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Theories of Everything and TRIZ

| On 07, Jul 2008

By Darrell Mann

Abstract

The Theory of Inventive Problem Solving (TRIZ) builds on a solid foundation of knowledge and is based on the idea that a better understanding of the world of technology can be built by studying many thousands of examples of excellence. TRIZ researchers have done for technology, what others have done in their own domains of expertise. This paper examines what excellence looks like in some of these domains and what happens when knowledge is integrated into a “theory of everything” (TOE) whole. While concluding that knowledge unification can be merely the next step in a continuum, the paper makes some predictions about the implications a higher-level theory might have on the interpretation and use of TRIZ.

Introduction

As suggested by TRIZ, all systems pass through successive phases of increasing and decreasing complexity. Mankind’s understanding of the world has followed a similar pattern. During the 20th century, the dominant paradigm was increasing specialization and fragmentation of knowledge, but during the Renaissance synthesis and integration of knowledge fragments occurred.

Due in part to TRIZ researchers, it looks again as though a period of consolidation has begun. What TRIZ has done to map and integrate the world of technology, others are doing in the worlds of biology, physics, social history, psychology, literature, religion and economics. There are various compatibilities and contradictions among the numerous different domain-specific theories of everything; TRIZ practitioners may soon be entering a period where a synthesis of these domains into a higher-level unifying theory will be feasible.

Some may find the concept of a theory of everything futile, impossible, arrogant or a combination of all three; the intention, however, is merely to start thinking about a theory of everything known so far. Any time an ideal final result (IFR) is approached with a preconceived notion of what it will look like, a new definition occurs that could not have been foreseen when the problem was first identified. This is similar to sailing toward the horizon – no sooner has the crew reached where they thought the horizon was, then they begin to see a new horizon stretching into the distance.

In more formal terms, when talking about integration of existing knowledge, practitioners are embarking on a journey along the decreasing complexity part of the perpetual increasing-decreasing complexity cycle as seen in Figure 1.

Figure 1: Successive Periods of Knowledge
Divergence and Convergence

Others are thinking in the same direction as evidenced by texts claiming to know the secrets of the universe. Renowned economists James Gilmore and Joseph Pine’s latest effort describes their commodity-product-service-experience trend pattern as the economic theory of everything.There are also a plethora of TOE discussions from super-string theory physicists, and American author Ken Wilber’s TOE synthesis of the world’s religions and psychology. Paradoxically, perhaps, the vast majority of these attempts are built on a premise that a theory of everything can be built from domain specific knowledge. Thus, there are two extremes: on one end Wilber suggesting he has uncovered the secrets of the universe without having any apparent understanding of either science or technology and at the other physicists thinking they can ignore any domain that involves psychology or sociology, human or otherwise.

If any genuine TOE attempt needs to encompass all known domains, a potentially enormous problem is created in terms of knowing where to start when trying to piece together the jigsaw. The TRIZ law of system completeness is a good place to begin a discussion of the theory of everything and provides a journey from a structured foundation. If the law is universal, and there can be such a thing as a theory of everything, then there must be an engine-transmission-tool-interface-control pattern structure. And according to the operational researcher and expert in management cybernetics Stafford Beer, who also recognized the existence of the completeness law, this pattern must be recursive, as shown in Figure 2.1

Figure 2: Recursive Law of System Completeness

Law of System Completeness

Before starting a discussion of what comprises a complete system it is important to have a clear definition of the useful functions that a system delivers. On a strictly biological level, a system exists to survive, replicate and make more systems. From a more human perspective, this argument can be extended to also encompass some concept of advancement – people exist to improve.

Wilber’s TOE model offers the closest attempt to a model featuring five essential elements.2 Rather than five elements, Wilber’s world is divided into four quadrants: I, it, we and its. It is a 2 x 2 matrix with internal/external and singular/multiple axes. As illustrated in Figure 3, these four quadrants correspond closely to the TRIZ engine, tool, interface and transmission elements. Wilber is missing the control element needed to co-ordinate the other four. Possibly, in Wilber’s mind, this higher-level coordination role is something that Wilbur himself exists to fulfill. Whether Wilbur does or not, Figure 3 provides a structure describing a way of dividing the world into a number of interacting parts.

