How to Manage Uncertainty with Six State Logic

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Need to manage uncertainty?  This requires recognition of the type uncertainty you are experiencing and creation of an associated action plan.  Here are the four types of uncertainty identified by my Six State Logic framework and recommendations for how to manage each.

Six State Logic Graphic

First is Knowledge Uncertainty which makes it impossible to justify any specific action.  Proof of Knowledge Uncertainty is required to ensure process consistency. The only acceptable management plan for Knowledge Uncertainty is right-sized resource allocation to search for actionable knowledge.

Second is Action Uncertainty.  This is when actions can’t be justified because of evident conflicting facts. The management plan is to first verify the facts are known and not time dependent or otherwise uncertain. Next, consensus teamwork and typically financial analysis is used to make difficult decisions.

Third is Pessimistic Uncertainty, which is a combination of Known Negative facts and Knowledge Uncertainty. Both characteristics must be proven and a threshold set for the extent of uncertainty that management is willing to accept to implement a negative action plan like terminating a project.

Pessimism is a human emotion that affects judgement. Teams must not overemphasize negative facts to avoid taking unjustified negative actions.  Teams must also not overemphasize Knowledge Uncertainty to avoid unnecessary delay in taking justified negative actions.

Fourth is Optimistic Uncertainty, which is a combination of Known Positive facts and Knowledge Uncertainty.  Like pessimism, both characteristics must be proven and thresholds set for the extent of uncertainty that management is willing to accept to justify implementing a positive action like investment.

Optimism is also a human emotion that can cause teams to mistakenly overemphasize the need for positive actions or to try to avoid taking justified positive actions. The making of overly optimistic or pessimistic decisions can also result in an equally unjustified and innovation capacity killing influence on future decisions.

Everything said above should be familiar to experienced managers. The new emphasis is the use of universal Six State Logic criteria to characterize uncertainty and identification of common uncertainty management challenges that an increasing number of stakeholders can actively help avoid.

The result is a more consistent, effective, efficient and universally applicable uncertainty management process. This engages more people in the effort of making better decisions and improves innovation capacity by reducing the uncertainty management anxiety associated with all truly new ideas like, for example, Six State Logic.

Best Regards,
Michael Klasen, PhD
+1 (503) 442 6524
mike@ciusers.com

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Best Thing Since Zero: Six State Logic

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Six State Logic is an improved way for humans and machines to answer simple questions like “How are you?”  Let’s skip the theory and look at some examples.

Six State Logic Graphic

Machine    (5) Known Good – All systems operational.
Human      (5) Known Good – Feeling great, got a raise.
Machine    (4) Seems Good – Working on new data set.
Human      (4) Seems Good – Feel good with no plans.
Machine    (3) Mixed – Some functions working, some not.
Human      (3) Mixed – Getting through a challenging day.
Machine    (2) Uncertain – Running a self-diagnostic.
Human      (2) Uncertain – Sleeping.
Machine    (1) Seems Negative – Trying to reboot to recover.
Human      (1) Seems Negative – Way overloaded at work.
Machine    (0) Known Negative – All functions unavailable.
Human      (0) Known Negative – Sick with the flu today.

So what? First, Six State Logic answers + reasons why + appropriate actions enable everything and everyone to communicate about any topic (beer, missiles, IRAs, cancer, poodles, etc.) without need to create topic-specific criteria or questions in advance. Yes, I believe Six State Logic enables both immediate and universal communications.

Second, logical and mathematical operations like matching, searching, comparing, weighting, averaging, if-then structures, action thresholds, status reports, etc. work better with rich Six State Logic information. This means IoTs and people can “get to the point” quicker and competitively improve most existing software applications.

I end with an example. Yesterday I updated an application and was shocked to see an entirely new and clumsy user interface. A wise software provider would ask “Is this better for you?” and enable continuous Six State Logic feedback instead of forcing open loop updates and losing my business in the process.

Will your competitors use Six State Logic to take your customers?  Contact me to learn how to use Six State Logic to attack, or defend against, completion and to (Where have I hear this before?) make the world a better place.

Best Regards,
Michael Klasen, PhD
(503) 442 6524
Mike@ciusers.com

Logic Evolves to a Six State Framework

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How many logic states are required to correctly communicate the status of reality?  It depends on the type of reality. When it works, a digital on/off indicator answers the question, “Is the oven on?” nicely, but we then need to add a third Uncertain logic state to communicate reality when the reliability of this type of simple 2 Stage digital data is in question.

A key benefit of 3 State “Real Digital” logic is confidence in the correctness of a digital logic answer to a positive or negative format question. Knowing an uncertain signal is available many times leads to better action-ability of results while the lack of explicit verification of digital logic reliability can lead to distrust of the entire question and response system.

