Innovation, Communication, and other Theories of Special Interest
Reinforcement learning is one of several breads of “machine learning” approaches within the field of computer science. However, at its origins, reinforcment learning is leverages the science of behaviour in an attempt to .
I took the StrengthsFinder assessment last weekend and renewed an obsession!
Have I mentioned before that I love frameworks? I do. I tend to gravitate to anything promising both depth and structure. Then I dissect it and synthesise its components into my internal web of interconnected frameworks. Each newly-internalised framework re-shapes the overall structure of my mind map, which is just thrilling.
I did not initially gravitate to the Clifton Strengths framework. I first took the assessment in late 2016. I was working as an analyst on a new government project, and we initiated our teamwork with the strengths assessment. I had so much fun learning about the talents of my colleagues, and mapped a plan for how we might best leverage each other. Then, I closed the book.
Then I discovered Gallop’s Strengths Based Leadership, which offered a new level of depth by grouping the themes á la Keirsey into quadrants. Wow, now the strengths work with the Myers-Briggs type theory, with some intriguing deviations.
In Please Understand Me II, David Keirsey introduces a historical overview of “character/ temperament typing” that I reproduce here:
Interestingly, C. J. Jung’s cognitive functions are not included. This is because Keirsian type theory focuses on observable behaviours of personality vice the cognitive functions themselves. Keirsey’s central idea is that the rapid-ability to “type-cast” people into one of four categories enables one to better empathise and relate to the motivations of others. So the focus is less personal development, and more other-relating (typical NF).
While these groupings do not perfectly align to produce one, universal perspective, (which would be awesome), I do think that they are all striving to describe four important axes (4^2 = 16 types) of human cognition.
So, choose your axes and the appropriate framework will follow.
Myers and Myers-Briggs, and Jungian typologists focus on the axes of:
Keirsey and modern business-world oriented frameworks (to include Gallop and Personalysis) focus on the axes of:
Now enter the revised Clifton Strengths as “domains”. I find it so interesting that the granularity of the identified strengths collapses back down into a quadrant framework.
From a universal framework perspective, Clifton’s 34 “themes” really should be condensed to 16. Alternatively, the next axis could be defined, which would square the number of types to present 64 potential talents. But how useful is a list of 64 strengths when trying to work well with others?
Because the number four reflects a most-basic and universal number sense among humans, a framework of four categorisations may most-easily apply to real-world, type-casting scenarios.
To me, this means that the focus for application might be better spent, not so much on the top five of 34 talents and on my uniqueness, but rather on the full-quadrant’s score and on the dominant quadrants of my team members.
For example, I scored highest in strategic thinking and executing “talents”. But my influencing and relational dimensions are quite limited. That is simple for me to understand, for me to articulate, and for others to understand about me.
With a quadrant model, it is also easy to identify where dominant preferences are an asset and should be leveraged, and where one should seek out specific preferences to fill a needed gap.
And, to make it really intuitive, we can add colour coding!
She is red and green, and he is blue and yellow. We need blue for planning this product, and red for executing this project. Let’s leverage him here, and put her there.
Interestingly, Gallop’s position that teams should strive to be well-rounded runs counter to Don Clifton’s theory that natural talents should be leveraged and trained up as strengths — basically, don’t waste time on the lower 29.
I disagree with the proposition that teams should be well-balanced in all four quadrants. I feel like an insistence on this balance would potentially produce high-conflict teams, where members do not find much in common due to core-value differences.
Perhaps this is because I work in IT. I work with a lot of intuitive “thinkers”. Many of us prefer to work with (and work well with) other intuitive thinkers. Stick a naturally-inclined sales person in our mix, and we might strangle them.
Where I have noticed or experienced conflict is less along the thinking/feeling axis, and more along the judging/perceiving axis or the intuitive/sensing axis. In function-type theory, thinking and feeling are both identified as rational functions, and sensing and intuition are identified as irrational. In terms of connection and communication, differences matter most in terms of our irrational preferences. We see these tensions best managed by Keirsey in his selected type groupings: SP, SJ, NT, NF.
The strengths framework offers “influencer” as a new category, possibly with an “executor” counterpart. (So, “doers” vs. “talkers”, lol.) This framework, like Keirsey’s, speaks to the inherent tension between judgers and perceivers. As an achiever, I do NOT want to work with people who do not deliver. But I do understand that I need to hand-off my solutions to a strong influencer, who can build the advocacy and buy-in that I would never have patience or skill to go after.
From a system’s perspective, the team may be understood to have its own type preference representing the collective talents and preferences of its members in relationship. Groups may have their own psychologies, after all.
A teams-focused strengths-based strategy would leverage cross-team collaboration, enabling each team to specialise in their dominant strengths, and then hand-off or leverage other teams along different phases of product/service development.
