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Chapter 12


Knowledge Technologies and the Asymmetric Threat




Section 1: Demand-side Knowledge Process Management


New methodology allows the measurement of world wide social discourse on a daily basis.   General systems properties and social issues involved in the adoption of this methodology are outlined in this paper.  Technical issues related to logic, mathematics and natural science are touched on briefly.  A full treatment requires an extensive background in mathematics, logic, computer theory and human factors. 


Community building and community transformation have always involved complex processes that are instantiated from the interactions of humans in the form of social discourse.  Knowledge management models of these processes involve components that are structured around lessons learned and lessons encoded into long-term educational processes.  As a precursor to our present circumstance, for example, the Business Process Reengineering (BPR) methodologies provide for AS-IS models and TO-BE frameworks.  Over the past several decades, additional various knowledge management disciplines have been developed and taught within the many knowledge management certification programs.  However, human knowledge management at the level of individual empowerment has not generally been part of the BPR methodology or knowledge management certification programs. 


New methodology allows the measurement of world wide social discourse on a daily basis.   General systems properties and social issues involved in the adoption of this methodology are outlined in this paper. 



Figure 1: Experimental system producing polling like output


The complex interior of the individual human is largely unaccounted for in measuring the thematic structure of social discourse.  But the individual is where demand for social reality has its primary origin.  So something has been missing.  What is missing is something that is missing from information technology.  What is missing in information technology systems is the available science on social expression and individual experience.  Why individual variation in response patterns, for example, is not being accommodated by information technology is due to many factors.  Some of these are technical issues related to limitations in design agility, and some of these are related to the nature of formal systems themselves. 


So in light of these limitations, we have to be reminded that human tacit knowledge expression occurs in human dialog, even if the fidelity of this expression is some times low and sometimes high.  Natural language is NOT a formal system.  Yes, abstraction is used in spoken language; but a reliance on pointing and non-verbal expression helps to bring the interpretation of meaning into a pragmatic axis that exists within the discussion, as it occurs and is experienced.  Written language then extends a capability to point at the non-abstract, using language signs, to what is NOT said but is experienced.  Human social interaction has evolved to support the level of understanding that is needed for living humans, within culture, to form social constructs.  But computer based information systems have so far failed to represent human tacit knowledge, even though computer networks now support billons of individual human communicative acts, per day, via e-mail and collaborative environments. 


There is a mismatch between human social interaction and computers.  How is the mismatch to be understood?  We suggest that the problem be understood in the light of a specific form of complexity theory. 


Computer science is a subset of mathematics, and mathematics is expressed in formal systems.  The near future holds an evolution of mathematics that is similar to the weakening of category theory by the use of rough sets, and the weakening of logic by quasi-axiomatic logic.  These evolutions move in the direction of a stratification of formal systems into complexity theory.  A number of open questions face this evolution, including the re-resolution of notions of non-finite, the notion of an axiom, and the development of the understanding of human induction (seen as a means to "step away from" the formal system and observe the real world directly). 


It is via this weakening of constructs lying within the foundations of logic and mathematics that an extension of the field of mathematics opens the way to a new computer science.  But as this happens, we must always guard against allowing computer science to make claims about such things as "formal semantics" and "machine awareness".  Why?  Because computer science is based on categorical abstractions - which when confused to be the same as objects that exists in physical reality leads to error.  Computer science has a proper place, and should remain in its place and not compete as if it were a natural science.


Side note on the AI failure


The “artificial intelligence” failure can be viewed, and often is, as simply because humans have not yet understood how to develop the right types of computer programs.  This viewpoint is an important viewpoint that has lead to interesting work on computer representation of human and social knowledge.  However, we take the viewpoint that a representation of knowledge is an abstraction and does not have the physical nature required as an “experience” of knowledge.  The fact that humans experience knowledge so easily may lead us to expect that knowledge can be experienced by an abstraction.  And we may even forget that the computer program, running on hardware, is doing what it is doing based on a machine reproduction of abstract states.  These machine states are Markovian, a mere mathematical formalism.  By this, we mean that the states have no dependency on the past or the future; except as specified in the abstractions that the state is an instantiation of.


