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This series has two objectives of significance.

One is to explain why IQ tests only test a limited aspect of human intelligence and that IQ estimates are not constant; they can be improved. People's contribution to the economy and human wellbeing through service and innovation requires properties that cannot be measured by IQ tests.

Secondly, as constituents in a so-called democracy it is important to understand how decisions are taken by governments, especially in the field of policy. Serious mistakes have been committed in decisions taken by UK governments related to specific errors in decision analysis which, of course, have not been admitted. A lack of ability to question, on the part of constiutuents, not only allows poor decisions to slip through, it also permits hidden agendas favouring specific people and groups, favoured by political parties, to proceed. This lack of transparency and scrutiny needs to be terminated. There needs to be a change in how the collective intelligence of the population is brought to bear on the process of decision-making in governance.

This has implications for our constitution, political parties and the shaping of policies with respect to, for example, access to education and health services. A more appropriate level of involvement of constituents can contribute to shaping policies to secure a balance in the distribution of wellbeing across all constituencies and constituents of this country.

To achieve an economically and environmentally sustainable future, this is an imperative

George Boole unraveled the process and developed the mathematical logic of human deduction or inference. Claude Shannon, having studied Boolean logic as an undergraduate, and having also understood it, realized this could be used to optimize the design of switching circuits. The process of "Boolean Reduction" is used, to this day, to optimize the design and improve the functionality of highly complex integrated circuits. This process reduces basic designs created by engineers which, in their raw state, can require many hundreds of inputs (pins) and outputs (pins) to achieve a processing objective. Boole's mathematical logic reduces the number of inputs and outputs to a significantly reduced number by removing redundancy to a bare minimum of logical statements. Even although sometimes the reduction in complexity is drastic the resulting simpler design still achieves everything the original design set out to achieve.



Although the growth in cybernetics and operations research developed with the early computers much of the underlying logic is contained in Boole's work. We have mentioned the concept of adaptive or applied intelligence which generally has a specific goal in mind. Thus, we are dealing with teleology or finality; decisions with an objective. Thus the objective provides the justification for decisions and associated actions. The mission is to apply methods of analysis and procedures that bring about a state of telesis. Telesis is derived from an ancient Greek word signifying

Telesis: - ".. the steady progress towards an objective through the intelligent use of resources".

Notice that the context of intelligence has broadened its scope to be applied to the rationality of the objective and to the practical application of resources.


Ronald Howard



The term, "decision analysis" was coined by Ronald Howard, today Emeritus Professor at Stanford University. He headed the Stanford Research Institute Decision Analysis Group in the 1960s. This team moulded decision analysis into an applied discipline making use of computer-based decision analysis models. These were cause and effect models that permitted the analysis of variations in the determinants of decision outcomes in order to identify the best options for decisions.



Part of this process is a preliminary learning phase used to refine models on an iterative basis so as to improve knowledge on the probability of events and relationships through the collection and analysis of additional information. This process is known as the decision analysis cycle and a model would not be used to guide decisions until the model can replicate known circumstances as proof of its ability to emulate reality. If a model cannot replicate past known events then the determinant model or the probabilities or the quality of the information being used is inadequate. Therefore the decision analysis cycle is a diagnostic procedure that identifies shortcomings in knowledge and data and is a useful safeguard in preventing decisions being taken before the full implications of decisions can be determined.


Decision analysis enables the rational analysis of a decision, no matter how complex, to be analyzed in depth and at a very low cost before committing resources to the implementation of a specified decision.


The process of model development exposes the information that is lacking as well as the gaps in knowledge concerning cause and effect relationships. A model is refined until it can reproduce known conditions and therefore is a good representation of "reality". Only on this basis is a model then applied to analyse decision options.



Claude Shannon identified an important application for Boolean logic which, it should be mentioned, also gave rise to Boolean logic being the basis for the logical expression used in programming languages. Decision analysis has provided a set of procedures to make Boolean logical deduction to embody, more directly, by automating George Boole's procedures on human logical inference and deduction. Beyond the unaided human capability, decision analysis has made possible the consideration of highly complex circumstances where gaps in human needs need to be resolved. Decision analysis provides an objective means of supporting decisions through specified feasible actions to improve wellbeing.

Within the discipline of decision analysis, a decision is defined as:

A decision is:-".. an irrevocable allocation of resources to a specific course of action".

