Dr. Ed Sickafus Memorial Archives
|Accessed & Edited by Toru Nakagawa|
Access: Mar. 15, 2020; Posted: Mar. 15, 2020
Editor's Note (Toru Nakagawa, Mar. 15, 2020):
This page is the secon half of the restructured copy of the Web site developed by Dr.Ed Sickafus in the WordPress plaform, as is the form accessible at present.
Since this Blog site is unfinished, some of the pages are brief draft, duplicated with others, vacant, etc. Such pages are shown with thin (not broad) fonts in their page titles.
|Sickafus Memorial Archive Welcome page||(5) Sickafus' USIT site|
|Japanese Archives Index page|
Site Structure: http://edsickafus.wordpress.com/
Site: Theories of Problem Solving for Innovation and Invention
(compiled by Toru Nakagawa, Mar. 15, 2020)
See the previous page: (A) Top Pages: on Theories of Problem Solving for Innovation and Invention
Author: USITer (Ed. Sickafus)
Theories of Problem Solving (Jul. 30, 2015) [Shor outline]
Intellectual Pursuit of Technical Problems Through Structured Abstraction (Jul. 30, 2015) (HTML About 15 pages, Extensive, but uncompleted manuscript)
The Theory of Problem Solving (Jul. 30, 2015) ==> HTML (only 4 lines)
Introduction to this Blog (Sept. 2, 2015) [See ]
Trial and Error Heuristic (Sept. 2, 2015) ==> PDF (2 pages)
Using Heuristics to Solve Problems (Sept. 29, 2015) ==> PDF (7 pages)
Introspection-Insight-Innovation Slides Oct. 2015 (Nov. 3, 2015)
==> See in the page: (4) Sickafus' Papers and Presentations
Glossary 8_4.Oct.15 (Nov. 4, 2015) ==> Glossary 8 (Oct. 4, 2015) (Posted: Nov. 4, 2015) PDF
I. Book sliding off Table (short, incomplete)
Publications: [See more detail in the pages: (3) Sickafus' Textbooks, Overviews, Tutorials
(4) Sickafus' Papers and Presentations ]
The 'Snake Oil Effect' ==> Presented at TRIZCON 2002
Heuristic Innovation (Textbook) ==> Textbook (2006)
Abstraction – the Essence of Innovation ==> IJoSI Paper (2009)
Subconscious Problem Solving Using Hazy Heuristics ==> IJoSI Paper (2013)
Brief USIT Tutorial ==> Tutorial at ICSI2014
Retired physics professor, retired industrial physicist, basic research in surface science, crystal morphology, single crystal whiskers, developer of problem solving methods (USIT, HI, Y3), and inventor. Hobbies: photography, travel, photo-poetry, published technical articles, textbooks (USIT, HI), essays on problem solving.
Theories of Problem Solving (July 30, 2015)
[Note (TN, Mar. 16, 2020): This part seems to be an outline of a draft, unfinished and written in some other place.]
Scope of writings found here:
A variety of writings discuss theories of problem solving. They contain developments in structured problem solving since the mid 20th century. Emphasis is placed on degrees of method from intuitive brainstorming, having no theoretical basis, to rather sophisticated, logical, structured methodologies.
Intellectual Pursuit of Technical Problems (July 30, 2015)
Intellectual Pursuit of Technical Problems Through Structured Abstraction
Ed Sickafus (Ntelleck, LLC, Grosse Ile, MI. USA)
Several intellectual levels of reasoning in problem solving are discussed as levels of rationalization. Each level has varying degrees of understanding and adoption. An understanding of rationalization in problem solving refers to ability to follow teachings and demonstrations at the level in question. At this level, a conscious effort is required to recall definitions and demonstrations in order to understand. Problem solving may involve use of aids such as, cue cards, lookup tables, flow charts, computer aids, etc. Whereas, adoption of a level of rationalization means that this is a working level requiring no conscious effort or aids. Reaching this level requires learning, experience, memorization and personal technique.
Problem solving refers to finding solutions to conceptual problems in technical fields – engineering design-type problems, for example. In particular, structured-type inventive thinking is discussed as a model. The procedures are generic; hence, their adaptability to non-technical fields is assumed, but is not demonstrated. Conceptual problems are abstractions of real-world problems, as will be explained.