There is no dependency on the laws of physics either, except as encoded into other abstractions.   This fact separates computer science and natural science. 


The tri-level architecture, developed by Prueitt in the mid 1990s, attempts to model the relationship between memory of the past, and awareness of the present, and the anticipation of the future.  However, once this machine architecture is in place, we still will be working with abstraction and not a physical realization of (human) memory or anticipation.  Optical computing or quantum computing may change this, but these “stratified” computational systems are not well understood as yet. 


Stratification seems to matter, and may help on issues of consistency and completeness, the Godel issues in formal foundations to logic.  Already connectionism models of biological intelligence have realized some aspects of the tri-level architecture.  But the clean separation of memory functions and anticipatory functions allows one to bring the experimental neuroscience and the cognitive science into play.  For example, memory and anticipatory systems are being modeled as separately caused by interactions with a world, external to the computer program. Awareness binds parts of these systems together in a present moment, having a physical and pragmatic axis.  


This external, to the computer, world is not an abstraction.  The measurement of the physical world results in abstraction; in the tri-level architecture the measurement of invariance is used to produce a finite class of categorical Abstractions (cA). These cA atoms have relationships that are expressed together in patterns and these patterns are then expressed in correspondence to some aspects of the measured events.  The cA atoms are the building blocks of events, or at least the abstract class that can be developed by looking at many instances of events of various types. 


Anticipation is then regarded as expressed in event chemistries and these chemistries are encoded in a quite different type of abstraction similar in nature to natural language grammar. 


We argue that the development of categorical abstraction and the viewing of abstract models of social events, called “event chemistry”, are essential to the protection of national security.  Distributed community processes are supporting asymmetric threats to American national security and to the security of other nation states. 


There is a national obligation to develop response mechanisms to these threats.   The response must start with proper and clear intelligence about the event structures that are being expressed in the social world.  Human sharing of tacit knowledge must lie at the foundation of these response mechanisms.  Maturity and principle must guide our use of this foundation. 


Understanding the difference between computer-mediated knowledge exchanges and human discourse in the “natural” setting is critically important to finding a measure of security within a social world that has been changed by globalization.  One of our challenges is due to advances in warfare capabilities, including the existence of weapons of mass destruction.  Another obvious challenge is due to the existence of the Internet and other forms of communication systems.  Economic globalization and the distribution of goods and services presents yet another set of challenges that delineate the present and the future from the past.  If the world social system is to be healthy, it is necessary that these security issues be managed.


Event models, to be derived from the data mining of global social discourse, will define a science that has deep roots in legal theory, category theory, logic, and the natural sciences. 


The current natural security requirements demand that this science be synthesized quickly from the available scholarship.  Within this new science, stratified logics will compute event abstractions, at one scale of observation and event atom abstractions at a second scale of observation. The atom abstractions are themselves derived from polling and data mining processes in order to create the abstractions. 


Note on privacy issues


Developing a stratification of information into two layers of analysis.


The first layer is the set of individual polling results or the individual text placed into social discourse.  In real time, and as trended over time, categorical abstraction is developed based on the repeated patterns within word structure.  Polling methodology and machine learning algorithms are used. 


The second layer is a derived aggregation of patterns that are analyzed to infer the behavior of social collectives and to represent the thematic structure of opinions.  Drilling down into the specific information about, or from, specific individuals will require government analysts to make a conscious step and thus the very act of drilling down from the abstract layer to the specific informational layer is an enforceable legal barrier that stands in protection of Constitutional Rights.


Given the recognition of human privacy rights, what will bring human tacit experience more easily into distributed community processes?  This issue of tacit knowledge is a core challenge to knowledge management methodology and technology.  But for this paper, the context is the natural security.


What science/technology is needed to “see” the private knowledge of events that lead to or support terrorism?  Perhaps, we need something that stands in for natural language?  Linguistic theory tells us that language use is not reducible to the algorithms expressed in computer science.  But if “computers” are to be a mediator of social discourse, must not the type of knowledge representation be more structured than human language?  What can we do?