A common symptom of a faulty system is users being forced into a yes/no response they are not prepared for and the result being displayed as if the Uncertain choice was originally available. For example, what would have happened if an “Uncertain” ballot choice appeared beside Trump and Clinton in the 2016 election?

If we need to communicate real world data that can’t be shoehorned into a digital format answer, then a third “Mixed” logic state is required to describe a blend of Known Positive and Known Negative data.  Uncertainty information is still required to assure data meaningfulness and to avoid the issue of users trying to pass off highly uncertain and therefore insignificant data as being significant.  I call this common form of logic “4 State Real Data Logic.”

4 State Logic can’t communicate human emotions well, but we all try to use 4 State Logic for this purpose because it seems to work so well for both technical and financial data. Consider the question, “Do you feel global warming is real?” and an answer like “60% positive with 50% error” which is close to useless. Science and business professionals are well aware that almost all data has some level of associated Uncertainty and the pitfalls of treating emotional responses or guesses as facts.

The next step is logic evolution is what I call “5 State Emotional Logic” which works well for the purpose of communicating human emotional intensity.  It is commonly seen in survey answers in the form, “Strongly agree, Agree, Somewhat agree, Uncertain, Somewhat disagree, etc.”  5 State Logic has many advantages when used for this purpose, but backward compatibility with 4 State Logic is not one of them. This is because 5  and 4 State Logic communicate entirely different information. Using 5 and 4 State Logic interchangeably or incorrectly is a root cause of the fake news phenomenon.

What is required to improve human and machine communications is a way to unify 5 State and 4 State logic into a universally applicable framework that can communicate both real data and emotional information.  This is exactly what 6 State “Universal” Logic does.  By bridging the 5 and 4 State Logic worlds, 6 State Logic creates a common language that both machines and humans can use to reliably communicate the answers to positive or negative questions.

The benefits of placing machine and human communications within the same logic framework are numerous.  For example, consider all the possible 6 State Logic answers and reasons why that result from asking a large group of people the simple question, “Is global warming real?”  The combination of 6 State Logic real data and emotional answers provides all the information a machine or human requires to create a next step action plan for seeking consensus among the individuals that answer that type of question.

Similar 6 State Logic communication benefits shown by this simple example also extend to many applications like e-commerce, search, and the augmentation of both humans and machines to perform white collar work.  I believe 6 State “Universal” Logic is especially important in the fast approaching age where augmented humans collaborate to enable “Super Intelligences” that, with luck, will become the type untiring and trying-to-benefit everyone business and government leaders that many people desire.

Evolution of Multiple State Logic

Decision Teamwork in Uncertain Environments using Six State Logic

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170808 Decision Teamwork In Uncertain Environments

The attached presentation describes consensus algorithms using the Six State Logic human/machine language coding system. Rapid and accurate Six State Logic consensus algorithms are required to support human power AI implementations. I personally would want a combined AI/response team to use this method if I was waiting for an answer a question like, “Can you keep looking for the type of house my family wants?”

Best Regards,
Michael Klasen, PhD
Mobile: (503) 442 6524
Skype: michaelklasen
Email: mike@ciusers.com
Website: http://ciusers.com
LinkedIn: http://linkedin.com/in/klasen
Twitter: http://twitter.com/michaelklasen

Easing Elon’s Artificial Intelligence Angst

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Artificial Intelligence seems scary, but fear of technology misuse can also apply to every human created tools since the stone hand ax. What is needed is a framework to control AI and human interactions rather than focus on the potential damage AI technologies like machine learning can do. Here I present the Six State Logic (6SL) framework for AI control to help ease Elon’s AI angst.

6SL enhances digital logic by adding one extreme uncertain logical state and three intermediate logical states between positive, negative, and uncertain. 6SL improves how we signal the intersection between human desires and an AI’s authorization to act for a human. More specifically:

  1. Known Positive and Known Negative 6SL states signal authorization for an AI to proceed with a known positive or known negative action without additional information exchange.
  2. Uncertain 6SL state signals willingness to accept new information that may support future action, but forbids an AI from taking any action on behalf of an Uncertain human or other AI.
  3. Seems Positive and Seems Negative 6SL states signal a combination of known and unknown factors, no AI authorization to act, and desire to collect more information
  4. Mixed (Known Positive and Known Negative) 6SL state signals indecision due to equal choices or overload, no AI authorization to act, and no desire for additional information

Use 6SL to signal appropriate action and information exchange during AI to human, AI to AI, and human to human interactions. 6SL assures both sides in any interaction correctly signal their desires and can anticipate resulting actions. 6SL AI behavioral programming takes the form of collections of interactions we can think of as “cells” that each contain: (1) A positive and negative topic question designed to elicit a 6SL response, (2) 6SL response and optional reasons for  that specific response, and (3) definition of authorized actions that correspond to the 6SL responses and reasons why.