Mathematical problems are not considered; however, mathematical reasoning is discussed as a demonstration of successful abstraction to differing levels of reasoning. Technical problems will be defined, analyzed and solved in terms of objects, attributes and functions, as used in the unified structured inventive thinking methodology1.
Experience in applying and teaching structured problem solving has uncovered difficulties and enlightenments that students encounter in learning, understanding and adopting a structured problem-solving methodology. These seem to relate to the level of abstraction a student exercises. The nature of levels of abstraction is examined.
The visible, tactile, audible observation of breaking a pencil lead while writing introduces an unwanted effect – a problem. Putting this observation into words to create a problem statement is an abstraction. Creating a graphic sketch of the situation is an abstraction. Everyone, given this situation, does the former, with varying degrees of skill. Many do the latter.
Asked to improve the design of the pencil, everyone immediately begins comparing past experiences with the given situation looking for solution concepts. This is an amazingly rapid mental process involving conscious probing and unconscious discoveries; most to be invalidated consciously, few to be tentatively accepted. At this level of rationalization, abstraction is limited to the verbal and graphic problem statements. That is, pencil lead, pencil, hand, paper, desk are all processed mentally as labeled or sketched.
Motivation for this writing is based on the hypothesis that an understanding of how we use abstraction in conceptual problem solving can aid both the instructor and the student. Students with varying academic and professional backgrounds may employ different levels of abstraction in their approach to problem solving, as well as to learning new methods. Appropriate accommodation to these differences can ease the learning process, and the adoption of a new method as a functional tool.
A number of problem solving methodologies exist and are used to solve technical problems. It is not the purpose here to explain these or compare them. Instead, one structured problem-solving method will be compared with one unstructured method for the purpose of understanding how structured thinking leads to innovative solutions to problems. The unstructured methodology is ordinary brainstorming, a procedure that every technologist uses whether or not they recognize it.
Abstraction Level of this Discourse
Suppose we are given a technical problem such as, "The gel dispensing system is loading bottles with too much foam, fix it." Before engineering a modified design and building a prototype, it is necessary to generate a solution concept. One approach is to brainstorm new ideas. Another is to use a structured problem-solving methodology. How the latter is used to produce multiple solution concepts is to be discussed. The methodology is not an algorithm that one follows until it produces a solution. Rather it is a process for sparking unusual perspectives of a problem so that one's mind makes new discoveries. Consequently, to appreciate this discussion requires some introspection into our personal thinking processes, especially to understand levels of abstraction we use in approaching a problem situation.
Conceptual Solutions for Conceptual Problems
One intellectual approach to solving technical problems is to divide the process into engineering and non-engineering phases. This may begin as a real-world problem defined with tangible parts, engineering-type mensuration and some malfunctions of concern. This situation is converted to a conceptual problem by stripping it of metrics (dimensions, color, weight, etc.). An unwanted effect is defined, and the situation described in terms of objects, attributes and functions1. This is now a conceptual problem to be solved with multiple conceptual solutions – a non-engineering exercise. Engineering begins as a solution concept is selected, scaled and rendered to a set of specifications.
Although this discussion addresses the pre-engineering phase of technical problem solving, we enter it with our engineering biases. Our technical training is already well ingrained in our minds and cannot readily be set aside. In fact, it often is a hindrance to adapting or even learning new problem solving tools. Subconsciously, every new idea is instantly scrutinized before being accepted or rejected.
As technologists we are trained to be critical thinkers. All new ideas are to be challenged to see if they pass the test of credibility based on our understanding of physics, chemistry, biology, mathematics and logic. These tests are filters by which ideas are culled for acceptability. With split-second reaction time we are capable of comparing a new idea with our mental database of technical experiences and decide if the new idea is plausible and worth further consideration. Such critical thinking derives from concise definitions, laboratory analyses, and logical reasoning.
Critical thinking distinguishes a technologist. It may also be a hindrance to innovative thinking – without the technologist being aware. Evidence of this can be found in the well-recognized phenomenon sometimes called "incubation". A problem that has not yielded readily to solution may be set-aside in our minds for later consideration while we engage in other non-technical activities (e.g., sports, hobbies, eating, sleeping, etc.). The problem incubates in our subconscious minds. Technologists often describe the experience of interrupting a sport, or awaking in the night with a fresh insight for addressing the problem.
A plausible explanation of the incubation phenomenon is non-critical thinking. Critical thinking is a conscious process by which the mind guards against "technically un-allowed" associations of objects and functions. Incubation activities may allow unguarded moments of non-critical thinking. In such moments, un-allowed associations of objects and functions can occur and, sometimes, produce surprisingly useful ideas.