The answer to the question of degree of structure is the most critical inquiry that technologists and scientists must develop a consensus about.  According to our viewpoint, the computer does not have tacit knowledge to disambiguate natural language, in spite of several decades of effort to create knowledge technologies that have “common sense”.  Based on principled argument grounded in quantum neurodynamics, a community of natural scientists has argued that the computer will not have tacit knowledge because tacit knowledge is something experienced by humans.  Our claim is that there is no known way to fully and completely encode tacit knowledge into rigidly structured standard relational databases. A large body of experimental work in the natural sciences suggests that computers alone, no matter the funding level, cannot do this.


A work around for this quandary is suggested in terms of a Differential Ontology Framework (DOF) that has an open loop architecture showing critical dependency on human sensory and cognitive acuity.    


2: Differential Ontology Framework (technical section)


Computers are not now, and likely will never be cognitive in the same way as humans are.  However, the notion that cognitive-like processes might be developed has brought some interesting results.  At the core of the solutions that are been, or are likely to soon be, made available is a class of the machine-encoded dictionaries, taxonomies and what are often called ontologies.  While encoded dictionaries, taxonomies and ontologies are often referred to as knowledge representation, one has to be careful to point out that knowledge is “experienced” and that these computer data structures are experienced only when observed by a human.  Some experts in the field express the notion that computers do or will experience, and this is the so-called strong AI position.  


The rejection of strong AI opens up an understanding that knowledge definition, experience and propagation requires a greater degree of agility and understanding by users of the processes that have been developed by the strong AI community over the past 50 years.  There has been a failure to communicate.


The strong AI community, on the other hand, should eventually come to support the principled arguments made in the quantum neuroscience community.  Quantum neuroscience has an extensive literature that reports on issues related to human memory, awareness and anticipation.  This literature is referenced in a separate technology volume, simply because the purpose of this paper is to develop concepts not to provide what should be a complete citation of the literature (as opposed to a very partial citation that might be misleading.)  However, we point out that the quantum neuroscience literature addresses the question of how things happen and in doing so this literature challenges classical Newtonian physics as a model of natural complexity.


The cause of events has a demand and a supply component.  Demand-side knowledge process management can be used to create machine-encoded ontologies.  These formal constructs, that are the states of computer programs, are constructions that exist in two forms, implicit and explicit.  Implicit constructs are defined as proper continuum mathematics.  The constructs of continuum mathematics are represented on the computer in a discrete form.  The discrete form of implicit ontology has the benefit of the precision of the embedded formal continuum mathematics, seen in mathematical topology, mathematical analysis.  Demand is a holonomic, e.g., a distributed, constraint like gravity and so a distributed representation is needed.  The continuum mathematics provides the distributed representation that the computer “cannot” provide by itself. 


An explicit ontology, on the other hand, is in the form of a dictionary or perhaps a graph structure.  The constructs of explicit ontology are expressed as discrete mathematics, graph theory, number theory, and predicate logics.  The interface between discrete mathematics and continuum mathematics has never been easy, so one should not expect that the relationship between implicit and explicit ontology be easy either.  However, a formal theory of by-pass has been developed, by Prueitt, which shows a relationship between number base conversions, data schema conversions, and quasi-axiomatic theory [1]. 


First-order predicate logics are often developed over the set of tokens in some of the explicit ontology that exists, for example by using a standard resource description framework (RDF).  Value is derived, and yet these ontologies with their logics suffer from the limitations of explicit enumeration and relational logics.  This limitation is expressed in the Gödel theorems on completeness and consistency in formal logics [2] as well as in other literatures.  By-pass theory is designed to manage this problem.


The bottom line is that high quality knowledge experience and propagation within communities needs the human to make certain types of judgments.  But the methodology for human interaction with these structures is largely missing, and is certainly not known by most of those who need now to use these structures for national intelligence vetting. 


We introduced the term “Differential Ontology” to talk about a human mediated process of acquiring implicit ontology from the analysis of data. 



Figure 1: Differential Ontology Framework


By the expression “Differential Ontology” we choose to mean the interchange of structural information between Implicit (machine-based) Ontology and Explicit (machine-based) Ontology.


         By Implicit Ontology we mean an attractor neural network system or one of the variations of latent semantic indexing.  These are continuum mathematics and have an infinite storage capability.

