current logic thinking

Current logic thinking: within the Institute for Futures Research (IFR) we studied South Africa’s societal, technological and ecological trends, many of them indicating deteriorating problems. We observed that the various societal problems mutually co-produce each other and that one cannot solve the one (e.g. poverty) before solving others (e.g. unemployment, population growth, education, health, land reform). With the IFR acting as a clearing house (i.e. compiling, comparing and further processing research from other specialist sources), we also took note of the various strategies suggested by researchers and policy makers. We came to the conclusion that the sum total of proposed ideas is grossly insufficient to significantly reduce most of the problems, let alone eliminate them altogether. Typically, the proposed solutions or strategies were derived from current strategic thinking (i.e. more of the same) and / or the results of problem analysis (i.e. problem logic thinking). (See also the blog section on Emergence; and the sections on Problem solving versus dissolving and Current versus Ideal Future on biomatrixtheory.com.)

These insights led us to explore systems thinking (e.g. systems dynamics, ideal system redesign and other methods) as tools for developing strategies that could actually (dis)solve the identified problems. It made us realise that the logic of most societal problems is not that of the solution (or as Einstein observed: the level of thinking that gives rise to a problem is insufficient for solving it) and that instead of fixing problems, the focus needs to shift towards redesigning systems and their interaction to produce different outcomes that do not reproduce the problems. Useful systemic approaches are systems dynamics modelling for understanding the current problem logic and its dynamics and ideal system design for designing strategies that will (dis)solve the problems.

At that time, I found the concepts and methods of systems thinking most appealing but insufficient for meeting the magnitude and complexity of the encountered societal problems. I therefore left the IFR to dedicate myself to further developments in systems theory and methodology. The Biomatrix Systems Approach (i.e. Biomatrix Systems Theory and its methodological application) is the result of this labour some 20 years later.

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relative figures

Relative figures are another irritating source of confusion. Many researchers and / or reporters on research argue with relative figures without giving the absolute ones at the same time. In the context of poverty, this often means a relative decline in poverty and an increase in the absolute number of the poor (largely due to the population explosion).

As a personal rule of thumb, I ignore any relative figure which does not provide a relation to the absolute. (On a personal note: I confess to an increase in smoking of 100 percent between 2011 and 2012, from 2 cigarillos smoked during my annual holiday in 2011 to 4 smoked during 2012!)

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assumptions

Without knowing the assumptions on which the statistics are based (e.g. different definitions of poverty, different assumptions concerning future economic or demographic developments) one cannot evaluate them. Knowing the assumptions, one can understand their differences. For example, at the time of my research at the Institute for Futures Research (IFR), unemployment in South African was officially about 25 percent of the total economically active population, while our estimate was about 45 percent. Both estimates were correct in terms of their definitions and underlying assumptions. To avoid confusion, we referred to our estimate as un- and under-employment and we always quoted both estimates and explained the reason for their difference.

Unfortunately, as a researcher or consumer of research, one cannot always discern the underlying assumptions of quoted figures, because of omission (e.g. researchers are sloppy about them, don’t quote them or are even unaware of them) and commission (e.g. researchers promote a deliberate ideological position and select evidence accordingly). Personally, I have sinned on both accounts on occasions.

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reflections on transversal issues

Transversal issues: a colleague recently made a study of the different approaches, outcomes and insights on poverty research by experts from different universities. He was concerned about the alarming divergence in statistics and what to do about it.

Oh, déjà vu: some 30 years ago, as a futurist within the Institute for Futures Research (IFR), I was involved in forecasts on poverty (also giving input to the Second Inquiry into Poverty and Development in Southern Africa in 1984) and encountered the same problems.

Concerning the divergence of statistics (e.g. some say global poverty is increasing, others decreasing), two sources of error seem most prevalent: assumptions and relative figures.

Concerning the diverging opinions on the causes of poverty and proposed strategies for solving it, two reasons need to be highlighted: current logic thinking and scientific fragmentation. They permeate practically all research related to all global problems.