Figure 3: Law of System Completeness and Wilber
Internal-External/Singular-Plural Model

The links between Wilber’s constituent part definitions and those of TRIZ are easier to see when relating the system to the function of advancement:

  • Engine: Ideas form the engine for advancement. Ideas fundamentally come from inside the mind of an individual (others can build on an idea, but each idea can only come from one brain) – Wilber’s “I.”
  • Tool: A physical manifestation of an idea gives us a representation of the tool element of the complete system. The manifestation of an idea occurs when an individual’s internal idea is turned into an external reality – Wilber’s “it.”
  • Interface: If the interface is what the tool acts on, then the interface becomes the world upon which the manifest idea acts in the TOE model. In the Wilber model, the thing acted upon is the “its” that the idea has an impact upon.
  • Transmission: The transmission connects the engine (individual idea) to the tool (the manifestation of the idea). In the Wilber model, therefore, it is the “we” that helps perform this translation.


The Tool (External-Singular) – The World of Technology

It is difficult to contest the idea that the father of TRIZ, Genrich Altshuller, has made the single biggest contribution to the “it” world of manifest ideas. If a TOE requires the ability to uncover and synthesize patterns, then TRIZ research has made an enormous contribution to the identification of patterns in and around the world of science and technology. In addition to being more familiar to readers, another reason for starting with the pattern finding in the “it” domain is that change and advancement tend to happen faster in the human engineered environment than in the environment at large. In the same way that biologists study fruit flies because evolution patterns can be observed more readily in a species that breeds in short cycles, studying patents and technological systems allows TRIZ researchers to observe many mutations over a short period of time.

Research shows that the large majority of attempted innovations fail – approximately 98 percent according to some figures. In 2007 nearly a half a million patents were granted. Assuming that 98 percent have failed, or will fail, to become commercially successful, only 10,000 will succeed. Ten-thousand data points per year describing how the world of science and technology advances is a large enough number to identify some distinct patterns of advancement. These patterns are the pillars of the TRIZ/systematic innovation methodologies.

Perhaps the biggest of these pillars is the idea of successive chains of contradiction emergence and elimination. Consequent to this are the discontinuous shifts that occur as any system advances following the resolution of each contradiction. Compared to the other four TOE domains, the enormous number of such discontinuous shifts provides a clear model of s-curves and non-linear shifts from one s-curve to another as shown in Figure 4.

Figure 4: Optimization and Discontinuous Change
as a Model for Evolution

By asking questions like “What do these jumps look like?” TRIZ has subsequently identified a number of distinct patterns of discontinuous evolution. In this model of the world, there are now 37 such patterns.3 Other researchers have divided the same basic world in other ways. Remaining constant is the belief that the solid-liquid-gas-field pattern shown in Figure 5 is the one that acts as a spine for all others.

Figure 5: Object Segmentation Trend

The Engine (Internal-Singular) – Intra-Personal Psychology and Brain Function

Because there is so much data in the world of technology, discontinuous evolution patterns like object segmentation are relatively easy to see. The human brain, by comparison, has evolved more slowly. And yet knowledge of how the brain works remains relatively sparse. If discontinuous shifts define advancement, then it is possible to begin to see a number of different psychology jigsaw pieces starting to fit together. Probably the biggest of these pieces is the one uncovered by U.S. psychologist Clare Graves. In many ways, Graves was to psychology what Altshuller was to the world of technology. Graves’ life work was trying to integrate different models of human psychology and attempting to (although he never used the words) create a unified theory of human development. Graves said, “Emergent cyclical conception of adult behavioral systems and their development.” This probably explains why, today, it is better known as spiral dynamics.4

Perhaps Graves’ biggest contribution to the world has been the uncovering of the discontinuous jumps that give rise to different models of human thinking. Figure 6 shows the different thinking modes and the typical contradictions that serve to trigger the shift from one level to another.

Figure 6: Spiral Dynamic Thinking Levels

The spiral dynamics model also presents a number of other concepts and ideas consistent with Altshuller’s findings. Not least of these – as corroborated by many other psychology researchers – is the idea of recursion. The German philosopher Georg Wilhelm Friedrich Hegel (1770-1831) was foremost in promoting the importance of contradiction resolution as a progress mechanism, although he never explicitly made a connection to the concept of recursion. The core of Hegel’s thesis was that an A or B conflict was best resolved by determination of a higher level C that explained and allowed both A and B to remain true.

