Innovation Uncertainty Management PhD Dissertation by Michael Klasen




This dissertation introduces the Innovation Management Uncertainty (IUM) Theory and Model for management of innovative proposal capture, evaluation, and investment decision-making. The IUM Model provides a theoretically robust solution for managing strategic uncertainty, resolving organizational behavior issues that inhibit innovation, and assuring ease-of-use in a broad range of management environments. The dissertation concludes with an IUM Model simulation that demonstrates the innovation management efficiency of the IUM Model compared to alternative organizational structures.


Six State Logic Re-Engineers Language, Programming, IoT and Politics


Although a simple concept, 6SL has potential to fundamentally re-engineer how humans think about logic in general with a resulting broad impact on diverse topics like application programming for language, IoT, Super Intelligences, teamwork and politics.

For language, 6SL provides a common syntax for basic communications across natural language and machine to human language barriers. The following example assumes availability of simple lookup table for phrase matching to support 6SL language applications. Phrase matching is sufficient because 6SL simplifies communication syntax to a positive or negative question and 6SL answer with reason why format.

Question simplification removes difficult-to-translate natural language nuances in questions while 6SL answer provides sufficient information for communication to be actionable. Actions can be conclusive in a single step, like a vending machine giving a prospective customer a Known Negative response to the question “Do you have Diet Coke?”, or actions can confirm the need for one or more additional cycles of questions and answers.

Consider the example of a human answering “Seems Positive” to a machine sales agent asking if they want to take a test ride in a new self-driving car.  In this example, the combination of positive and uncertain elements in the Seems Positive answer would stimulate the agent to clarify the reasons behind the human’s answer. Reasons could be “Positive: Looks Interesting” and “Uncertain: Concerned about safety.”

If we assume the agent’s mission is to offer free sample rides to humans, then the agent would want to support the positive element of the human’s answer and provide information, like data showing self-driving cars are safer than human driven cars and a copy of the generous insurance policy, to help the human overcome their uncertainty. After showing the targeted information, the machine sales agent asks the human again if they want to take a test ride to see if the information had the desired impact of moving the human from a Seems Positive 6SL state to an immediately actionable Known Positive 6SL state.

Codifying questions and actions associated with 6SL responses from the other party represents a type of simple application programming language humans use to program any machine agent to interact with other machine agents or humans for any 6SL application. In the case of a Super Intelligence, the type of programming is exactly the same as for a simple, single function IoT component like a vending machine.

The difference between an IoT component and a Super Intelligence is the scope of programming topics.  Super Intelligences are programmed, updated, and supported by teams of people to address a wide range of complex applications like the onboarding of professional clients for doctors or lawyers while the programming of many IoT components will be comparatively limited in scope such as a vending machine.

6SL supported teamwork is an application with far reaching implications because so much of human effort is spent preparing for and attending meetings. First, much of 6SL decision support for meetings is easier to do remotely than in person. This is because meetings involved many distractions like the dynamics of office politics and because meetings must be conducted on a restrictive timetable. One-to-many dissemination of new information and education will continue to be meeting supported activities, but 6SL blends into this traditional meeting environment superior and far less expensive remote idea evaluation and decision validation methodology. 6SL changes how human teamwork is done because the quickest way to a decision will involve participants interacting with their individual screens rather than the informal, hierarchical, or other type of live meeting interactions we are all familiar with.

Many political problems are associated with a lack of any objective perspective of the truth which makes everything except what someone wants to believe false.  Also, many problems are associated with people with mixed or uncertain feelings being forced to make all or nothing decisions. 6SL has a role to play in correcting both of these problems. First, 6SL helps collect more accurate descriptions of voter sentiment and uncertainty.

This highlights the absurdity of pushing through policies with minority support while a majority of constituents remain uncertain. 6SL also enables creation of fully transparent policy-specific Super Intelligences that are far more inclusive and resistant to motivated reasoning or outright dismissal than partisan human institutions. Just these two 6SL innovations can transform the political landscape by providing humans with the best possible version of the truth and enfranchising uncertainty as a legitimate and non-partisan state of being.

Six State Logic Enables Universal Translator


Six State Logic (6SL) makes universal translation between humans and machines possible and practical. 6SL is a unique communication and logic system that answers positive and negative questions with a Known Positive, Known Negative, Uncertain or intermediate Seems Positive, Seems Negative, or Mixed state selections along with a free form text reason why.  Please see my YouTube Channel for more information about Six State Logic applications ranging from simple surveys to sophisticated networks of human-preference driven, machine agent interactions.

For the universal translation application, 6SL’s exclusive use of positive or negative format questions simplifies the translation of questions between languages to a simple phrase match search. For example the phrase “Are you hungry?” is easily translated into any human language.  6SL use of one of the six color coded states as an answer to any positive or negative format question is by definition language independent and typically results in an immediately actionable response.

The benefit of using 6SL answers to positive and negative questions comes from the qualification of uncertainty inherent in the six state choice instead of trying to have a bilingual natural language dialog about uncertainty.  This 6SL benefit includes knowing that Known Positive, Known Negative, and Known Mixed states are certain as much as knowing the Uncertain, Seems Positive, and Seems negative states are uncertain.  In other words, 6SL helps people that speak difference languages avoid the difficult translation task of questioning into another person’s “Yes” answer to see if it really means “Yes and ready to act now” (Known Positive), “Probably Yes depending on something else” (Seems Positive), or “Yes but I am just as likely to say No” (Mixed).

Consider the example of an English only speaker asking a Chinese person if they are hungry. A common smart phone app can recognized the question phrase and say the equivalent question in Chinese. If the answer is “Seems Positive” which means they are considering eating, but are also open to additional information to make sure, then offering the Chinese person a menu and asking them if there is anything they want to order now would be a reasonable action.  If they choose an item with a Known Positive answer then the 6SL universal translation session between these English and Chinese speakers is complete.

In cases where more information about the reason for a 6SL response is required, a phrase matching translator can once again look up standard phrases that are specific to the justification for each 6SL response.  For example, justification phrases for a Known Negative answer to the question “Are you hungry?” could be “I just ate”, “I have special dietary requirements”, etc..  The fact that 6SL choice justifications fall into one of six categories makes phrase match translation of answer details more meaningful and easy to translate.

In addition to enabling human translation between natural languages, 6SL universal translation also enables humans and machines to interact similarly. Consider the above with a vending machine asking “Are you hungry?”  The 6SL interaction between parties follows that exact same 6SL question and response method when pursued by machines or people.  In this sense, 6SL truly enables universal translation by helping humans communicate with other humans or machines and lastly by helping machines communication with other machines quickly and in a way that humans understand.