(Wow, I really see that individualisation preference now…)
Having once immersed myself within the infamous “KonMari” de-cluttering process, I understand the importance of maintaining tidy surroundings to one’s peace-of-mind. Mari Kondo is a Japanese tidying wizard, whose philosophy consists of surrounding oneself with only those possessions that “spark joy”. Her revolutionary method applies this philosophy categorically, working outward from the largest buckets – such as clothes, shoes, and books – to the most granular – paper, keepsakes, etc.
Within the Kon Mari method, the “paper” category refers to both physical and digital artefacts, and I repeat this definition here. Mari Kondo does not recommend keeping paper, particularly because of its cumulative tendency. Her advice is to “toss it all”, with three categorical exceptions:
This sounds simple enough… (Spoiler Alert!) It’s not.
In my experience, paper is the unwieldiest of all the KonMari categories. And it makes sense: not only is tidiness entropic (to maintain a higher-degree of imposed order, tidying must be re-engaged iteratively), per Claude Shannon, information itself is entropic.
This selective pressure entirely sums up the corporate buzz around “data-driven decisioning” and “managing data-as-an-asset”:
Paper is one essential medium of information, which coveys a set if potential meanings across the enterprise. For this reason, many companies have “record management” policies. Yet, while such policies validate the importance of information retention, they fail in terms of assuring the usability of that information-as-an-enterprise-asset.
Rather than spontaneously emerging as a corporate asset, growing collections of archived information grow noise exponentially. As the volume of information increases, the sets of potential meanings within that volume grow, thus expanding the entropy of the information.
Despite several decades of promise, AI-embedded “learning systems” nevertheless are handicapped in their capacity to extract meaning from information – their ability to deconstruct information into actionable insights remains highly-dependent upon human design and interpretation. What we can do innately as thinking, thinking man(kind), embedded learning systems can yet only approximate for highly-specific purposes.
And herein lies an opportunity for us both personally and professionally as knowledge workers.
Information is only useful when it is at work, i.e. when it produces meaning. The management of information, therefore, is a process that must support the optimal production of meaning – for ourselves, our families, our teams, our departments, our clients, our offices, the enterprise, etc. Otherwise, information devolves into noise.
Decisions for what to save and where to save information, therefore, must reflect the utility of that information to produce relevant meaning across different contexts.
Mari Kondo’s no-nonsense approach to tidying “paper” is just as relevant to the enterprise as it is to the individual. And following her simple criteria is an excellent starting place for overhauling your own professional information-management practice.
What to obtain that holiest-of-all seemingly-impossible objectives: inbox-zero?
Your computer documents should be subjected to the same rigorous culling:
The key to managing the “paper” category is to regularly process new information through to action. The key to managing innovating is regularly processing new information through to action.
Whoa, look at you, innovator!
Now, share this, and go spark some “paper” joy…
So, I just spent a happy weekend building a data set for a new market initiative. There are several potential uses of this data set from a government contracting perspective. First, I hope to build local relationships with government contractors who may need technical communications or strategic communications support (short term). Second, I hope to partner with local non-competitors as an economically-disadvantaged, woman-owned small business to compete for government contracts (mid range). Third, by understanding geographical counts per trade, I gain an understanding of supply chain “agility” per a given service or product in a given region. Finally, how many of these non-set-aside companies do actually qualify? Potentially, I can determine sub-sets of entities with development needs to nurture and support (long-range).
So far, this data represents what is publicly available via https://SAM.gov.
This week, I’m pin-pointing one city as my first focus, and cross-referencing this information with the awards info that is published on https://beta.SAM.gov. I have about 7k entries in total, and I’ll be leveraging categories to cluster data with immediate value, working outwards in geographic radius. I hope to create some personal connections in this wild, beautiful region, and I’m excited for the excuse to work from the road.
Potentially a skills training video to follow this week, with focus on VLOOKUP and data-cleaning techniques in Excel. I am anxious to see how Wyoming compares to Nebraska (my two favorite states).
This morning, I was talking with a close friend about a decision I am making — one of life-altering variety. I invited them to share in my happiness to which I received a startling response:
I just don’t see that this in line with your values.
This gave me significant pause. This person knows me. They have read the values written on my office window, and they have listened to me spiel about one or another particular value-driven intention on more than one occasion. What the heck did they mean? Of course I am living consistently with my values! Surely they are biased and not seeing me clearly.
Happily, I did not react defensively, and I noticed that I felt unhappily aroused by this comment. I stepped over the window, and I began reading my values. Mentally checking them off. Consistent with this, check; consistent with that, check.
What could they possibly mean? I returned the question to them:
In what ways do you see that I am acting inconsistency with my values?