Abstractions and Metaphors – Two Modes of Thinking
Definition of abstraction:
"1. The act or process of removing or separating.
2.a. The act or process of separating the inherent qualities or properties of something from the actual physical object or concept to which they belong.
Abstract: from Latin abstractus, "removed from (concrete reality)."2
Separating inherent qualities from the actual physical object, and considering general qualities apart from concrete realities are keys to abstraction. But what is the actual physical object? For our purposes the actual physical object is something that exists of itself, occupies its own space, and we can make "contact" with it through one or more of our five senses (each has an appropriate method of detecting, i.e., "contacting"). Realization of the object, as derived from our senses, is a mental abstraction. This abstraction allows subconscious recall of the object without its actual presence.
Another definition is needed here, as evidenced in the last sentence. That is a definition of metaphor.
Definition of metaphor:
"1. A figure of speech in which a term is transferred from the object it ordinarily designates to an object it may designate only by implicit comparison or analogy, as in the phrase evening of life.
2. Figurative language: allegory: parable. [From Greek, transference, from metapherein, to transfer: meta- (involving change)]" (ibid)
These two definitions, abstract and metaphor, allow a convenient separation of two modes of thinking. One mode, abstraction, will be used for the introductory and learning phases of problem solving. Here we are taught to use words and graphics to represent concrete objects. In practice, this requires a conscious effort of trial and error to find appropriate words and execute simple, but definitive, sketches as abstractions of concrete objects. Metaphor will be used to characterize a more advanced phase where the abstraction has become a figure of speech. That is, an abstraction is so ingrained in our mental process it has become a subconscious object, a figure of our speech. (In the last two sentences speech is a metaphor for our language of speaking and thinking.)
With this separation, concrete objects –to– abstraction –to– metaphor, a next level of abstraction becomes evident. Namely, once the metaphor stage is reached, a metaphor can become a mental object subject to abstraction. This is clearly evident in mathematics. Consider the process of learning and practicing calculus.
Algebra teaches us to represent the position of a thrown ball in its flight in terms of its elevation, y, as a function of a lateral distance, x; namely, y(x). When this abstraction becomes a metaphor it submits at the next level of abstraction to a general function without specific ties to thrown balls, f(ξ).
In analytical geometry we learn to draw a function, f(ξ), as a two-dimensional curve on graph paper. By now, f(ξ), from algebra, has already reached the metaphorical stage of our thinking. We then learn that the rate of change of f(ξ) can be illustrated by a straight line made tangent to the curve. When these graphics become metaphors in our thinking we move on to the calculus treatment of the subject as a next level of abstraction.
- First we learn that the average rate of change of a function, f(ξ), with respect to its independent variable, ξ, over a period, Δξ, from ξ to ξ + Δξ can be represented as
f(ξ) = f(ξ + Δξ) – f(ξ)
- From this abstraction of a slope we move to instantaneous rate of change by reducing Δξ to zero:
Lim f(ξ + Δξ) – f(ξ)
- After some laboring with such detail we readily accept its abstraction to d f(ξ)/ d ξ.
- Next comes simplification to f'(ξ).
- We even advance to the metaphors d/d ξ and D ξ as figures of speech for operators.
- From then on, we easily see and express word and symbolic problems in these figures of speech. After a time, metaphorical thinking at this level actually requires a conscious effort to dredge up the original definitions (1) and (2) above.
Simpler levels of mathematical abstraction, and their adoption to become metaphors, are illustrated in Table (1). Note, these are not necessarily sequential, some are learned in parallel with others.
3D objects => 2D sketches
Objects => verbal sounds (names)
"box", or "cube", etc.
Printed characters => sounds
Printed concatenated characters => words
A mental count => printed character
A word => a single printed character
Quantification and mensuration => words plus numbers
Word fractions => objects of arithmetic operations
ft * in/ft = in
Ruled scales => interpolation
Word problems => equations
pig and 2 cows
p + 2c
Geographic phenomena => 2D maps
Similar types of equations => classes of functions
2f'(x)+3x = 7
linear differential eq.