6SL behavioral programming for AIs starts with the simple topic question. This syntax simplification removes the complexity of natural language processing from AI and human interactions. The interpretation of each 6SL state also help users extract all available information to support next step decisions. Lastly, 6SL defines two modes of operation that are safe from undesired AI or human actions.  These are Uncertain, which desires additional information to complement what is already known, and Mixed which does not desire additional information.

So Elon, please consider using the 6SL control framework described here to relieve angst about “AIs gone wild.” Accidents are bound to happen, but 6SL helps protect AIs and humans from pursuing inappropriate actions. enables more productive information exchanges, and defines a simple-to-implement AI behavioral programming framework.

Michael Klasen, PhD Six State Logic Prospectus

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170811 Michael Klasen Six State Logic Solution Architecture Prospectus

Michael G Klasen
Lake Oswego, OR 97034
503.442.6524 | mike@ciusers.com

I WILL

  • Be a prolific Innovation Data Scientist that generates sustainable enterprise-wide value from implementation of Six State Logic product architectures and innovation process improvements

I WILL ACCOMPLISH THIS BY

  • Applying 15 years of Innovation Data Science PhD and applied research and 20 years of technical product management experience to creation of new products.
  • Demonstrating the financial benefits of using Six State Logic (6SL) solution architectures to create new applications that:
    • Improve on the quality of yes/no format questions and answers
    • Use the clarity of known 6SL states created by extracting uncertainty to enable acceptance of 6SL based automation and augmentation applications
    • Improved human to machine communications for applications like IoT by using 6SL as a common language to describe both data and emotional uncertainty
    • Emphasize human value by focusing on human managed 6SL blended states that require decision thresholds, team consensus, contingency planning, etc.
    • Establish qualitative standards for justification of 6SL state selection to enable immediate recognition by humans and machines of the implication of 6SL state selection
    • Directly link 6SL states to justified next step actions which improves the quality and transparency and actionability of human and machine decisions
    • Advance market research technology with 6SL data collection that simulates professional survey results without the need for expensive survey preparation
    • Enable individual innovation activities with machine augmentation which significantly reduces organizational cost-per-innovation.
    • Use ability to split indecision into uncertain and equal choice root causes to enable more effective decisions
    • Use ability to merge uncertain and equal choice causes of indecision to linearize 6SL data collection and enable 6SL search.
    • Use derivative 6SL data topics like status, change of status, and influence on status to collect and illustrate deeper level insight and better decisions
    • Use related and independent 6SL feasibility data about strategy, finance, technology, etc. to enable portfolio management algorithms for investment prioritization.
    • Use dense 6SL data dashboards elements with insightful and visual data augmentation.
    • Use 6SL algorithmically driven team member input comparisons to maximize the quality and minimize the costs of team consensus decision work.
    • Use human 6SL data weighting and voting methods to enhance the speed of consensus teamwork to approximate individual decision efficiency without loss of team accuracy
    • Integrate business sector-specific knowledge and all 6SL data management and decision process support advantages into a comprehensive ideation management solution
    • Calculate universally applicable satisfaction search for database items based on target differences, more/ less is better, range is better, and 6SL data matching criteria
    • Conduct multivariate searches using continuous target, database, and importance weighting data to generate ordinal “#1 best match” search results
    • Show the distribution of search satisfaction for alternative collections of search criteria to explore and recommend insightful search criteria alternatives
    • Apply 6SL multivariate search to automation of machine agent transaction networks that function based on real time human preference inputs
    • Enable multiple topic search dashboards that accept preference inputs, show prioritized search results, and forecast alternative agent settings that can yield high satisfaction
    • Provide 6SL based question and answer format translation services across all languages based on simple phrase look up tables.
    • Enable high level and universally applicable behavior programming based on the topic questions, 6SL response, justification alternatives, and defined actions.
    • Enable human programmed Super Intelligences that function similar to humans without crossing the “uncanny valley”
    • Enable human programmed Super Intelligences that can perform human quality controlled tasks at machine speeds.
    • Use 6SL to convert social networking-like messages into personally accountable and highly functional program elements that self-organize to address complex functions.

