The model provides the conceptual tool set to observe, analyse, and understand the behaviour of humans at varying scales, from the individual to groups to society and culture. It does so by identifying the functional nature of dynamic patterns within a cognitive system from the ground up. The scale relates to the detail and number of participating elements - all of which can change - but the types of patterns exist regardless at what scale they manifest.
The range of possible applications are as wide as the entire field of human affairs. To illustrate what can be done under Otoom the following example refers to a medium to large scale focus on the general nature of demographics.
What are the major drivers of identity within a certain demographic?
Every demographic has an identity, however covertly expressed it may be. It represents the core of cognitive systems, and of all the possible dangers perceived by humans the most severe response is reserved for threats to one's identity.
It helps therefore to know how that core is made up, and from what sources the elements are drawn. These sources are the drivers. The label 'major' refers to those which are collectively shared by all.
A blunder along these lines committed by an outsider has dire consequences, for both sides. Identity and its protection has been at the basis of such scenarios as the crumbling monarchies in Europe, the various Balkan wars, the failure in Vietnam, the experience by several nations in Afghanistan, and Iraq.
What information, plan, or initiative from the outside would be potentially dangerous due to these
Since identity exists in any cognitive system and the respective sources are often dissimilar, what seems harmonious on one side is not necessarily perceived in the same manner by the other.
An analysis of the functional nature of the sources will explain their mutual relationship with each other and hence their potential for conflict.
How does the cultural lens of one demographic interpret the nature of another demographic?
A corollary to the above. Understanding the cultural source of a demographic leads to recognising the manner in which they interpret reality and to what extent (for example, a taboo has the effect of censoring information from one's knowledge base).
How one sees the other is not only important to a proposer or initiator of some policy, if several demographics are targeted the relationships among those becomes significant as well.
How complex is a demographic in terms of its information-processing capacity?
The perceived need under whatever circumstances is a function of the pressure a demographic is subjected to and its inherent capacity to process information. The pressure decides the level of significance attached to the need, and the capacity determines the quality and realism of perception. Analysing into greater detail shows how the two influence each other along the timeline.
Therefore the level of complexity sets an ultimate limit to the kind of initiatives that can be contemplated in a certain region. Should the overriding concern encompass many demographics (as is the case with climate change) such a disparity becomes a very real problem for all stake holders. Yet it still needs to be addressed one way or another.
If two or more demographics interact with each other, how do their respective capacities influence the
outcome of such interactions?
Any solution to mismatching levels of information-processing must entail an understanding of the systems involved. Lack of information can be identified and/or the cultural processes leading to a lack of information. Only then can the right questions be asked.
Naturally, no human activity system is a perfect knower, including one's own. And so the analysis of its overall performance and ability should be an ongoing enterprise in an age of globalisation, high density, polarising demographics, and environmental, political, and religious pressures.
At the lower level of cognitive dynamics in an individual, is there a mutual interdependency among phase
As the neuronal phase states give rise to the ideations we are able to identify at the cognitive level, the emergent phase states along the dynamics' time line are a function of the previous states' latency. That is to say, although the exact states are not predictable, they nevertheless fall within a probability spectrum as defined by those previous states (their latency). Since the whole is an ongoing process, there exists a backward compatibility between a current cluster of phase states and its precursor (evoked during memory recall for example). That feedback loop is a feature of complex, dynamic systems.
The question is, can this backward view also be applied to the underlying physicality, that is the biological system?
It leads us into areas addressed by the Baldwin effect and the Lamarckian evolution model. Lamarck proposed that efforts by an individual organism translated back to its genes so that the changes would be passed on to its offspring. Baldwin suggested that individual learning in a supportive environment leads to evolutionary traits influenced by such adaptation.
What sustains the debate between Lamarckians and those who subscribe to the Baldwin effect on one side and the adherents to the Weisman barrier (the principle that information only travels from genes to body cells but never in reverse) on the other is the fact that at this stage of our knowledge we do not have a model of how such links can be instantiated on the biological level - we can hardly define the forward-directed effects of genes. However, in terms of the mind's dynamics it can be shown that a mutual interdependency between existing phase states generated by the neurons and the conceptual results in the layer we can identify as mind does exist.
While this alone does not provide sufficient evidence to extend the model to biological systems in general (including genes), the universality of interdependencies in complex systems as can be described so far would at least suggest the possibility of similar functionalities occurring elsewhere.
Applying the displayed features of the Otoom model to wider society, certain fundamental observations can be made which relate to human activity systems in general.
Such systems can be identified in terms of
Functionally speaking in systems
See also The 10 axioms of Society.
It is important to remember that first and foremost the above refers to functionalities, rather than object-related content; these principles apply whether a resource is hay, mortar, or steel. They equally relate to systems in general regardless of their complexity. What differentiates a more successful version from a less successful one is the inherent capacity of its members to comprehensively process the past, consciously decide on their present, and realistically plan for the future.
While some of the items may seem almost trivial, readers will have to decide for themselves to what extent any of them have been appropriately realised in their own surrounds. What constitutes a 'system' for the purpose of observation depends on the scope of the focus. A demographic can be a system, or its host society can be seen as one, and societies form clusters of more or less common characteristics through economic and political blocs.
The picture gets skewed when an indiscriminate use of resources takes place without considering their sustainability in terms of the system's inherent capacity at the relevant scale. The current use of oil is one example, the application of aid in demographics that do not contain the ability to meet the costs of supplied resources from their own base, is another. As the emergent scarcity of food, water, oil and space on this planet shows, the dynamic principles outlined here are as pertinent as ignorance of them is dangerous.
Although the above points are expressed in a general form, the functional nature of the Otoom model permits, indeed invites, delving into greater and greater detail. The only limits are the resources available for observation and analysis (and of course one's own capacity for understanding).
This is not the first time human behaviour has been analysed. But so far the information was often incomplete, tainted by subjective interpretations, and without technical basis. The formality of Otoom enables comparisons to be made across data, timelines, and local perspectives.
Note (February 2017): Applying the concept of functionalities through the Otoom model also becomes increasingly significant at a time when the development of surveillance using data matching reaches ever rising levels of sophistication. For example, China wants to turn urban centres into smart cities, where cameras and other detectors combined with data from city administration, waste management, pollution tracking and such will be used to provide a streamlined system of information that includes the personal IDs of citizens. The automatic tracking of people's behaviour and using this information to alert police in case of suspicious behaviour may not (yet) explicitly follow Otoom's framework, but would have to involve techniques that come a close second (K Yu, "China's smart city plan to boost surveillance", SBS News, 18 August 2017, https://www.sbs.com.au/news/china-s-smart-city-plan-to-boost-surveillance.