Multiple mathematical operations => computer code
Σxi + 3
Do () until …
A Strategy for Conceptual Problem Solving
Goals of structured problem-solving methodologies are to simplify the process, search thoroughly for solution concepts (multiple solutions), inspire innovative ideas, shorten the process and be applicable in multiple technical fields. Simplicity, thoroughness, innovation, speed and generic applicability, are attractive to both technologists and management in industry. There are implied efficiencies with broadly adaptable tools. There is also the expectation that technologists may find inspiration and direction to become effective innovators.
Should such goals be offered as claims, a label of "snake oil" would surely follow. Thus, the situation begs of proof through testing and results. Unfortunately, structured problem solving is further ahead in commercial adaptation than in academic verification. Definitive experiments by cognitive psychologists are needed. Meanwhile, structured methodologies have been proposed, used, improved, and spun off with repeated cyclic development of other methodologies for continued improvement.
Without independent definitive proof through testing, a structured problem-solving methodology may be evaluated by examining the plausibility of its underlying structure – is it leveraged from a logical strategy, and can it be personally tested? An effective strategy must address the five goals (simplicity, thoroughness, innovation, speed and generic applicability) in a logical fashion impervious to critical judgment (within the non-testing caveat described above). An outline of such a strategy is shown in Table (2). In ways to be shown later, the components listed are designed to support differing levels of abstraction of a conceptual problem.
Table 2. Goals and components of a strategy for structured problem solving.
Goal Component 1 simplicity one problem, problem definition, problem minimization, 2 thoroughness root causes, analysis tools, solution techniques covering all component interactions, generification of solution concepts 3 innovation unconventional perspectives, guidelines for new effects 4 speed concise problem definition, generic abstractions, analysis tools, procedural structure 5 generic applicability generic problem-statement abstractions, analysis, and solution tools
It will become evident that goals (1-3) are more attainable in early stages of training and application. Goals (4-5) develop more slowly because they benefit from lower levels of abstraction first becoming metaphorical thinking.
The goals and components shown in Table (2) will be discussed in order (1-5).
Beginning Levels of Problem Abstraction
Description of the pencil-lead breakage problem in words and sketches directly depicting pencil-lead, pencil, hand, paper and desk, and subsequent execution of problem solving using these abstractions, is the first level of abstraction. By the time we are addressing technical problems we already have perfected this level to metaphorical thinking.
A major step toward higher levels of abstraction is the introduction of "object" as a generic representation of real-world things (pencil-lead, pencil, hand, paper and desk, etc.). At this level, the real-world shape and the real-world name of an object are no longer needed. Abstraction allows the object to be renamed according to its function, and represented graphically as simply a box, for example. This abstraction brings the mind quickly to address functions of each object in a problem situation.
A practiced abstraction at this level is to name an object according to its most important function. Hence, in the pencil-lead breakage problem we might change hand to moving guide, or just guide (pencil → support, pencil lead → friable marker, or marker, paper → display substrate, or display, or substrate, desk → force reactor, or reactor, and others). Every new object name sparks the conscious/subconscious brainstorming process. Herein lies the beginning of structured problem solving.
With objects and functions come attributes and their modes of interactions along with procedures for analyzing them and mentally processing them for new concepts. These entail more abstraction with supporting definitions.
A simple working definition of a problem will be employed as any unanswered question; once answered, it is no longer a problem. Hence, problems exist in the minds of those who do not yet know their solutions. A puzzle is not a problem to the puzzle originator.
This definition says nothing about a problem being well defined and nothing about existence, plurality, or uniqueness of its solutions.
Three basic elements of all problem descriptions: objects, attributes, and functions.
Generic definitions not tied to a specific technical discipline:
Objects occupy space, exist of themselves, and in contact with another object support a function.
Attributes characterize objects without using metrics. A metric is not an attribute. Attributes describe "higher/lower", "heavier/lighter", "stronger/weaker", "brighter/dimmer", etc. Attributes are characterized by metrics.
Functions exist at the point of contact of two objects. Functions change or prevent the change of intensity of an attribute. Two attributes, one from each contacting object, support a function.
Effects may be functions in that they arise from two attributes. Effects and functions can be interchangeable words.
Transduction is a special kind of effect in which an attribute of an object is mapped onto another attribute of the same object (examples of named effects include piezoelectricity, sonoluminesence, magnetoresistance, and many others).
Simplicity in Structured Problem-Solving Strategy
The transition from a problem situation to a well-formulated problem is a critical phase of problem solving. It is simple to define a "well-defined" problem to be one properly couched for the solution methodology to be applied. Although true, this doesn't reveal much.