(And then I read them out to make sure they hadn’t forgotten them). That’s when I noticed a newer addition to the values list.
Justice.
I’m still thinking on that one. I added it to the list after a conflict where I felt that there had been injustice, and I am not fully committed to claiming it as a personal value. Is my since that there “should be” justice indoctrination as a daughter of democracy — liberty, justice, and pursuit of property, for all, etc. — or does it form the cornerstone of my identity?
As a value, Justice exists as if on another realm, completely independent from external influence — think Plato’s theory of Forms. Justice doesn’t give a damn if I believe in her or not. However, by setting “values-driven” intentions, I enter into a feedback relationship with my values, wherein they become the inputs in a system that influences my decisions and shapes my behaviors and actions.
What would a commitment to Justice-as-a-value (JaaV) mean in terms of the decision I am making?
Potentially, there is a conflict, especially as I move beyond the locus of self. Moving outward across longer scopes, I see that the outcome of this decision will eventually lead to an ethical dilemma in terms of JaaV. Is this a trade-of that is justified?
What does JaaV mean in terms of value-driven intentions that I set for myself, for my company?
Am I inherently biased to prefer justice for myself to the point that I may not recognize injustice to others?
There is an injustice created in the production of mobile smart phones whereby one person benefits at the expense of another, unseen, on the other side of the globe — do I reject smart phones?
Just how much injustice is justifiable?
* * *
I don’t know that these questions have clear outcomes. In pursuing them, we enter the murky territory of the dreaded “ethical dilemma”. However, it is only in asking them that one collects enough information to make the best possible decision at a given point in time.
And after experiencing this tangential line of thinking (with just a touch of annoyance towards my friend), I realize that herein this challenge lies a gift: there is room in this collegial “feedback system” for us to ask the really “sticky” questions.
I can trust there to be accountability from my circle that pushes me to peer into my blind spots and gain a more holistic perspective.
Further, I can feel, at a core level, when I am not living in alignment with my values…
(hint: there is the experience of external conflict).
…and I trust that these tensions support an always-improving alignment to my core values.
Feedback systems — with people, with beliefs, with values — are inherently adaptive to new information. Even if there are elements of this decision that turn out to be mistakes, I can trust my ability to learn and to grow, to make the next best possible decision at the next cross-roads.
* * *
Thank you, my friend, for your challenge to me this morning.
And welcome, Justice, as a verified member of the window list! You’re a pesky bugger, but you raise good questions.
Yesterday, I was talking with a colleague, who is learning to execute the first step of a stage-gate process for the purposes of proposal management. Currently, her role is to filter the results of specific FedBidOps automated searches, and to elevate those opportunities that align most with stated initiatives – she is the first of five defined gates within the proposal management process. (Figure 1).
In describing the decision-making objective for this gate, I was suddenly reminded of an image that I saw in my mind as I was listening to an audiobook based on Sun Tsu’s “Art of War”. In the first chapter, “Laying Plans”, Sun Tsu describes five factors to consider when seeking to obtain the field: (1) moral standing (2) timing; (3) terrain; (4) leadership; and (5) management.
Earlier this summer, I developed this mental image into a model (Figure 2), hypothesizing that considering a project in terms of the five consideration helps a team to assure its quality and performance through the alignment of right purpose, timing, resources, frameworks and attitudes, and execution. Further, these five considerations may also lend themselves as factors within a listening framework for capturing and defining a comprehensive understanding of a client’s emerging needs.
I pulled out my sketchbook and showed her an early draft of this model (Figure 3):
“Your goal is to assess both purpose and timing. ‘Right Purpose’ is answering the question: Does this opportunity align to an initiative? ‘Right Timing’ is answering the question: is there enough time to respond?”
This model proved a surprisingly effective tool for outlining a simple context for where this initial gate fits within the entire proposal management process.
The goal of next phase – qualifying opportunities – is to determine: do we have/ what do we need to do to have the “Right Resources”? We approach this determination by researching the requirements against business capabilities and past performances, by assessing feasibility, and by determining a partnering strategy.
Continuing clock-wise around the wheel, the purpose of the pre-proposal development phase can be understood in terms of assessing, defining, and refining the “Right Leadership” of our stated solution. I call this “mentality” in the model because Leadership may be understood both in terms of the empowerment of and alignment to specific people, and as a team or individual’s ability to bring forward the right mentality/approach/framework to problem resolution (i.e. the battlefield of the mind).
Finally, within the proposal development phase, the cost and optimization engineer must determine and structure the cost-volume well enough to support the “right management” both of the awarded contract and of the future-project’s execution so that both the organization and the client profit, while also remaining price/value competitive.
Brilliant! I just love when frameworks coalesce.
Hello and welcome!
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.
🦋
Zephyrus Informatics 