Interpretable Information
Many information in the real world depend on context and are open to interpretation. For example, the exchange rate between two currencies is dependent on time; the best way to reach a person can be dependent on the person’s location; the trustworthiness of a news feed post is dependent on its source.
The ambiguity of information is often poorly reflected in digital information systems. If it is reflected, then often as an application feature, not as part of the information itself. For example, we cannot just shift the temporal context and expect our apps or websites to show us information from the point of view of that time; we usually cannot specify dynamic or dependable values when filling in forms; we cannot automatically asses the trustworthiness of something we read.
Havel allows information to be interpretable.
Because of its unstructured data model, the information bits that make up a Havel-expression can be randomly stored, collected and merged. Structure is dynamically created by an interpreter only when needed. The interpreter searches for known patterns and orders them depending on given parameters; thus dynamically creating structure.
Creating structure is only one part of the interpretation process. Another one is to solve dynamic expressions, e.g. contextual values, dynamic values, implied relations, information trustworthiness assessment, temporal/logical sequencing, just to name a few. Most of these concepts are universal, meaning that they can be applied anywhere and can be encapsulated as well as chained, therefore allowing for very rich and detailed information that can change depending on from which point of view we look at them.
Havel expressions can be very precise but also purposely imprecise, vague or even paradox. The statement an information is making can depend on its interpretation. The interpretation process can be very formal and objective when just resolving patterns and logical expressions. However, with increasing complexity, it can also be subtle and even subjective, just like in the real world.
This post is part of a comprehensive introduction into SRDM, graph languages and Havel.
Continue reading: Remarkable Features
コメント