Prolog: Before discussing levels of abstraction in problem solving, the stage should be set to portray a technologist's starting point when bringing USIT to bear on a problem. Typically, a technologist has had a particular problem for enough time to have a fairly well developed understanding of the problem situation, to have investigated its history, to have found current solution concepts, and to have attempted some brainstorming to find any "low hanging fruit" (readily accessible solution concepts). If the stage thus set represents a USIT class, the student is asked to begin by listing all known solutions and any concepts recently found. These are to be set aside and a USIT analysis then begun to find unpicked fruit. With known solution concepts out of the way, the student is open to a fresh start with mentally challenging abstractions on the way to new insights and new solution concepts.
Prolog Example Problem
As a concrete reference for this discourse on abstraction, consider the following problem situation. Hold an inflated balloon in one hand and a pin in the other. Jab the pin into the balloon and it will burst. Why?
PEP_1: Make a sketch of the problem situation. Label objects and any pertinent functions. Write a statement of the problem. List root causes.
Note: At anytime a solution concept comes to mind write it down. Do not wait to reach solution techniques in the procedure before discovering solution concepts; but, don't stop for a concerted brainstorming effort either.
Levels of USIT Abstraction
Levels and sub-levels of USIT abstraction are described. These levels are derived in a self-consistent manner that complies with USIT's underlying structure and definitions. They are termed levels of abstraction not because of any innate features of USIT structure but because students tend to work at one of these levels. It seems that each higher level is a stretch of the imagination not readily reachable or useful at the current stage of thinking. In time, and with practice, these students may improve their capabilities of abstraction.
Level I of USIT abstraction involves organization of a definitive description of a problem (detailed but not complex) and then simplifying it to essential points of focus. This is accomplished in terms of simple words and graphics that contain the problem situation.
Emphasis is placed on the use of objects and functions – concepts readily adaptable to technologist's conventional thinking. An important tactic at this level is to write complete sentences, and to make definitive sketches (labeling all objects, functions and unwanted effects). These are crucial to initiation of clear and critical thinking. Carelessness, and lack of detail, obscures the usefulness of a problem's definition in guiding one's thinking process.
The most effective starting point combines sufficiently detailed verbal and graphic statements to capture the definition of the problem situation. These are then carefully reduced to statements that contain the problem (not define it). The thought process invoked in this transition, from the detailed to the simple, is a powerful exercise in abstraction basic to critical thinking.
In Level I, the first stretch of the student's imaginations occurs when they are required to simplify their verbal and graphic problem statements. Simplification requires further the reduction of the problem situation to a single unwanted effect. This may require deciding on one effect among several. Detailed verbal and graphic statements are attempts to tie thinking to the concrete – visual objects and dynamic interactions. Simplification attempts to move away from the concrete to a first level of abstraction.
Verbal and graphic statements serve to expose inconsistencies in one or the other. The mental process of simplifying them requires critical analysis of one's understanding of the problem. Underlying phenomenology begins to surface in the student's mind.
In addition to simplification, this level of abstraction requires the student to generify object names according to their functions – the most important function of each object.
Also at this level, object-object contact diagrams are used to describe a properly functioning system containing the minimum set of objects. This should expend no stretch of the analyst's imagination. However, it is intentionally bounded by a limitation of one function per object in the diagram with focus on a properly functioning system (one without a problem). This turns out to require a stretch of imagination for those inexperienced in this type of structured problem solving.
Requirements of detailed verbal and graphic statements, limitation to a single unwanted effect, transition from a set of objects that define a problem to just those that contain it, generification of object names, and initiation of a critical analysis constitute the first level of abstraction of USIT. For some students it may be as far as they can go without further instruction and demonstration. In such cases, students at this point tend to resort to brainstorming to find solution concepts. Often they are over eager to discover solution concepts and impatient to see the analysis stage completed.
Of course, they can find solution concepts with no more analysis than this. And that's fine, to a point. The shortfall here is that the student may never attempt a higher level of abstraction and miss new and unconventional perspectives of a problem. If the goal is to discover as many solution concepts as possible, and I maintain it is, it is essential to continue to higher levels of abstraction in pursuit of new insights and more solution concepts.
PEP_2: Restate the bursting balloon problem as a single unwanted effect. Identify the important object-object contacts. Minimize the number of objects. Generify the object names. Describe root causes in terms of contacting objects.
