One of the great innovations that lay in eToro’s OpenBook platform is the social-financial entanglement that resides in its core, being derived from the interplay between an open social network and a financial trading system. Having the inherent ability to share ideas and information between each others, OpenBook’s users are given a nre source of information they can use in order to enhance their trading performance. As the users are not playing against each other but rather – against the market, this situation becomes a non zero-sum game, hence incentivizing the users to share as much information as possible. In my research I am interested in observing this process, and the value it encapsulates.
However, before one embarks to a journey of analyzing the way information that flows in social networks influence the decisions of financial traders, one must first study the basic relevant theoretical and mathematical infrastructure involved. In the coming few weeks we shall briefly browse through some of the basic concepts that required in order to understand the dynamics of social trading platforms.
The most basic term we should first mention is perhaps the concept of Complex Systems. A complex system is a system composed of interconnected parts that as a whole exhibit a well observed behavior, that cannot be obviously obtained from observing the properties of the individual parts.
Complex systems can be observed in various real world domains, many of which are highly relevant for financial trading. One such example is social trading, that is – trading based on observations regarding the trading activities of other users. Having its very essence lays within the local interactions between its components – the trading users – social trading therefore makes a perfect example for a complex system.
Other examples include human social structures, climate and atmospheric systems, ant colonies, nervous systems, electricity and telecommunication infrastructures, and many more. Indeed, many systems of interest to humans are complex systems. After their formal definition in the recent decades, complex systems became a topic of high interests, and are currently being studied by many areas of natural science, mathematics, and social science. Their relevance to social trading, as we will see in the next weeks, is also great.
Although one can argue that humans have been studying complex systems for thousands of years, the modern scientific study of complex systems is relatively young when compared to conventional science areas with simple system assumption such as physics and chemistry.
The history of the scientific study of these systems follows several different research trends. In the area of mathematics, arguably the largest contribution to the study of complex systems was the discovery of chaos in deterministic systems, a feature of certain dynamical systems that is strongly related to nonlinearity. The study of neural networks was also integral in advancing the mathematics needed to study complex systems. In this perspective, the study of social finance, or social trading networks is exceptionally young, and subsequently has also plently of scientific knowledge to discover.
A specific case of complex systems is Complex Adaptive Systems (CAS), that are complex in that they are diverse and made up of multiple interconnected elements and adaptive in that they have the capacity to change and learn from experience. One of best examples of complex adaptive systems are of course the stock market, however, other examples include social insect and ant colonies, the biosphere and the ecosystem, the brain and the immune system, the cell and the developing embryo, manufacturing businesses and in fact – any human social group-based endeavor in a cultural and social system such as political parties or communities. This includes some large-scale online systems, such as collaborative tagging, or social bookmarking systems.
More on complex adaptive systems and social trading, next week.