         By Explicit Ontology we mean an bag of ordered triples {  < a , r, b > }, where a and b are locations and r is a relational type, organized into a graph structure, and perhaps accompanied by first order predicate logic.


The differential ontology framework uses implicit ontologies now found in stochastic and latent semantic indexing “spaces” and derives a more structured form of dictionary type ontology.  The class of process transformations between implicit and explicit forms of machine ontology is to be found in various places.  For example, we have seen market anticipation in the large number of automated taxonomy products that are appearing in the marketplace.  However, we claim that the notion of deriving explicit ontology from implicit ontology is an original contribution. 


Within the explicit ontology there are localized topics.  These localized topic representations “sit’ by themselves. In the implicit ontology the information is distributed like gravitational wells are in physical space.  Move anything and everything changes; sometimes only minutely and sometimes catastrophically.  The perturbations of representation are then formally seen as an example of a deterministically chaotic system.  Natural systems may or may not be deterministic, and thus the argument is that formal chaos is not the same as the process seen in the emergence of natural systems in the setting of real physical systems.  Neural network type attractor manifolds is an early form of the implicit ontology.  But now we have also genetic algorithms, the generalization of genetic algorithms in the form of evolutionary programming and other mathematical constructs.  The tuning of these systems to the physical reality is thus of major consideration.   This tuning is not a done-once for-all-time task.  As the world changes, the tuning of implicit ontology must change also.  How is this to be done?


Moving between these implicit and explicit forms of machine ontology "state gesture mechanisms" can be attached to topic constructs, whether distributed or localized, using new type of “stratified” architecture [4-6] that is grounded in cognitive neuroscience, specifically in the experimental work on memory and selective attention [7,8].  The stratified architecture has multiple levels of organization, expressed as an implicit or explicit, continuum or finite, state machine, in which within each level certain rules and processes are defined. 


Cross level interaction often MUST involve non-algorithmic [9] movement within the state spaces.  Thus a human, who CAN perform non-algorithmic inference, is necessary if the over all differential ontology system is to stay in tune with the external complex world.  This means that implicit and explicit formalism is NOT sufficient without real time human involvement.  A state gesture mechanism drives the information around that part of the system that lives in the computer, but the demand for this supply comes from within private personal introspection, perception and decision-making by humans.  Again, our viewpoint seeks to return responsibility, for control actions in the world, to the human and push away the notion that the crisp and precise states within computer information systems can reasonably be managed outside of this responsibility. 


State gesture mechanisms allow the machine ontologies to be “assistant-to” human decision making.  The mechanism is not a closed formalism or an information technology, but rather an intellectual framework with stratified theory and cognitive neuroscience as the framework’s grounding [10]. 


Following architecture, and design of a related applied semiotics theory developed by Pospelov and his colleagues [11], the state gesture mechanism itself is deemed largely subjective.  This means that the mechanism is not reducible to algorithms.  A ‘second order’ cybernetic system is required that is primarily controlled by direct human intervention.  So the machine ontologies are required to be sub-servant to a human knowledge experience process.  The details of the machine ontologies are visible as reminders or signed informants within a sign system or natural language.  It is for this reason that one can call this a knowledge operating system.


Elements of micro-ontologies, that are formative within the moment, act as signs about the knowledge process that are regularly occurring in social networks.  The differential ontology framework requires a human interaction with these signs.  That is the Demand side.  Ontologies are streamed using formatted micro-ontologies from point to point in the knowledge ecosystem resident to the community of practice.  This is the Supply side. 


A computational mirror of the state gesture mechanism is also “assistant-to” the location where information is moved within the enterprise.  So the mechanism acts as an automated reporting and assessment technology.  This assistance has great value to social and economic processes.  Productivity goes up.  Social value goes up.  The fidelity of knowledge representation goes up.  Return on investment goes up. 


Responsibility can, and should, be assigned exactly where responsibility is situated.  This is true, in particular, where Constitutional rights to privacy are involved.  A knowledge operating system, such as the Knowledge Sharing Foundation, can provide complete transparence to all instances where a human examines private knowledge. 