Level II of USIT abstraction consists of unconventional analyses to discover distinctive insights to causes of the unwanted effect. Here attributes of objects are introduced and become key points of focus. Experience shows that technologists have little difficulty with the concepts of objects and functions, but are not so familiar with attributes. Attributes, and their role in problem analysis, raise the procedure to a new level of abstraction.
Root causes are listed and stated in terms of objects, attributes, and functions. A statement pattern is used involving the pairing of two attributes, one from each object, for a single effect or function. As many relevant pairings as possible are found.
In the absence of root causes, a plausible root-causes analysis is executed. The same pattern is used; two objects in contact support an unwanted effect. In this case, the objects are analyzed independently to identify causal attributes of each object. Once this is done, the attributes found are paired, one from each of two objects.
The unfamiliarity of attributes, and the unusual concept of pairing them give rise to new perspectives of a problem – another way of examining the same problem situation.
PEP_3: Construct a plausible root-causes analysis of the bursting balloon problem. Build a tree diagram with the unwanted effect at the top, and the contacting objects at the next level below the unwanted effect. Under each object list causes by which this object could give rise to the unwanted effect. Convert a cause to an effect and iterate to lower levels of the diagram until attributes of the object are identified. A single level may lead to causal attributes.
[Note (TN, Mar. 16, 2020): The descriptions below seem to be an outline of further manuscript Dr. Sickafus was planning to write down, but left unfinished.]
Level I: Problem Definition
IA. The Unwanted Effect
A problem situation is reduced to a single unwanted effect.
The effect is described verbally and graphically in terms of objects and functions. Several sentences and sketches may be employed.
Objects are renamed according to their functions – a step easily glossed over and forgotten by students new to the concept.
Brainstorming starts at this level. Sometimes students do not progress beyond this level. That is, in teams or in class they may join in further abstraction, but during mental problem solving they resort to brainstorming the information generated at this level.
IB. Object Minimization
Points of functional contact between objects are identified along with the functions involved.
Supporting attributes of functions are identified.
Minimization of the set of objects to just those needed to contain the problem (not understand the problem).
On first learning of object minimization, students tend to be skeptical of its value and suspicious of information loss that will reduce the value of the results.
Level II: Underlying Phenomenology
IIA. Root Causes
Known root causes are listed.
Level III: Basic Phenomenology — Root Causes
If root causes are not known, a plausible root causes analysis is performed to identify attributes of each contacting object, which could support the unwanted effect.
OAF statements are generated with which to connect causal attributes.
Basic phenomenology of the unwanted effect is identified.
Levels of abstraction // Levels of understanding of USIT
Reduction of objects to a minimum set.
A pen punctures an inflated balloon and it bursts. Why?
A pen puncturing an un-inflated balloon does not burst the balloon. So pen is not an essential object to contain the problem. Balloon and air are two objects in contact that contain the problem. Hole in balloon is an attribute of balloon's shape. Elasticity of balloon and pressure of air are supporting attributes.
Try making a hole in an un-inflated balloon, then plug the hole, inflate the balloon, and release the plug — does the balloon burst?
- Formulation, analysis, and solution of real world problems.
- Formulation of problems in terms of OAFs.
- Easier handling of Ofs than As
- Formulation of effects as AFAs
"Unified Structured Inventive Thinking – How to Invent", E. N. Sickafus, 1997, Ntelleck, LLC, Grosse Ile, MI USA (www.u-sit.net).
Excerpted from "The American Heritage Dictionary of the English Language" 1980, Houghton Mifflin Co., Boston.
Introduction to this Blog (Jul. 30, 2015)
[See: Introduction to this Blog (in the Top page) ]
II. The Trial and Error Heuristic
Structure in structured problem solving refers to the graphic and verbal heuristics they employ. Trial and Error is an example of an early verbal heuristic. Typically, heuristics do not explicitly involve aspects of brain physiology in their explanation or definition. They came into use as tricks of the trade learned on-the-job or self learned from experience. Their validity arose from results in practice not from considering how the brain thinks. Various problem-solving methodologies have been developed that are based on collections of heuristics proven in practice or accepted on the weight of their plausibility. Specific collections of heuristics distinguish methodologies.
Heuristics can have names and classifications. Trial and error falls under the heuristic to simplify. Consider how it works and its implications of brain activity.