Therefore a declaration of intent, or a decision carries no meaning without it being associated with a carefully specified plan of action linked to the stated objective. The fundamental point here is that the feasibility of implementation needs to have been established before decision is taken. Any changes in decision will result in additional resources having to be used. Decisions are important.



Even with a plan of action that has been meticulously analyzed as being feasible a decision cannot be considered to be a decision unless the decision-maker or decision-makers have the authority to commit the necessary resources to the action within the time frame and on a basis that can sustain the implementation up until the point of achieving the desired results



It may be recalled that we have arrived at this point by starting with an issue of relating economic policies to human intelligence or rather designing policies to promote adaptive intelligence so that all can benefit from beneficial change. It might surprise many that this was the objective of people such as Aristotle and several Greek "philosophers" as well as the origins of cybernetics which created the theory related to feedback loops to inform the decision-making function or governor of progress in achieving or maintaining the desired state by altering "inputs". In this context, the term cybernetics, which today is considered to be about self-regulating systems, comes from ancient Greek terms that transmit the combined meaning of "government" and "steersmanship, navigation or "governorship". The key to this process is by keeping progress "on track", telesis is achieved.

In 1968 Matheson & Howard noted the evolution of decision analysis which, in their words, seeks to apply logical, mathematical, and scientific procedures to the decision problems of top management that are characterized by the following:
  • Uniqueness - each is one of a kind, perhaps similar to - but never identical with - previous situations
  • Importance - a significant portion of the organization's resources is in question
  • Uncertainty - many of the key factors that must be taken into account are imperfectly known
  • Long run implications - the enterprise will be forced to live with the results of the situation for many years, perhaps even beyond the lifetimes of the individuals involved
  • Complex preferences - the task of incorporating the decision-maker's preferences about time and risk assumes great importance
Thus although deployed in the resolution of problems within large companies it is also ideal for resolving decision issues surrounding the allocation of resources in local, regional and the macroeconomies.



Governance, the process of governing needs to identify gaps and needs in provisions and to understand the constraints that have created the gaps. Gaps should be measured as products or states and not as processes. Thus what is it that members of the constituency lack? Is it income, adequate diet, health status or perhaps adequate housing. These gaps are problems to be solved. Because the current processes that supply income, food, health services and housing are associated with gaps in provisions, it should not be assumed that more of these processes is what is required. It is best to work backwards from the gap and build a decision analysis model that can identify existing as well as optional processes to fill the gaps. The objective, besides closing gaps, is to achieve this effectively and efficiently so as to have the desirable impact in a timely fashion. This invariably involves change, sometime slow sometimes rapid, making up the content of innovation.

As a result we shift from the analysis of gaps and constraints to having to manage this information to bring about changes involving people and organizations. There is a need to be able to assign tasks to groups of people who can perform in conformity with a specific requirement specified in terms of quantified qualities and quantities of output over specific time scales. Covide-19 is an example of this sort of challenge on steroids.

So the solution to addressing some objective passes from the decision analysts logic to an assessment of the reliability of the adaptive intelligence of groups not connected directly to the decision-maker who will deliver the decision. This where things normally go wrong and what might be a correct direction and a correct objective the decision is in reality a failure in adaptive intelligence because in reality it cannot be delivered in time, if at all. This because the requirements do not map over the available capabilities. The normal lame explanation in the political domain, and especially in the domain of macroeconomic policies, is that "the decision was correct, but it was not implemented in the right way" or in the scapegoat clause, "the government was so badly advised" associated with some special advisor from some department finding themselves without a job and who often had nothing to do with the decision.

This article in this series has not been particularly enthralling, it has pulled some strands together to consolidate some aspects of issues surrounding policy decisions. However, in the next article in the series we will return to the human aspects of adaptive intelligence and how they relate to our survival and wellbeing. How all of this relates to the space of how we, as constituents, achieve a more transparent view of how decisions are taken "on our behalf" and how we as citizens can participate more effectively in these decisions will be described. This will be related to the main strands and capabilities of individuals in how they handle very different aspects of decision making and implementation of necessary actions. These involve an amazing array of knowledge and activities requiring different aspects of intelligent or rational behaviour and little of which relates to IQ tests taken by children at an ages when the natural disparities in the measurable aspects of their developing minds is too broad as to be, at best, unreliable.