Network Analysis

8-7-13

Mirsad Hadzikadic

Professor Hadzikadic showed us some examples of the network and relationship tools that he has at his disposal in order to simulate networks in the physical world.  These include disease spreading through people and animals, how an area of land could potentially be settled and the resulting distribution of wealth among those that settle, and how birds flock or fish school.  There are a variety of variables that Professor Hidzikadic could control to affect the outcome of the particular test.  However, given enough time, no matter what the variables the multiple outcomes of a single test always seemed to result in the same or similar result.  It’s programs such as these along with observing natural systems such as population or even economics that are the tools Professor Hidzikadic uses to conduct his research.  It seems that the most promising way of analyzing this type of data is with both diagram and listing.  The data here can be easily represented in both manners and in combination, can be more easily interpreted.

            Conceptually, the space here is limited by ones own observation.  The networking patterns that Prof Hidzikadic described seem like they can exist in any situation as long as one can find a pattern to follow.  Artificial networks such as economics can be analyzed and followed to see past performance and predict where the future will take us.  Natural networks such as climate can also be examined in the same manner. It seems to me that the only thing that could limit how one deals with networks is the amount of data available to create a large enough subject to analyze.  Once enough data is aggregated and continues to aggregate, the principles of analytics and network analysis could be easily applied.

            In regards to evaluating data, it seems that there’s no “good” or “bad” alternative, there is only subjective data to be analyzed.  That being said, perhaps the better question to ask is whether or not the data is relevant to the network and yields an applicable result.  We could then take that result and see if it’s a desirable outcome, and if not then utilize the data to see how the network could be changed to create  more favorable outcome.