**12. Semantic mapping**

256 Semantics – Advances in Theories and Mathematical Models

Such an approach is the acceptance of adaptation to real world negative feedback loops with

Control systems part of output may be 'fed back' to the system, resulting in an action-

Identification of critical paths and enablers to the existence of 'Command—causalities consequences' (C3N) (Dictionary, 2004) e.g. 'Willingness–to–pay' or 'benefit' for a given

A new paradigm of cognitive adaptation is not to say that there is no nexus between the 'Command' construct in both the C3N and C3T mode. Causalities—consequences might act as a continuum of drivers and as such making it no longer acceptable to fund projects without a business case, being within a strategic management planning framework or having

The developer has a self interest by way of ensuring a bonus is paid at the delivery stage within the projects set financials. The outcome therefore sought is to drive corporate governance; transparency with Total Quality Management (TQM), and Continuous Quality

This is not a new concept but still provides guidance to owner—funder—managers. "*It will be knowledge that will provide sustainable competitive advantage, and knowledge is the capital of people*" ( Goldsworthy, 2002) or are likely to have radicals as drivers of uncertainty and

Simple Semantic and Semiotic mapping may be a way forward? A recent view point is provided by Dr. James Canton Institute of Global Futures CEO and advisor on Fortune 1000: "Despite the bleak economy and uncertain future, technology is key to our future" and 'Tech workers and IT leaders are in a unique position to create opportunities for

People knowledge—information—learning management are the corner stones of any plausible C3 semantics modeling—dynamics to assist owner—funder—managers and

Various management and leaning styles exist that may complicate the implementation and use of ridge processes and tool sets. It is therefore appropriate to utilize a continuum approach that allows a practitioner to start and finish at any point and achieve workable

**10. Paradigm shift** 

scenario—context.

defined outcomes.

**11. People and knowledge** 

'Willingness–to–pay' or 'benefit' conflicts.

themselves" (Daniel, 2009).

outcomes such as cognitive adaptation and capabilities.

developers as partners.

the interplay of causalities and consequences:

Opposing the condition that triggers it (continuum)

Output of a pathway inhibits inputs to another or the same pathway

reaction in the opposite direction (Golec, 2004; Bowen, 2001).

Improvement (CQI) (LeBrasseur, et al., 2002; Nüchter, et al., 2008).

 Notion of mechanism of feedback Return to the input of part of the output' Negative resultant actions e.g. 'Willingness' Semantic mapping (Fig 5) has variability within a 'fit–for–purpose' ethos (e.g. F–semantics; I–semantics). 'Fit–for–purpose' results in the need for cross platform control systems and memory with information–knowledge cipher–prima strings that may support families of delivery engines. Semantic mapping aids in verification, interoperability and collaborative distribution and facilitates moving from the macro, meso, micro, and quantum–nano scales within key themes (Table 2) for a given scenario—context.


Table 2. Semantic Themes (Coppock, 2005; Kiss, 2005; Ikehara, et al., 2007; Armarsdottir, et al., 2006)

In summary, Semantic mapping comprises:


Fig. 5. Semantics modeling—dynamics mapping (He et al., 2005; Hatten et al., 1997; Wagner et al., 2005; Avery et al., 2004).

C3

risk)

[outcome to be met])

**15. Graphic notations** 

linguistics (Sowa, 2006).

1999; Tancredi, 2007; Eiter et al., 1999).

over (Dictionary, 2008) system theme have a nexus:

studying how that affects the dependent variables

results (separation between controller and the system)

the same influence upon the different sample groups.

groups should minimise the unknown variables.

experiment is as accurate as is possible (Kiss, 2005)

information—learning (Ask, 2010).

**16. Command and control** 

 Suitably (informally [likely] or formally [will]) Processing (human or machine or both)

delivery engines for a given scenario—context.

Causality (facts; assumptions; criticality)

W semantic Temporal Entanglement Modelling for Human - Machine Interfaces 259

Importance (specific [must]; explicit [description] or implicit [consensus]; essential

Freedom of action (operating space; Limitation; restrictions; boundaries; opportunities;

Consequence (deductions; shape; course of actions; areas of interest) coalescences of

A graphic notation tends to be cognitive by nature and as such is a feature of a semantic network or network*.* As an enabler, what is suggested is representing information– knowledge domains with patterns and symbols. By doing so the various management and learning styles are able to be accommodated. Additionally, what are highlighted are interconnected nodes and threads with alignments with philosophy, psychology, and

So what should it be F–semantics, I–semantics or a hybrid? F–semantics that is, the semantics is deterministic: no stable models or well-founded model is empty, but is meaningful. I–semantics namely lexical semantics: no principled reason to restrict being an ad hoc stipulation or satisfying declaratives for agent programs or a hybrid? (Phan Luong,

Command (to direct with specific authority) and Control (to exercise restraint or direction

Experimental design: manipulating one variable, the independent variable, and

Factors-characteristics: must be known control potential adverse influences on the

Causality: cause-effect upon the results must be standardized, or eliminated, exerting

Analysis: statistical tests have a certain error margin as such repetition and large sample

Consistency-systematic: via monitoring and checks, due diligence will ensure that the

Consequence: can refer to a good or a bad result of your actions—knowledge—

 Reasoning: A cognitive process of looking for reasons for beliefs, conclusions, actions or feelings (Deductive reasoning [support that conclusion]; Inductive reasoning

 Context: must be the same for all entities (human-machine) and tests Controlled: variables are important than if dependent or independent Isolate: the controlled variables as may lead to ruining the experiment

Control group: to give a baseline measurement for unknown variables