One of the greatest transversal challenges is sustainability.

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open-endedness

Open-endedness of systemic research: systems thinking emphasises learning as a driving force of change. Learning allows systems to adapt to a changing environment and to co-produce deliberate change in itself and its environment.

Systems use iteration with the environment and self-referral (e.g. self-reflection) to generate the knowledge they need to adapt and develop. This leads to an alternation between divergence of knowledge (through iteration) and convergence (i.e. through self-referral based selection and integration).

Iteration can be spatial (i.e. information exchange with systems in the outer and inner environment). This is typically referred to as looped learning. It can also be temporal (i.e. consideration of past and future and their impact on the current situation of the system and its future development). This is referred to as anticipatory learning.

Systemic research builds looped and anticipatory learning into the research design.

Systems thinking also emphasises action learning. Besides the obvious argument that learning by doing is more effective, there are also systemic reasons for action learning, namely the difference in knowledge related to the physical and conceptual reality of the researched system.

Biomatrix Systems Theory distinguishes between the physical and conceptual reality of a system, analogous to an existing house and the plan according to which it was built. Learning how to make a plan is different from learning how to build. A marketing or production plan is different from actually marketing or producing the product or service. Action learning bridges the two realities. It allows learning relating to the conceptual reality, the physical reality and the interaction between the two.

Learning relating to the conceptual reality involves learning the generic systemic framework and organising principles that guide the design of all systems and their application to the specific system that is being analysed and (re)designed. The generic knowledge is trans-disciplinary, the applied knowledge requires discipline specific knowledge.

Learning relating to the physical reality of the system also requires discipline specific knowledge, albeit not necessarily the same. By analogy, the design of the house requires architectural knowledge, that of the builder requires building knowledge (e.g. bricklaying, cement mixing, electrical, plumbing, etc.).

Learning relating to the interaction between the conceptual and physical realities involves managing the building of physical reality according to its conceptual reality. By analogy, the builder needs to plan the building operation (i.e. designing the conceptual reality of the building operations through a project plan). There could also be a feedback from physical reality to conceptual reality (e.g. a need to change the plan due to resource shortages or unexpected terrain difficulties).

In a nutshell, there are layers of conceptual and physical reality within an action learning project and learners keep iterating between them and reflecting on it.

Systemic research should also build some action learning into the research design, as well as ongoing self-referral (e.g. monitoring and evaluating each phase of the action learning project, self-reflecting on learning throughout).

The typical conclusions and recommendations from conferences and research papers are: We need more research on this. What subtle (or maybe not so subtle) difference to state: We need to keep learning about this.

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observer

A neutral (i.e. value free) observer so as to not affect the research outcomes is a corner stone of traditional philosophy of science. Although this is increasingly challenged (especially in researching systems in the psycho-sociosphere) and new approaches that involve observer participation (e.g. narrative inquiry) have become acceptable, these can be enhanced and systematised through a research design based on systems theory. Amongst others, such a design would involve self-reflection and iteration, as well as divergence and convergence in the collection and processing of data (e.g. through iterative systemic surveys and / or survey and interview combinations that repeatedly feed the integrated outputs back to participants, using systemic frameworks and systemic organising principles to ensure comprehensiveness).

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emergence

Systems theory distinguishes between type I and type II properties of a system. Type I properties refer to the qualities inherent in a system and / or its parts. They are typically derived from analysis and the subject of traditional scientific research (i.e. type I research). They lend themselves to repetition, ceteris paribus and value neutral research (at least most times). Type II properties arise from the interaction of (parts of) a system with other systems in its environment. Since the environment of a system (especially the social environment) is continuously changing, the outcomes of these interactions are unique and not repeatable. Thus synthesis poses a challenge to the traditional philosophy of science which governs analysis.

Systems thinking is concerned with describing the organising principles around synthesis (i.e. the process and outcome of interaction). It is therefore a transdisciplinary body of knowledge. In interaction with a specific scientific discipline it extends the disciplinary knowledge derived from analysis with that derived from synthesis.

