Complex systems like biological ecosystems, economic systems, or the human brain are difficult to represent and properly understand. Non-linearities, emergent properties, self-organizing principles, agonistic or antagonistic effects, and feed-forward or feed-back mechanisms make it difficult to disentangle governing rules and foundational properties within them. We often depend on external tools to help us see particular patterns that are otherwise non-salient. These tools help us better understand narrow relationships, aggregate and filter large sets of data, run simple analytics, and to simplify complicated networks and processes.
Despite the vast expanse of available data and computing power, current predictive modeling can only provide so much insight. Typical heuristic solutions are effective for tasks like natural language processing, anomaly detection, or recognition/categorization; these techniques, however, must be applied to specific uses and require fundamental understanding about the parameters and desired outcomes. Microscale modeling techniques like Agent Based Modeling (ABM) are capable of representing systems of interconnected entities to elucidate emergent principals, but require defining of relational properties. Yupana attempts to combine the principles from these processing and modeling techniques into a cohesive and modular framework.
Yupana seeks to channel the power of machine learning and ABM towards mapping a data ecosystem without directly relying on end-goal considerations. To accommodate drastic changes in the environment that would otherwise render a simulation model obsolete, Yupana will need to consume asynchronous data updates from customizable data streams. The simulation framework of this project must be developed to adapt to many different systems of inquiry. This means allowing integration of an array of different processor types, as well as easy integration of new sources and modeling parameters.