Although it is difficult to see what new contribution he makes to the subject other than saying things in an easy to understand way, it is also worth integrating into the “I” model, the description of recursive brain physiology made by author Jeff Hawkins.5 Hawkins’ explanation of brain architecture and function, when coupled with Edward De Bono’s contributions on the importance of non-linearities in the creative process and Roger Schank’s model of hierarchical information organization in the brain all serve to provide a rich picture of the creative process within an individual. There are two key uniting themes in all: contradiction resolution as the primary mechanism of advancement and hierarchical recursion as the cornerstone of information organization.

The Interface (Internal-Multiple) – Inter-Personal Psychology and Societal DNA

Not long after the engine creates an idea, other people are required to test and verify the validity of that idea. The “we” or “interface” part of TOE can, therefore, be considered the voice of the customer. People liking the proposed advance is a significant determinant for whether a new idea is successful.

In moving from the “I” to the “we,” the attention shifts from individual to group and social psychology. If the TOE requires input from people who have been attempting to integrate knowledge, the best equivalents to Altshuller and Graves are U.S. historians William Strauss and Neil Howe.6 Like Altshuller and Graves, Strauss and Howe’s primary research objective has been to uncover patterns in the mass of social history research data. Moving from the technical to the individual to the group involves an order-of-magnitude leap in complexity. In addition to increased complexity, the relative scarcity of reliable historical data makes the social pattern finding task more difficult. Nevertheless, the resulting “fourth turning” findings provide some highly consistent findings to both Altshuller and Graves.

Recursion and discontinuity feature large in Strauss and Howe’s model of the U.S. and Western European world. Strauss and Howe uncovered a repeating pattern of generation cycles making up societal s-curves. Figure 7 reflects the essential elements of this picture. The fourth turning model is the concept of large-scale four-generation societal patterns that emerge from a bottom-up model of parental influence – the way parents raise children influences the way children raise their own offspring. Subtle shifts in this parental influence from one generation to the next then produce macro-scale shifts in society. Strauss and Howe offer compelling explanations as to why people in the Baby Boomer generation, Generation X and Generation Y are all so different.

Figure 7: Generational Cycles

The model shown in Figure 7 also shows further evidence of the importance of emerging and resolving contradictions as a primary societal evolution driver. According to Strauss and Howe, these societal contradictions climax every 80-90 years (i.e., every four generations), resulting in a significant shift in society.

According to the model, society is entering one of these societal contradiction periods, and it might be fun to speculate on some of the implications on the world. While undoubtedly interesting, it will not help assemble the TOE model. It is the important to study the emerging connection between the fourth turning model and consumer and market trend patterns. According to research, by integrating Strauss and Howe’s work on generational cycles with Graves’ work on spiral dynamics, a framework is created that not only maps past and present market trends, but also makes a stab at predicting future ones.7 Figure 8 offers a first hint at what this framework looks like, along with some trend examples – creating a pair of jigsaw pieces that start to fit together.

Figure 8: Generational Cycles + Spiral Dynamics =
Voice of the Customer
/Trend Framework

The Transmission (External-Multiple) – Networks, Environment and Complexity

The jigsaw assembly job takes yet another turn to increasing complexity when exploring the fourth TOE system element. The transmission, or Wilber’s “its,” is in many ways the most complex of the elements – “its” is about the world that “advance” must find its way into. The world of the external-multiple is the world of survival of the fittest; if 98 percent of all technical advances fail, then a large proportion of them fail because they fail to win such survival competitions. In large part, they also fail because organizations fail to understand the complexities of the market environment they play in. Innovation can be little more than a lottery in a world where no one understands how everything connects to everything else.

Charles Darwin was probably the first big picture TOE contributor. Like Altshuller, Graves, Strauss and Howe, he spent time trying to uncover patterns in large quantities of data. His seminal work, On the Origin of Species, continues to be as relevant and influential as it was when it was first published in the middle of the 19th century. The text has been the subject of considerable enhancement over the years, but it appears to have hit upon some fundamental and universal truths. Much of what is seen in Darwin’s models can also be seen in the other models.