A very common use of trial and error is in recall, such as, recalling a person's name. Consciously stepping through the alphabet one letter at a time, and expecting the subconscious to discover the needed name, is how this works – when it works. It is so highly successful that it is commonly used without concern for how the brain does it. A name may also be recalled using a cue with which it was memorized, such as, an event, another name, a food, etc.
In essence, it appears that each cue voiced in the mind acts as a seed to cause the desired association to be recalled. This discovery has led some to the assumption that the name and its first letter, or an associated cue, constitute a filling system maintained in the brain. While that may be plausible it is not an established model from the laboratories of cognitive scientists. We don't yet know how the brain does it. Nonetheless, seeding is a common heuristic in problem solving.
Separate roles for conscious and subconscious signal processing
An important observation about this simplification heuristic is how it separates activities of the subconscious and conscious in problem solving. It appears that the conscious relies on the subconscious to discover a solution concept following seeding by the conscious. In practice, nearly each cue causes the subconscious to offer one or more trial solutions before the correct association is found. Each trial solution is vetted for correct association before moving on. But, who is doing the vetting? The conscious seemed to have posed the problem because it did not know the answer in the first place. So how can it now know when a trial solution is wrong?
Somewhere in this process the brain discovered it did not remember a particular name. So it decided to use the alphabet heuristic. In my experience, my brain, by this point, has already tried a random seed or two and has not made any progress. It then remembers to use the alphabet heuristic and proceeds to test sequential letter seeds. Hence, a process of using random seeds is tried first and then the brain shifts to testing using organized (logical) seeds, A – Z.
Separation in function and time
The new bilevel model of the brain places conscious and subconscious on different levels. (1) Subconscious exercises random thinking and finds ideas while conscious uses logic with which it voices rational communication of these ideas. It also invokes another finding from the laboratories of modern cognitive science research. Namely, that the conscious lags the subconscious in comprehension of an idea. The subconscious is aware about 1/3 second before the conscious. Evidently the subconscious randomly proffers a solution concept to the conscious and 1/3 second later, if not rejected, the conscious begins to voice the concept for communication purposes – both internal and external voicing.
The 1/3 second lag is a very long period of time on the scale of subconscious and conscious processing of neuronal activity. With our chemical and physical sensors creating warnings, and other useful information for maintaining our homeostasis, there are thousands of different issues to address. More of them are handled by the subconscious than the conscious while it is busy also translating the neuronal signals of thinking to logical language.
- - - - - - - - - - - -
(1). S. Dehaene, 2014, 'Consciousness and the Brain – Deciphering How the Brain Codes Our Thoughts', Viking.
Using Heuristics to Solve Problems (Sept. 29, 2015) PDF (7 pages)
Using Heuristics to Solve Problems
Heuristics are ubiquitous in technology. This essay addresses the use of heuristics in problem solving methodologies (PSMs) in ways that make them effective. Issues discussed are what to expect of heuristics, their limitations, and how to think during their use, i.e., how to voice to yourself what you mentally focus on.
Cognitive scientist's research in this century has discovered that the brain doesn't use logic in problem solving. 1 The subconscious discovers solution concepts, which it proffers to the conscious for logical voicing both internally and externally – a bilevel model. Introspection is a key ingredient of exercising heuristics. In part, the motivation for this essay relates to the new appreciation for introspection revealed by cognitive scientists. References to logic herein allude to the new bilevel model of thinking.
Using heuristics to solve any type of problem is the fact and the art of discovering and constructing solutions to problems. It's the fact because the definition of a heuristic is an aid to thinking – it provides for the logic of analytical-type solutions to problems. It's the art because it imbues intuition, dodges unaware constraints of logic, and thereby discovers the unexpected insights of innovation. Hence, it covers both the technologist's need of a rational algorithmic path to meaningful construction of solutions and the inventor's need of freethinking for new insight. Those are the good parts of heuristics. Missing in this simple definition is any explanation of what a heuristic is and how to use it.
In the opening sentence of the above paragraph 'any type of problem' was mentioned. My preferred definition of a problem is any unanswered question – implying scope. Hence any is covered. A key to broadening scope of PSMs is the use of metaphors to generalize problem statement and analysis – a move in the opposite direction of using the argot of specialized technologies. Heuristics are plastic aids not rigorous rules.
A question produces some kind of disturbance in one's otherwise quiescent neuronal network. Instantaneously, the subconscious addresses this disturbance to cull for threat to one's homeostasis. Passing that test the subconscious sets out to find instances in memory that may resolve the disturbance; i.e., solution concepts or pieces of them.