THE RESULTS WILL BE

  • Immediate return-on-investment from employing the originator and innovator of 6SL to create and promote 6SL thought leadership through your industry
  • Realization of a unique and organizational-wide, 6SL powered IP innovation engine for profitable new product definition and rapid deployment
  • Broad engagement of existing and potential customers and stakeholder to expand the revenue base into unforeseen applications like industrial IoT, market research, etc.
  • Outstanding innovation team support for industrial application development by organizations that have no current industrial product plans

Michael Klasen, PhD Industrial Resume

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170811 Michael Klasen Resume

Michael G Klasen
Lake Oswego, OR 97034
503.442.6524 | mike@ciusers.com

Uniquely qualified technical sales, marketing, business development and innovation manager. Natural team leader with outstanding written and verbal communication skills. Exceptional builder of creative potential through well-trained application of Six State Logic based innovation management techniques. Proven track record of best-in-class sales performance and sales team management enabled by a consultative and friendly management style. Broad electronics and electro-mechanical know how from designing and selling semiconductor, power management, RF, software, instrumentation and industrial products. An eternal optimist and fan of good people working smartly together to achieve exceptional customer value.

AREAS OF EXPERTISE
  • Ideation Stimulus
  • Business Modeling
  • Negotiation Strategy
  • Feasibility Investigation
  • Market Development
  • International Business
  • Program Management
  • Sales Presentations
  • Strategic Planning
  • Account Management
  • Innovation Risk Control
  • HR Development
PROFESSIONAL EXPERIENCE

Eaton Corporation, Tualatin, OR, 2013.1 – PRESENT
Product Marketing Manager (Power Conversion and Management)

  • Manage a $46M DC-AC and DC-DC power conversion business.
  • Ensure global product line profitability and operational excellence.
  • Launched $6M revenue, 400% growth strategy for new inverter product line.
  • Lead 100 person team interdivisional product sales campaign.
  • Conceptualized multiple patent disclosures for power control products.
  • Initiated and lead all aspects of >$100M/year sales potential new product line

Eaton Corporation, Tualatin, OR, 2012.6 – 2012.12
Program Manager (Laser Drive and Embedded Power)

  • Managed operations, sales, and NPI for $10M laser power supply product line.
  • Facilitated 35 person factory move from Pittsburgh, PA to Tijuana, Mexico.
  • Managed all aspects of a $5M revenue new LED power supply product line.
  • Advanced key customer relationships during challenging price negotiations.
  • Worked through difficult personnel, IT, and other production startup challenges.

Eaton Corporation, Tualatin, OR, 2012.1 – 2012.5
Project Manager (New Product Development and Recall)

  • Managed internal team and legal reporting requirements for $2.5M product recall.
  • Improved integration of Engineering Change Controls into stage gate NPI system.

Robust Decisions, Corvallis OR, 2008 – 2011
Startup Research and Business Development

  • Worked with David Ullman, PhD, Oregon State University on decision software NPI.
  • Developed sales and marketing plans for government and commercial markets.
  • Created original IP and established independent company for commercialization
  • Developed and implemented sales, marketing, business development initiatives.

Mentor Graphics, Wilsonville, OR, 2006 – 2007
Business Development Consultant

  • Created high ROI recovery strategy proposal for parasitic extraction product line.
  • Initiated Electrical Design for Manufacturing with 30 person commercial team.
  • Developed and secured support for external technology acquisition plans.

Kyushu University PhD Program, Fukuoka Japan, 2004 – 2006
Innovation Uncertainty Management Dissertation

  • Completed Industrial Economics PhD from Kyushu University, Fukuoka Japan.
  • Self-published book titled, “Innovation Uncertainty Management”
  • Conducted post-doctoral research for one year in Scottsdale, Arizona.

TriQuint Semiconductor, Fukuoka Japan, 2001 – 2004
Japan Country Sales Manager

  • Managed team of six Japanese sales engineers engaged in GaAa product sales.
  • Executed pre and post sales technical application support
  • Conducted research for handset, military, fiber, base station, and foundry products.
  • Increased sales from $500K to $5M by taking business from competitors.

Kyushu University PhD Program, Fukuoka Japan, 1999 – 2001
Dissertation Topic Research

  • Investigated environmental PhD topic before focusing on innovation management.

Carraro SPA, Fukuoka Japan, 1997 – 1999
East Asia Sales and Marketing Consultant

  • Managed $7.5M product recall in Korea for thousands of transmissions products.
  • Generated $2M of incremental business in Australia, Indonesia, Japan, and Korea.
  • Manage $35M Korean factory acquisition from Samsung Heavy Industries.

Datatech, Tokyo Japan, 1995 – 1997
Technical Support and Planning Manager

  • Transferred diagnostic repair system Tokyo Electric meter reading system.
  • Developed proprietary power line carrier communication system for Panasonic.
  • Lead new product introduction effort for ruggedized laptop PC product line.
EDUCATION

PhD, Industrial Economics, (Innovation Management), Kyushu University Japan

MA, Pacific International Affairs, (Japan Business), UC San Diego, San Diego, CA

BS, Electrical Engineering and Physics, (Microwave), Penn State University PA,

BA, Math and Natural Sciences, Edinboro University of Pennsylvania, Edinboro, PA