The problem of Information Evolution

All information is produced in a certain context. It is the context that makes information meaningful. Over a period of time, people interpret information in different ways and try to apply and build something based upon what they perceive. This creates an explosion of interpretations of any unit of information, each tailored to a specific way of its application. However, just like human evolution, only some of these variations survive the test of time. These are the ones that are well accepted by the community as a whole and form a basis of further evolution. However, the ones that fail forever remain lost in the abyss of time.

The implications of being unable to track the evolution of information can have a significant effect on the future of research and development of new technologies.

Consider the following example.

Since the industrial revolution, there have been significant leaps in the amount of information and processes that were invented. Lets just talk about technology for converting heat energy of steam to mechanical energy - the principles behind steam engine. Since the invention of the steam engine, there have been so many different variations of engine designs. And with each evolution, researchers learn something, iterate and create something better. Notice that in order to create something better and useful it is imperative to not repeat the mistakes done in the past. Now imagine someone 500 years from now trying to design a steam engine. How much effort and research will be required by that person to be able to identify the different variations of engine designs in the past (800 years of engine designs) and study the principles and the differences between them. Without a proper way of identifying the different phases of evolution of the steam engine and saving enough contextual information about the designs, it will be an absolutely painful process to analyse the different approaches that were taken in the past and the reasons for their success or the failure.

The more information that is produced, the harder it will become to assimilate a greater percentage of that information unless each unit of information is organized and associated with its precedents and successors.

The thought has been bothering me for quite a while. A part of the study of HCI is to create effective ways of information assimilation. The problem described above is at its core a problem of finding an effective way to transform information so that it can be easily assimilated even with ever increasing data points.

The problem described in the previous paragraphs is different from the problem of big data. While big data attempts to derive meaningful analytics from large data sets, the solution to this problem demands a different approach to information design, one that focusses at capturing context, and its relationship with time and its neighbouring information units to meaningfully express the evolution of information.