As an example, an evocative question can be answered in various ways.  The answers made are part of a natural language generation capability that involves the emergence of word structure from a mental experience.  This emergence must involve a human awareness, and the action of requesting the computer supporting processes can be a trigger to reporting mechanisms about the inspection of private information.  This the barrier to inspection is transparent. 


The structure of differential ontology mediated communication allows a mechanical roll-up of the information into one of several technologies for natural language generation from semi-structured information.  However, the process is formally underdetermined, and some constraints must be imposed during the construction of the natural language response.   One sees the same situation as the mathematics of the wave functions in quantum mechanics.  The formal mechanism for “collapsing the wave” and realizing locality cannot be derived from the classical notions, and perhaps cannot be derived at all in a single formal system (having both completeness and consistency).  Many post-modern scholars of physics have equated this problem with the notion of finding a single unified theory of everything [14]. 


Differential micro-ontologies represent the first of a new generation of more complete (supply side plus demand side) knowledge process management paradigms.  Machine based ontologies generate natural language that communicates to users in a way that is familiar.  This drives knowledge sharing in a new way. 


What is surprising about this vision is that it is NOT the popular Artificial Intelligence vision of the future.  The “real” future brings the human more fully into contact with other humans within what is essentially a Many to Many (M2M) communication device. 

3: Many to many communication


The television industry is a one to many communications device.  One community, having some diversity within the group, uses the television to express that group’s views of the world.  There is a well-defined community boundary, having a complex interior and community membership.  The community is diverse but nevertheless, the group represents one type of person.  There is something in common within the group.  The commonality is not something that is always recognized. It is organically evolved as a general system property rather then by the explicit intent of those who choose to be a member of this community.  Communities have “autopoietic” structural coupling, in the sense defined by Maturana and Varela [13], reinforced by the economic system and by the limitations of the television itself.  Specific structural coupling has formed, again organically, due to the presentation aspect of television where one group develops media and then markets this product in a marketplace. 


The television is not the only one to many communication device.  Books and radio industries also have specific structural coupling that reinforces a presentation of viewpoint within a marketplace.  One might think that writing a book is something that anyone can do, and if the book is a good book then other people will read the book.  However, this common perception does not account for the barriers to entry that the book industry has organically evolved.  Other one to many communication is under attack from many quarters and for many reasons.  Command and control institutions (such as the Cold War type military organizations, and media outlet industries) have a natural resistance to these attacks.   However, these institutions must meet the present challenge by renewal and by adoption of general systems thinking and behavior. 


American has a strong multi-cultural identity, as well as a treasured political renewal mechanism (e.g., the Presidential elections every four years).  Thus the reinforcement of multi-cultural social theory seems the likely consequence of the current challenges from fundamentalism as expressed in reductionist science, and in religious and economic fundamentalism.  Fundamentalism does not have a renewal mechanism, as the history of religions shows.  Specific ignorance and specific mythology is held onto in spite of contrary evidence.  Deeper values related to spiritual beliefs are more complex and are, likely, renewed within the private sphere of individual self-image.  But social structures, churches and religious institutions, tend to not renew. 


Asymmetric threat comes from non-government organizations.  Challenges emerge from these social systems because this social system serves some purpose that the nation states do not serve.  However, in the countries of the Third World, the basic needs of individuals are often perverted by hunger, economic injury and cultural insult [15].  Understanding the diverse opinions of non-governmental social systems is thus the single most important response that the United States Administration can make to the challenge of reducing the causes of terrorism.


It is natural that the social origins of asymmetric threats will use new forms of many to many communication in order to attack the vulnerabilities of a system where a large number of social organizations has organically developed around the economic value of one to many communications systems.  Developing agility and fidelity to defense information systems is the strongest defense to these asymmetric threats.  This defense strategy applies to asymmetric information warfare, as well as to the infrastructures that support mainstream command and control systems. 


In the 20th century, many subsystems of the economic order have developed economic structural coupling to organically developed one to many technology.  The shift to many to many communications tools is then essential, and yet inhibited.   This enigma must be sorted out.


The differential ontology framework enables processes, which have one to many structural coupling, to make a transition to a many to many technology.  This is where the enigma is most fully seen.  The asymmetric threat is using many to one activity, loosely organized by the hijacking of Islam for private hatred and grief.  It is a new form of the “people arise to over throw the unjust government”.  The defense to this threat is the development of many to many communication systems, and the related notion of categorical abstraction and event chemistry.


