If two weeks of graduate school has taught me anything, it is that I don’t use words like ‘ontology’ and ‘epistemology’ in everyday conversation. Practical experience aside, I am now under the gun to come up with a few pages talking about both abstractions as it relates to Informatics.
This will be my main task this weekend, along with solving the fictitious problem of converting TiVo program scheduling to a phone interface. To date, I have managed only to jot some shorthanded notes down and separate them into two columns. Cohesion as paragraphs must follow soon.
On the one hand, there is the concept of ontology. The nature of being. An intangible function that asks, “Why does this exist?” It is a top-down approach to information that starts with some abstract knowledge or behavioral observation and examines its form for some concrete evidence to support its existence. It is the quantification of a concept.
Its mirror is epistemology, the nature of knowledge. Based on concrete structure and measurable data, it asks, “What is this thing that exists?” The bottom-up approach to information shapes bits and pieces into something meaningful, giving a name to that object. It seeks to identify existence.
Moore’s Law states that data doubles every 18 months. While that has always been impressive, it hasn’t been perceived until recently. It is like the man who asks as payment $1 to start and doubled salary every day for a month. Two weeks into the arrangement, the employer is paying just over $8000 for the day and starting to worry. By the end of February, that wise man would be pulling in $134 million for the day and over $223 million for the month. From a data generation standpoint, we’ve just barely reached the foot of the mountain.
That volume of data is at odds with our tools of measurment and thus our ability to quickly glean meaning. The gap between data and comprehension increases exponentially and gives rise to this new discipline, informatics, to form a bridge to knowledge. The complexity of the problem is such that we require both the ontological and epistemological takes on information to be able to discover meaning.
This is best illustrated by examining the problems of complex systems — that is, a massive collection of simple objects using simple rules to create emergent behaviors that cannot be predicted by examing the simple rule alone.
An ant colony, for example, forages for food successfully by relying on the collective actions of individual ants operating on a simple set of rules. The member ants venture out in a random manner without instruction from a centralized queen ant. If an ant finds food, it reacts by releasing a phermone and heading back to the colony, the phermone trailing behind. Other ants respond to the phermone by following it, migrating to a stronger signal and away from weaker smells. if another ant finds food at that location, the phermone traile becomes stronger and more attraictive. Thus, the colony plunders the best local sources for food without some algorithm to pinpoint the exact spont without activity from the ants.
The foraging ants are an example of the exploration within a system. There is no solution until lots of individual objects interact with their environment. An exploitative approach seeks to identify the solutions and apply them to other problems. Solutions, therefore, are discovered through both ontological and epistemological methodology. Form and function. Knowledge and data. Measurements and Emotions. What and Why.
