Machine-augmented Mindfulness
Image via Creative Commons

Machine-augmented Mindfulness

to project awareness and think coherently across organizational discontinuities and spans of time.

(see also richardarthur.medium.com)

Mindfulness refers to a state of holistic awareness. Meditation and yoga practices center focus on one’s immediate place and moment in time — as a cornerstone of coherence from which to contemplate context. Mindfulness provides a strategy to think and see clearly — despite uncertainty, without becoming overwhelmed by one’s VUCA surroundings (volatile, uncertain, complex, and ambiguous).

In “The Minding Organization”, UCLA’s Moshe Rubinstein and Iris Firstenberg frame strategies and highlight anecdotes for problem-solving by “envisioning the (desirable) future and bringing it into the present.” One example the book provides is pausing to pour concrete sidewalks for a new building until able to observe paths worn-in to surrounding turf — perfectly fitting emergent behavior of humans.

“It’s a poor sort of memory that only works backward” — Lewis Carroll

Continuum Mindset

The mind of an individual can struggle with constraints imposed by time: poverty of time afforded for agility or urgency, or long intervals of time fading coherence of thought into unreliable memory. Collective coherence of thought across an organization can degrade further as decision-making and attention become divided between subgroups or distinct (and role-changing) individuals.

Emergent opportunities can be lost due to inadequate investment in preparation — and preparation squandered in failing to recognize opportunity to act demonstrate two fundamental failure modes in competently executing plans, duties, functions, or responsibilities. (or collectively across an organization).

For example, suppose a patent is granted three years after submission — but the applicant has since transferred to another division and focused on entirely different matters, while the new team is unclear on how to employ this in their current strategy. Or consider how a cancer patient might experience inconsistent testing, therapies, and guidance over years of appointments with ever-changing specialists.

A continuum mindset pursues coherent (consistently logical) thought over spans of time and across organizational discontinuities. Leveraging the complementary strengths of people and machines, we can devise digital knowledge systems to overcome human limitations and augment cognitive mindfulness in decision-making — to awaken the enterprise.

Collaboration between Minds and Machines

We can bring an envisioned future of methodical mindfulness into the present through a potent collaboration between people and machine technologies. Decades of sustained innovation in computer technologies has driven increased performance and capability to process and store data.

As legacy systems such as Google Search have mastered ask-respond “discrete event Q&A”, humans developed proficiency in iteratively sifting through results for relevant information. New interactive systems have begun to emerge, leveraging large language models (LLMs) such as GPT, to facilitate end-to-end problem solving — taking more effort and expertise, but finding better results through “campaign research” or “slow search”.

U.S. Sec. of Defense Donald Rumsfeld, Feb. 12th, 2002, “

Emerging technology shows promise to transform information systems from tools we direct to knowledgeable machine-collaborators. Consider the potential capability to answer questions such as:

  1. What is a known unknown relative to my query?
  2. What unknown unknown did this query reveal as a new known unknown
  3. Under what conditions might a known unknown become known?

A potential answer to the last is simply “time” — e.g., “If the patient continues at current dosage for 3 years without adverse effects or developing tolerance to the drug, then patient may resume consumption of alcohol in moderation.”

This illustrates the shift from an event mindset to a continuum mindset — posing a question that endures until answered or retracted. In the preceding article, we describe Decision Provenance as a framework to capture and query unknowns and assumptions corresponding to consequential decisions [Fig-1].

Figure 1

Digital agents aware of those knowledge gaps of the present can then assiduously (persistently, judiciously) monitor for revealing information — onward into the future [Fig-2]. Discoveries relating to an unknown or assumption then trigger re-evaluating associated decision(s), then informed by that emergent future context.

Figure 2

This allows more confidence when committing to a decision— reducing undesired time, effort and conservatism imposed by uncertainty. That is, the system can act as a “safety net cast into the future” hedge. The decision-maker can agree conditionally, aware that any of her consequential concerns (such as known unknowns & asserted assumptions) will be recorded, and then persistently monitored for contradiction or confirmation.

Enabling this continuum mindset empowers a decision-maker (in the present) to act with the advantage of future knowledge — by asserting adaptation-by-design through decision provenance.

Examples

Let’s consider a decision to select design 4.3 for Part-X of a wind turbine. The design-time contextual Decision Provenance for that decision can then archive something like the attributes shown in the notional example [Fig-3] — capturing this sensitivity as known unknowns and assumptions, along with the data supporting the trade-off decision between alternatives and methods for evaluation.

