Social physics or sociophysics is a field of science which uses mathematical tools inspired by physics to understand the behavior of human crowds. In a modern commercial use, it can also refer to the analysis of social phenomena with big data.
The first elements of social physics were outlined in French social thinker Henri de Saint-Simon’s first book, the 1803 Lettres d’un Habitant de Geneve, which described the idea of describing society using laws similar to those of the physical and biological sciences. His student and collaborator was Auguste Comte, a French philosopher widely regarded as the founder of sociology, who first defined the term in an essay appearing in Le Producteur, a journal project by Saint-Simon. Comte defined social physics as
Social physics is that science which occupies itself with social phenomena, considered in the same light as astronomical, physical, chemical, and physiological phenomena, that is to say as being subject to natural and invariable laws, the discovery of which is the special object of its researches.
After Saint-Simon and Comte, Belgian statistician Adolphe Quetelet, proposed that society be modeled using mathematical probability and social statistics. Quetelet's 1835 book, Essay on Social Physics: Man and the Development of his Faculties, outlines the project of a social physics characterized by measured variables that follow a normal distribution, and collected data about many such variables. A frequently repeated anecdote is that when Comte discovered that Quetelet had appropriated the term 'social physics', he found it necessary to invent a new term 'sociologie' (sociology) because he disagreed with Quetelet's collection of statistics.
There have been several “generations” of social physicists. The first generation began with Saint-Simon, Comte, and Quetelet, and ended with the late 1800s with historian Henry Adams. In the middle of the 20th century, researchers such as the American astrophysicist John Q. Stewart and Swedish geographer Reino Ajo, who showed that the spatial distribution of social interactions could be described using gravity models. Physicists such as Arthur Iberall use a homeokinetics approach to study social systems as complex self-organizing systems. For example, a homeokinetics analysis of society shows that one must account for flow variables such as the flow of energy, of materials, of action, reproduction rate, and value-in-exchange. More recently there have been a large number of social science papers that use mathematics broadly similar to that of physics, and described as “computational social science”.
In the late 1800s, Adams separated “human physics” into the subsets of social physics or social mechanics (sociology of interactions using physics-like mathematical tools) and social thermodynamics or sociophysics, (sociology described using mathematical invariances similar to those in thermodynamics). This dichotomy is roughly analogous to the difference between microeconomics and macroeconomics.
In modern use “social physics” refers to using “big data” analysis and the mathematical laws to understand the behavior of human crowds. The core idea is that data about human activity (e.g., phone call records, credit card purchases, taxi rides, web activity) contain mathematical patterns that are characteristic of how social interactions spread and converge. These mathematical invariances can then serve as a filter for analysis of behavior changes and for detecting emerging behavioral patterns.
Recent books about social physics include MIT Professor Alex Pentland’s book Social Physics or Nature editor Mark Buchanan’s book The Social Atom. Popular reading about sociophysics include English physicist Philip Ball’s Why Society is a Complex Matter, Dirk Helbing's The Automation of Society is next or American physicist Lazlo Barabasi’s book Linked.
Blockchain, Cryptography, and Social Physics Applications
Over the past few years, Social Physics has been shown to be a potent analytics tool for a variety of applications where either data is anonymized or only metadata is available, as is typical of crytographic and blockchain systems. This is because Social Physics is primarily about interactions between elements, so that the form of the data is often irrelevant.
Following are a few examples for the use of Social Physics applied to Blockchain and Crypto systems:
- Social Physics for Financial Interaction. In a recent MIT PhD Thesis, a social physics approach to cryptocurrency market buy/sell timing was shown to drive “bubbles” in AltCoins. In this experiment, carefully timed investments totaling just a few dollars often drove more than 5000 times increase in AltCoin market value.
- Social Physics for breaking cryptographic anonymization. A recent ruling of the US Supreme Court cited the use of Social Physics for the efficient re-identification of cryptographically anonymized metadata (similar to Bitcoin transactions), discouraging defense agencies and private companies from collecting and using this so-called "anonymized" data.
- Social Physics for Anti-Money Laundering (AML). Social Physics was used to uncover a collaborating network of bitcoin addresses, an arrangement often used to launder stolen Bitcoins. This was done using a handful of known malicious addresses, that were used as “seeds” for the Social Physics “behavioral similarity detection” algorithm, resulting in an accurate detection of dozens of additional members of the network.
Most recently, research by Prof. Alex “Sandy” Pentland and Dr Yaniv Altshuler at MIT has combined social physics with Machine Learning to improve the performance of machine learning applications. Endor, a company co-founded by Dr. Altshuler, utilized social physics to introduce a predictive intelligence platform that automates the process of answering predictive business questions, using natural language. A ICO of 50 million USD was also conducted by Endor.
In the field of cyber-security, a team headed by Dr Altshuler at MIT has shown how the concept of social physics, manifested as mathematical invariances that are embedded in large datasets, can be used in order to generate new and highly efficient types of attacks on behavioral information.
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