The many to many technologies allow relief from the stealth that many to one is given from the perception of a fully developed and mature economically reinforced system having one to many mechanisms.  The relief comes when machine mediation allows the formation of differential ontology as a means to represent, in the abstract, the discussion being made by organically self identified social structure.  This representation is done via the development and algorithmic interaction of human structured knowledge artifacts. 


The evolution of user structured knowledge artifacts in knowledge ecosystems must be validated by community perception of that structure.  In this way the interests of communities is established through a private to public vetting of perception.  Knowledge validation occurs as private tacit knowledge becomes public.  The relief from the asymmetric threat evolves because a computer-mediated formation of a defense community structure is facilitated and once this community structure exists in this form, communication traffic analysis provides selective attention to most of the threatening events from non-governmental entities.   For example, a new social community cannot form outside of the perceptional field of pre-existing communities that already have established structural coupling within well-defined economic entities. National surveillance systems have a way to see the threat.


The validation of artifacts leads to structured community knowledge production processes and these processes differentiate into the three levels of economic processes [16].  However, the validation process can be addressed unwisely.


4: The role of Communities of Practice 


Individual humans, small coherent social units, and business ecosystems are all properly regarded as complex systems embedded in other complex systems.   Understanding how events unfold in this environment is not easy.


Schema independent data representation is required to capture the salient information within implicit ontologies. The class of latent semantic index techniques is one class of examples of representation of information without a database schema; see the topic map standard [11].


The current standards often ignores certain difficult aspects of the complex environment and attempts to:


1)     Navigate between models and the perceived ideal system state, or

2)     Construct models with an anticipation of process engineering and change management bridging the difference between the model and reality.


The new knowledge science changes this dynamic by allowing individuals to add and subtract from a common knowledge base composed of topic / question hierarchies supported within the differential ontology framework.  The software enterprise is hidden from the user in two ways.  First, a community process validates the formation of the core knowledge base.  This is a social experience, not a technology.  The core knowledge base consists of reusable components which have the form of topic / question pairs within a hierarchical ontology.  Second, the knowledge base is used via a simple viewer/controller that works through web browsers. 


The technology becomes transparent and does so because information technology has matured and been refined and made a ubiquitous and stable commodity.


This presentation will close as we address a specific conceptual knot and untie it by separating issues related to natural language use.  Language and linguistics are relevant to our work for three reasons. 


First, the new knowledge technologies are an extension to natural spoken languages.  The technology reveals itself within a community as a new form of social communication. 


Second, we are achieving the establishment of knowledge ecosystems using peer-to-peer ontology streaming.  Natural language and the ontologies serve a similar purpose.  However the ontologies are specialized around virtual communities existing within an Internet culture.  Thus ontology streaming represents an extension of the phenomenon of naturally occurring language.


Third, the terminology used in various disciplines is often not adequate for interdisciplinary discussion.  Thus we reach into certain schools of science, into economic theory and into business practices to find bridges between these disciplines.  This work on interdisciplinary terminology is kept in the background, as there are many difficult challenges that remain not properly addressed. To assist in understanding this issue, general systems theory is useful.


These issues are in a context.  Within this context, we make a distinction between computer computation, language systems, and human knowledge events. The distinction opens the door to certain theories about the nature of human thought. Through a body of theory one can ground a formal notation defining data structures that store and allow the manipulation of topical taxonomies and related resources existing within the knowledge base.  Establishing the knowledge sciences will do this.


In Summary:  The differential ontology framework consists of knowledge units and auxiliary resources used in report generation and trending analysis.  The new knowledge science specifically recognizes that the human mind binds together topics of a knowledge unit.  The new knowledge science holds that the computer cannot do this binding for us.  The knowledge science reflects this reality.  The rules of how cognitive binding occurs are not captured into the data structure of the knowledge unit, as this is regarded as counter to the differential ontology framework.  The human remains central to all knowledge events, and the relationship that a human has with his or her environment is taken into account.  The individual human matters, always.




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