Figure 3: Notional

The known unknown: “Will this wind turbine need to operate above an elevation of 10,000' /3km?” could be answered with “no prospective customer orders known to date — if this changes in the future, an alert will trigger.”

Suppose 5 years later a wind farm order is placed by La Paz, Bolivia. While monitoring the company’s ERP, the assiduous agent performs a lookup on new order locations, and upon identifying the elevation of La Paz as 3640m (above the 3000m concern noted in the assumptions), triggers an alert to Ann, Bob and Cathy referencing the recorded assumption and the design decision impacted. The associated risk factors can then be re-addressed in context of the specific contract.

Figure 4

Revisiting the design choice, the elevation assumption concern is found to relate to selection of composite material A, based upon criteria at the time of the original design (required properties and cost). But, in the five years between the design decision and this order, Bob developed a cost-effective material B less sensitive to altitude factors that raised concern for material A. Referencing the data and analyses previously employed to rate alternatives, Bob can re-evaluate adding B to the candidates — updating the selection criteria appropriately for this new context.

In effect, through its decision provenance, the decision-makers employed a continuum mindset to attune the systemic awareness of the organization to act despite uncertainties, while preserving robustness as new events unfold.

Guided Learning

The Awakened Enterprise structure can facilitate learning: guiding pursuit of significant assumptions and unknowns to research — as well as the study of the decision-making process itself, which is often opaque or clouded by organizational amnesia (an all-too-familiar experience with the cognitive limitations of humans, the passing of time, and inevitable changes to work focus and the workforce.)

Consider awareness to investigate: “What allows our team in Moline to assume 5-year maintenance intervals using lubricant Y — but our teams in Houston, Deerfield and Lansing assume 3-year intervals and use lubricant Z?

In other situations, resolving an unknown may be prohibitively expensive within a single decision or project, but well-justified when found to be commonly posed in concert with other projects.

For example: “What are the top three known unknowns across the designs of our gearbox product family?” (and consequently) “What limits our ability to know? sensors with improved noise-suppression? new experimental test conditions? a higher fidelity model?

The continuum mindset lends the power of asking and answering questions across the span of time.

Awakened Enterprise

Systematized data gathering and knowledge management underlies the Awakened Enterprise strategy to employ methodical mindfulness in decision-making processes — while taming complexity and pursuing agility vs. volatility, understanding vs. uncertainty, and judgment vs. ambiguity

This visibility across the enterprise allows executive decision-makers to intervene with potentially simpler, more proactive solutions compared to unproductive and inconsistent solutions in the absence of such a view.

However, executive surveillance of enterprise decision-making should not institutionalize micromanagement or fear-of-reaction — potentially resulting in adaptive human behaviors suppressing the candor and transparency that make the practice valuable.

“Judge a man by his questions, rather than his answers.” — Voltaire

Through qualified accountability — moderation in judging with hindsight, retrospectives should consider the constraints and context for the decision.

The continuum mindset enables opportunistic adaptation to emergent changes in the future — that impact decisions made today, which had been limited to knowledge of the past and present.

? 2020 All Rights Reserved. Revised 2024.


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Brian Kennedy

Founder & CTO, Targeted Convergence Corporation

3 年

You mention “known unknowns” and “unknown unknowns” … both are knowledge gaps that you should work to close before you make decisions. The former we can clearly make visible, allowing us to innovate ways to close that knowledge gap, estimate the cost of those ways, and then mindfully make decisions on whether it is better to invest in closing that knowledge gap, or to move forward with decisions that will be successful regardless of where that missing knowledge ends up, despite the VUCA.?(You can also decide to take a guess and move forward with risk that your guess is wrong; but we prefer to avoid that path, preferring paths where “Success is Assured”, rather than carrying risk.) Much harder is to deal with those “unknown unknowns”. One of the key criteria that we focus on when devising visual models for decision-making is the degree to which those visual models can make “unknown unknowns” visible. For example, our Decision Map often does so. When coaching teams, I often look at their Decision Maps and, even though I lack any particular expertise on their knowledge, I can point out “unknown unknowns”.?“Based on your Decision Map, this problem should be easy to solve: to reduce this, you can just drive this down; so, something must be limiting this… what is it?”?If they don’t know the answer, an “unknown unknown” just became a “known unknown”; and now they know to look for who might know that, or can start innovating ways to test or otherwise close that knowledge gap. We’re always on the lookout for visual models that expose “unknown unknowns” that we can add to our suite of visual models. Good stuff.

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