text: Introduction – Zephyrus Informatics
  I have wanted to produce meaningful content for sometime, but I have found it difficult to know what would be appropriate to share and where (personal, business, client, brand, et cetera). My interests have varied, seemingly per day. Yesterday, I shared this intention with my sister, who pointed out that I am always talking (info-dumping, ha!) about feedback systems and value-driven behaviour. This observation made me chuckle because, not long earlier (on the same day), I had info-dumped these very same topics upon my mother. It seems that there is a theme across all of my interests, after all — one that I care very much to develop and share.
  I am, in short, fascinated with information. How it is stored, how it is transmitted, how it can be effectively received, how it is recalled, how it can be dissected into data elements, how it can be matured to knowledge, how it is encoded into animal instinct and genetic code, how it is (potentially) the fabric of our universe.
  I think that this interest began to emerge while studying for my undergraduate degree. At that time, I approached it through the question of the interaction between language and cognition: how does language shape thinking? There are many approaches to this question! Neuroscience looks at brain and neural structures and information processing; biological studies explore the coevolution of human anatomy and language capacity; anthropological studies consider human symbol-making and cultural evolution.
  Of all of the approaches, one that intrigued me the most (because I knew the least about it) was an investigation of whether and how gender could be encoded upon AI. As I recall, the hypothesis was something along the following lines. First, two assumptions: (1) there is a reciprocal relationship whereby computer logic shapes human thinking and human thinking shapes programming (see present-day discussions on algorithmic bias); (2) computer programmers are disproportionately of male gender (this research was published in the early 90's). If we examine that reciprocal influence between human thinking and computer "thinking", then we can see how gender is being encoded into computer programmes. Well before the modern push for women-in-STEM fields, this book inspired my interest in the technological field by arguing that "human" values would be better reflected if a diversity of cultures and genders were developing computer code.
  I'm pretty sure that I info-dumped this theory to my previous employer when I met him, and this is a big reason why I was hired. For the past four years, I've worked for this employer, studying the intersection of technological solutions and defense applications. Over this period, I shifted from studying the brain, and started studying decision-support information systems, the defense acquisition process, and Marine Corps "sense and respond" logistics.
  The DoD has a sincere obsession with "innovation". The overabundance of this term in all inter-agency literature prompted me to explore with increasing depth, all of the modern theories of innovation. Much of what is currently published on this topic builds (meagrely) upon the innovative management theories that emerged from Japan in the late 70's/early 80's. These concepts, such as continuous improvement, lean manufacturing, kaizen, hoshin kanri, etc. were themselves leap-frog developments in management science initially introduced to Japan by American theorists in the 50's (during their post-war reconstruction).
  And down the rabbit-hole we go!
  I now have a growing collection of canonical books in the topic of innovation management /management science, which, happily, do *not* include any of the mass-produced, contemporary shite. Although we are culturally obsessed with innovation, we (the American-defense agencies) are still stuck trying to successfully implement strategies introduced in the 80s. Meanwhile, technological developments are changing the global environment and the very nature of warfare. The DoD's obsession with implementing innovation is well-founded. But how do you modernise a megalith like that?
  Two years ago, I undertook a year of graduate studies in decision analytics because the course list reflected what I was writing about at a theoretical level, and I wanted to have hands-on, tacit knowledge of their applications. I left the programme before completing the second, final year, because I decided that I wanted to study the mathematical theory operating behind these technical programs more than I wanted to learn the various (quickly obsolete?) technologies themselves. However, it was during this time that I encountered my first understanding of push/pull analytics (and Boyd's OODA-loop).
  Writing about the needs of innovative organisations introduced me to systems thinking and systems design, which introduced me to the concepts of open and closed systems. Open systems produce information that does not return to inform future decisions and actions; closed systems are feedback systems, which capture and respond to information taken from the operating environment. Together, my work to rebrand "sense and respond" logistics for the USMC, my research into theories of innovation management, and my studies of push/pull analytics supporting data-driven decisions all began to inform an understanding of enterprise innovation as a systematic process of knowledge life-cycle management.
  Most recently, I picked up a book on communication theory from a local college bookshop where I discovered the author Stanley Deetz. He researches the industrial paradigm of control-based decision-making within organisations. I immediately saw parallels with principles of systems design and adaptive vice efficiency models of organisational design, with new context from the field of organisational communication. I told my sister to prepare for info-dumps on control vice collaborative models of decision-making. She laughed, but I'm not quite joking — this is fascinating to me, and I can perceive its relevance across domains.
  Through all of these different frameworks, there is an underlying consistency: information.
  Innovative organisations create closed, feedback systems with their customers that use both internal and external information to shape decisions as responses. An innovative organisation is a "learning" organisation that networks information, pushes knowledge out as meaningful products/services, and pulls knowledge back in to inform the system. Likewise, an "intelligent" computer system employs some model of responsive (or other) "learning" to adapt decisions to both internal and external input. Effective project engineering filters strategic streams of information into predictive models to support optimal business decisions.
  Synchronicity is such divine bliss.
  The purpose of this blog is to explore, synthesise, and discuss various applications of a diversity of theories from across information, communication, and management fields.
  I want to both share my excitement for these various theories and frameworks as they are discovered and integrated, and also to give room to this focus for further development and refinement over time.