Research

Systems Intelligence, Systems Thinking and Systemic Intervention

Profound belief in the general systems theory has led GTTi to the practice of Systems Thinking.  In looking at nature, organizations and other challenges; the process of seeing them as interactive, interdependent systems has had a significant impact on our research and consultancy.  

This section is a portal to our work in this field, the work of academic researchers and other items related to systems thinking, systems intelligence and system intervention. 

Systems Thinking has been a cornerstone principle in work we have done in Healthcare (Preventative Medicine and Longevity), Education (Systems of Learning), Service industry (Sales Optimization and Operational Efficiency).  Systems thinking frames most of our research, as it forces us to look at the inter-connectivity and the system dynamics.    

          Bibliographies on Systems Thinking


Systems Thinking Thought Leadership:  Points of View

Collaborative Algorithms and the Power of Adaptive Agents

Adaptive Agents, Collaborative Algorithms and Multi-Agent Systems are an emerging interdisciplinary area of research and development involving artificial intelligence, computer science, software engineering, and developmental biology, as well as cognitive and social science.  GTTi's application of adaptive agents and interest in developing collaborative algorithms is centered on the development of our Software as a Service applications, so it continues to learn about the system and the cohort as more information is added to it.  

Recombination & Building Objects

The work of John Holland and the Sante Fe Institute in the 70's on building blocks and recombination is essential to the work we do with organizations delving into who they are and what they do.  Holland posits that genetic algorithms function by recombining the building blocks of a system. Each product of recombination, each design, is tested for fitness. The building blocks of the best designs are recombined in a subsequent generation until fitness is found.  This process has been instrumental in the development of systems of optimization and organizational transformation, as looking at the building blocks of the organization provides a depth of knowledge critical to understanding the challenges and opportunities faced by the company.  This fitness mapping, is what we do when providing organizational analysis and data definition.  

Bounded Rationality

Brian Arthur’s (1994) paper on “Inductive Reasoning & Bounded Rationality” centers on distinct aspects of human capacity to think rationally showing severe limitations in this respect. We simply do not think deductively with any great ability: our minds are not built to hold multiple levels of perfect logic with ease. What we do well, exceptionally well, is to recognize patterns and fill in the rest; we build schemata that are constantly being improved by experience. Experience helps us become adept at filling in the gaps. We are inductive thinkers, with bounded rationality, as any chess player will testify. 





Data Defines Dialogue: Transforming Organizations with the Power of Number

Dialogue is an underlying element of the facilitation process that moves the organization from standing state belief to emergence. Stacey, Griffin and Shaw (2000) contend that dialogue is the basis of all solutions.  Price and Shaw (1998) posit that if language; the currency of companies1, frames how we see the world, then relanguaging frees us to new ideas and constructs.[Source credit: Price, I and Shaw, R. (1998), Shifting the Patterns, Management Books, LTD, Gloustershire, UK. Page 273.]

There is much evidence to suggest that the most successful businesses are still the ones that capitalize on human assets, and there are many ways to do so, but none of those methods are designed by spreadsheets.  In truth, a great business is one that recognizes the people within it as integral to it, not as a part in a machine, but as a part of a collective knowledge.  My method seeks to use patterns in the collective knowledge at an individual level, because change at this level is change at the root of an organization.

Schein defined dialogue in (1993) as: “Dialogue, on the other hand, is a basic process for building common understanding, in that it allows one to see the hidden meanings of words, first by seeing such hidden meanings in our own communication. By letting disagreement go, meanings become clearer, and the group gradually builds a shared set of meanings that make much higher levels of mutual understanding and creative thinking possible." 


As we listen to ourselves and others in what may appear often to be a disjointed, rather random conversation, we begin to see the bias and subtleties of how each member thinks and expresses meanings. In this process, we do not convince each other, but build a common experience base that allows us to learn collectively. The more the group has achieved such collective understanding, the easier it becomes to reach decisions, and the more likely it will be that the decision will be implemented in the way that the group meant it.1

[Source Credit:  Schein, E., (1993) On Dialogue and Organisational Learning,, Organisational Dynamics, Vol. 22, Summer 1993, Page 34. ]

 

Complex Adaptive Networks (CAN)

Complex Adaptive Networks for GTTi are the pulse of our inter-connectivity, and in our research we are finding new ways all the time to use the principles of complexity to expand the reach of genetic algorithms, improve the functionality of our Software as a Service applications and to provide an infrastructure for understanding the complexity of human dialogue.  

The work we did to develop Arborcision™ and our Utility Vendor Fleet Model Project were built from the concepts of complex adaptive networks.  The work we did to improve the process of Systems Assessment for Arbor Intelligence looked at the dynamics of complex adaptive systems in nature, and how this inter-connectivity impacts changes in the system.  We have applied the same techniques to the development of Pattern Intelligence, as one can not look at the parts of the organization in isolation, but rather we must learn from systems thinking, complexity and complex adaptive networks to develop solutions that provide lasting improvement and evolutionary leaps in organizational paradigms. 

White Papers in Complex Adaptive Systems and Networks: 
Computability and Evolutionary Complexity: Markets As Complex Adaptive Systems (CAS) 
[Source credit:  University of Essex, Markose (2003)]

Synaptic Node Analysis and Hypercube Framework

Complexity demands a more complex analysis of data and system's behavior.  Synaptic node analysis and hypercube framework offer a method to look within each building block, a multi-dimensional approach to the data, meta-data and the organization's approach to that information.  

 

[Source credit:  http://decoder.moy.su/photo/7d_hypercube/1-0-185 ]


Double Looped Learning

We have always found the work of Chris Argyris (et. al) on Organizational Learning and double loop learning to resonate with our beliefs in how organizations learn, discover and believe.  The reflective learning is an incremental part of our work and our ongoing research.