Transdisciplinary guidance is also important in issues such as sustainable societal development or problems like poverty, as these span all levels (from the planetary to the atomic) and dimensions (e.g. psychological, cultural, economic, political, technological, ecological, biological and physical) of life.

concept of emergenceSystemic research designs build emergence deliberately into the research process. Through iteration and self-referral in system analysis and design, new ideas can emerge that were not previously voiced. This is of special importance in a leadership context as it is concerned with initiating personal, organisational and societal change.

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new research paradigm

Students who want to do research relating to what one could loosely refer to as the “new research paradigm” encounter problems with finding promoters and a disciplinary and even interdisciplinary “home”. Amongst others, this type of research includes issues spanning arts and science, religion and science, consciousness, mind / matter, the role and impact of consciousness on social change, metaphysical issues and the apparent “miraculous”.

The distinction between the web and field perspectives of the biomatrix (i.e. the web of all natural, psycho-social and technological systems) as proposed by Biomatrix Systems Theory can be useful in contextualising this type of research.

More specifically, the web perspective describes the manifested reality of social, natural and technological systems and their organisation in time and space (i.e. the observable physical reality of systems which is the study of the various disciplines of the social and natural sciences).

The new paradigm research is associated with the field perspective. It is derived from more recent research in physics (e.g. quantum mechanics) and biology (morphic fields, holographic brain / mind functioning) as well as consciousness research. This research suggests that the field has different organising principles (e.g. it is universally accessible, is non local and not time bound). This field represents the conceptual (or potential) reality of systems and in-forms (i.e. puts form into) the systems in physical reality. Some research is also related to the interaction between the web and the field (e.g. the role of learning, the issue of the critical mass in paradigm shifts and the clockwise versus counter-clockwise flow of change in systems as proposed by Biomatrix Systems Theory).

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context in research

All issues involving social systems are multi-dimensional (i.e. have a psychological, cultural, economic, political, technological, ecological, biological and physical dimension) and span levels (e.g. planetary, international, national, institutional, organisational, community, individual, physiological, cellular, physical). Rarely can one dismiss one of the dimension or levels as being too insignificant to the issue under consideration.

The failure to contextualise a research issue is a serious error from a systemic philosophy of science perspective, even if acceptable by conventional philosophy of science. Most of the proposals during the colloquium would have benefitted by such a contextualisation.

Contextualisation of research refers to using systemic frameworks to identify multiple causation and multiple impacts associated with the topic of research, as well as frameworks and systemic organising principles for redesigning multiple strategies to (dis)solve problems. Systemic frameworks could also guide the literature search, making it more complete.

Generic contextualisation is based on considering the research issue within transdisciplinary frameworks and organising principles (e.g. that of a meta-systems theory).

Specific contextualisation requires the consideration and / or involvement of stakeholders. The underlying systemic principle is that a system has most knowledge about its own situation and in interaction with other systems creates knowledge relevant to itself. Involving stakeholders in knowledge generation acknowledges that they have unique knowledge relevant to the research issue and that this needs to be considered in the research. This does not imply losing focus in research as it is not any odd knowledge that is tapped from a stakeholder, but only that which is relevant to the research issue, whereby relevance is initially determined by the stakeholder. Later in the research, as all stakeholder perspectives are considered and integrated, the researcher determines relevance for the issue under consideration, based on the generic systemic frameworks and principles.

I observed during the PhD colloquium that some promoters were not familiar with systemic concepts and methodologies and resisted them. Their argument was that the student should focus on a specific issue and not be sidetracked by contextualisation, instead of understanding that it gives rise to new knowledge from synthesis.

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reflections on a phd colloquium

I recently attended a PhD colloquium with about twenty selected students from different countries and disciplines. It was organised in the context of a leadership conference and therefore the topics related to societal change. As the students presented their proposals, the systemic challenges of the current research paradigm were apparent. They include

context
new paradigm
emergence
observer
open-endedness.

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