Darwin also did not make a connection to things like s-curves and discontinuous shift as the basis for evolution. Darwin’s original proposal that discontinuous shifts were instigated by random mutations, although still believed to be a mechanism of speciation, increasingly has been challenged as the dominant mechanism. Irrespective of whether random mutation or, American biologist and university professor Lynn Margulis’ more plausible proposal that the dominant mechanism is actually the “symbio-genetic” merger of two forms, step change advances take place when contradictions are resolved.8 Such contradictions in nature tend to emerge through either sudden environmental shifts (consider the extinction of the dinosaurs) or through “arms-races” between predators and prey. Nature and natural systems remain as better optimizers (continuous improvement) rather than innovators (step change), and so it is difficult to find even a fraction of the number of attempted jumps as are present in the world of technology. A reason for this is that an attempted technical innovation is at least visible for a short while in the market; a failed mutation in the natural world will come and go before any scientist is likely to have any chance to observe it.

Another aspect of the natural world that resonates across other domains is the high level of complexity. Everything in the natural world is connected; any change in one part of the system has potentially non-linear impacts on other parts of the system. Emergent systems and complexity theory thus form an essential part of any “its” model. This also applies to modeling the interaction of systems beyond those found in the natural world. Gilmore and Pine, for example, have been finding patterns in the world of economics. They call their customer expectation trend the economic theory of everything.9 While this may be an overstatement of what can only be a partial understanding of reality, it seems true that this discontinuous trend pattern (reproduced in Figure 9) acts as an important part of the discontinuous evolution story. It forms the same sort of spine observed with the object segmentation trend illustrated in Figure 5.

Figure 9: Customer Expectation Trend –
The Economic Theory of Everything?

Staying in the realms of business and business systems, people like Benoît B. Mandelbrot, Peter Drucker, W. Edwards Deming and Peter Senge have contributed to the TOE. Senge in particular popularized the idea of s-curves, self-correcting systems and systems thinking as a whole.

Complexity and complex systems is probably the biggest piece that the “its” element contributes to a higher-level theory of everything. The key is the recognition that whether an attempted advance is successful or not is driven strongly by the complex interaction of a myriad of different elements. The key driver of those interacting elements, in turn, is identifying the singularities and conflicts among different trends.

The Control – Weaving the Tapestry

As in the TRIZ law of system completeness, the control element acts as an overseer of the other four elements. In the context of TOE, the control becomes the rules and regulations that determine how the engine, tool, transmission and interface work together. Schematically, the model and its five elements are shown in Figure 10, along with key figures contributing to each of the elements.

Figure 10: Five Innovation DNA Strands

Few people have dared operate in the control role in the TOE context. Playing in this zone opens one up to criticism from the other parts of the system. Paradoxically, the person who has contributed the most in this area would probably never make the connection to any kind of theory of everything himself. Nevertheless, ex-computer-game designer, Steve Grand and his attempts to build an intelligent robot, Lucy has forced himself to contemplate many cross-disciplinary boundaries in the drive to, bottom-up, work out how life-forms think, learn and interact.10

Building on Grand’s work, plus that of Christopher Alexander and David Deutsch, this is a preliminary attempt at describing the big patterns that a more holistic TOE can contain:11,12

Initially, the pillars remain relevant across each of the different domains. Seven pillars have been identified: ideality, functionality, contradiction, resources, emergence, recursion and space/time/interface perspective.3 Criss-crossing among these pillars are some unifying concepts and ideas.

  • Systems come into being and survive only if they deliver useful functions.
  • Systems evolve in a direction of increasing ideality, where the person wishing to perform the useful functions delivered by that system defines ideality.
  • All systems advance through sequential optimization and step change cycles as may be described by a pattern of s-curves.
  • Each domain possesses certain distinct and repeating patterns of discontinuous change. These patterns describe how systems within a domain jump from one way of doing things to another. They repeat consistently across all systems.
  • There are right and wrong times for step change jumps. A complex but mappable interaction of market trends and patterns ultimately determines when the jumps will and will not succeed.
  • There are right and wrong sizes of jumps – too big or small of a jump from the current system will result in failure. The jump “sweet spot” is driven by a complex mappable interaction of market trends, patterns and technical possibilities.
  • For any jump to succeed, the conditions of a viable system must exist immediately before, during and after the jump.

