, use data that is provided under regulated circumstances, and is internal to the institution (“design data”), provided with clients’ consent. Clients essentially trade their data for favors, such as overdrafts, credit-limit increases, mortgages, private loans etc. This is opposed to the unethical capture and use of personal data with the aim of manipulating sentiment.
Those working in the field of Computational Social Science are able to construct computational models never thought possible before in order to interpret data provided by human activities. However, this requires businesses to re-evaluate their current data architectures to facilitate this shift towards high-power, real-time computations. With new technology comes the need for recalibration of business processes to facilitate innovation, rather than restrict it to pre-existing structures.