TRIZ Implications

One conclusion of this kind of TOE study is that TRIZ plays a significant, but relatively small, part. The foundations of TRIZ are built upon an analysis of just the technical aspects of the world. As such, TRIZ is a necessary but insufficient part of some bigger picture. From an innovation perspective, TRIZ provides guidance on what technical systems should evolve to become in the future. It is incapable of determining which of the possible evolutions are right at any given point in time; it is incapable of answering questions relating to geographically where or when an innovation should be launched; it is incapable of answering questions regarding how a given what can be realized; and, it is incapable of coordinating an answer to these questions.

TRIZ has largely failed to enter the mainstream in terms of either familiarity or usage. Some of the other elements of TOE help explore these issues. A particular problem appears to be the interface between the “I”’ – the person who will generate a new idea – and the “it” (in this case the TRIZ method itself). If, according to TOE, the “I” story is driven by discontinuous shifts from one mode of thinking to another, then there is likely to be an impact of such modes on how TRIZ is used and taught.

Spiral dynamics research shows the eight main thinking modes (Figure 6) all work in fundamentally different ways. What chance, in such a scenario, is there for a single way of doing things to satisfy all different modes? The answer according to ongoing research is “absolutely none.”13 Acceptance of such an idea requires a mental shift that runs almost 180 degrees counter to the prevailing driver ARIZ (the algorithm for inventive problem solving). ARIZ says that a trained mind must follow a specific step-by-step sequence. Because people think differently, they need different procedures to satisfy them. The following table shows how the different levels on the spiral “think” and how they will best respond to TRIZ:

Teaching/Using TRIZ at the Different Spiral Levels
TheoryToolsTemplatesExercisesProcesses
TribalNoHide the complexityEssentialOne “right” answerOne- or two-step procedures
Feudal“World’s best”Quick hits, cards, gamesEssentialA clear “best” answer< 4 step procedures
Order“World’s finest problem solvers”Contradiction matrix, 9-windows, radar plots,patent database, no PI toolsEssential“Best” answer depends on contextSequential (ARIZ)
ScientificThree million data pointsTools should adapt to the userFlexible, feel free to adaptOpen questions, real problems, patent ableBuilding blocks to be sequenced as user sees fit
CommunitarianHere is what has been foundso farSegment the group according to what fits whom, emphasis on definition over solutionTeam decides, and possibly divides, into sub-groups – some with templates, some withoutMeaningful problems where learning points emerge from debate and discussionFlow-charts, if/then gates, divergent/convergent cycles, thinking hats
Holarchy/Holistic“All theories are wrong; some are useful”Think of this as a start point; if the individual thinks she can improve it, she can do soNoRelevant problems with no known solution, the bigger the betterSelf-correcting

Look for similarities between the requirements at the different thinking levels and the way TRIZ may have been used or taught.

Into the Future

The naive naïve TOE hinted at here is still in its formative stages. In keeping with the idea that “all theories are wrong, but some are useful” it is currently being used as the foundation for author’s author’s research activities as he and his colleagues attempt to disprove the theory.

References

  1. Beer, S., Decision and Control: The Meaning of Operational Research and Management, John Wiley, 1966.
  2. Wilber, K., A Theory of Everything: An Integral Vision for Business, Politics, Science and Spirituality, Gateway, 2001.
  3. Mann, D.L., Hands-On Systematic Innovation, Second Edition, IFR Press, 2007.
  4. Graves, C.W., The Never Ending Quest, ECLET Publishing, California, 2005.
  5. Hawkins, J., Blakeslee, S., On Intelligence, Times Books, New York, 2005.
  6. Strauss, W., Howe, N., The Fourth Turning: An American Prophecy, Bantam, 1998.
  7. Systematic Innovation E-Zine, “A Universal Market Trend Framework?” Number 72, March 2008.
  8. Margulis, L., Sagan, D., Acquiring Genomes: A Theory of the Origin of Species, Basic Books, 2003.
  9. Gilmore, J.H., Pine, B.J., Authenticity: What Consumers Really Want, Harvard Business School Press, September 2007.
  10. Grand, S., Creation: Life and Howto Make It, Phoenix, 2001.
  11. Alexander. C., A Pattern Language: Towns, Buildings, Construction, Oxford University Press, 1978.
  12. Deutsch, D., The Fabric Of Reality: Towardsa Theory of Everything, Penguin, 1998.
  13. Systematic Innovation E-Zine, “Teaching the Different Spiral Dynamic Levels,” Number 71, February 2008.

Note: This paper was originally presented at The Altshuller Institute’s TRIZCON2008.