Strategies of heuristics
Since heuristics are aids to thinking in problem solving they usually have implied or stated strategies. For example, seeding is a common heuristic for recalling a person's name. It involves mentally stepping through the alphabet one letter at a time and awaiting a suggestion from the subconscious. It works sometimes but not always. The assumed strategy in seeding is that a voiced clue (a letter in this case) somehow steers, or links, the subconscious to a potential answer or relevant concept.
No heuristic problem-solving methodology having a selection of heuristics can be completely comprehensive, in the sense of having covered all of solution space (whatever that may mean). So when seeding fails, for example, the problem solver moves on to other heuristics. There are many.
PSMs are collections of selected heuristics designed to generalize the scope of problems they can be used on, to accommodate different problem-solving situations, to adapt specific PSM tools, to unify the way they are applied, to emphasize the style of their application, or to use different underlying philosophical theories.
PSMs include brainstorming
A particular real-world industrial problem-solving situation that I am familiar with is so called 'fire-fighting'. The name implies urgency requiring speed and innovation. For example, discovery of a product defect or failure that affects safety requires immediate attention. Brainstorming teams are effective for this purpose and the method requires only plausible solution concepts – i.e., no filtering.. Proof can come after ideas have been captured.
Typically, an ad hoc brainstorming team is brought together to find as many solution concepts as it can as quickly as it can. Brainstorming, a fundamental, automatic way that technical brain operates is the PSM of choice for generating ideas rapidly. Its strength relies on its user's technical training, experience (specialized memory), and imagination.
My most memorable experience with brainstorming (not a fire fight) occurred in a group of about 25 scientists and engineers from the research staff of Ford Motor Company. Management had a prepared a random list of potential technical problems and new product opportunities for us to attack – and interesting test of closed-world thinking. We were given a meeting time and place but no advanced information about the meeting's contents. It was a very interesting meeting that had a slow start and ended in high spirits. However, it was noticeable how quickly fresh ideas waned. This was handled by moving on to the next topic. A philosophical insight I got from the meeting was that we seemed to consist of two types of brainstorm thinkers, those who offered ideas instantly and those who improved on them. This meeting led to my interest in development of PSMs.
An example of differing underlying philosophy of PSMs is the difference in the Russian theory of solving inventive problems (TRIZ, ca 1970s 2) and a fourth generation spin-off called unified structured inventive thinking (USIT, ca 1990s 3-2014). In essence, the underlying philosophy of the former is to glean inventive concepts from the patent literature and reverse-engineer them to infer the inventor's thinking strategies. Consequently, it would seem, resulting insights arise from inferred strategy based on past experience of the inventor; i.e., someone other than the current problem solver. It uses a database of patent information and software to assist its application. It works.
In contrast, the USIT's underlying philosophy is to make the problem solver self sufficient without the need of mental crutches. It works.
Advanced USIT: After completing a working first version of USIT 3, I began teaching it and started an effort to simply it further. This effort led eventually to a modernized version using one verbal/graphic, the 'OAF' heuristic, and the method was referred to as advanced USIT. It was designed for problem solver's experienced with heuristics, USIT's or others. The method capitalizes on a problem solver's experience with heuristics and problem statements using metaphors (such as OAF). PSMs of the last century, USIT included, rely heavily on logic.
A new player on the block: In this century cognitive scientists discovered the bilevel brain. Its model replaces the lateralization model already deprecated years ago. The bilevel model has been adapted in a new PSM called I3 for introspection, insight, and innovation. 3(2015) Following the cognitive scientists discovery, I3 operates without logic and thus is not constrained in its search of solution space. It applies introspection to capture fleeting insights. (See its prequel; 'Subconscious Problem Solving Using Hazy Heuristics', 3(2014))
Expectations of PSM heuristics
Problem-solver expectations of heuristics have at least two variations. Some expect a heuristic to be a closed-loop algorithm. A problem statement is written according to a selected heuristic's requirements, it is processed according to the imagined algorithmic steps of the heuristic, and then the problem-solver sits back and awaits inventive solutions to come to mind – a form of seeding. This is like filling in the blanks and waiting for results without turning the crank.
The other variation is to voice a problem with mental variations, per the expectations of the heuristic, testing each word while watching for mental images and words that produce insight into solution concepts (introspection). The latter is effective because the problem solver is more likely to vary and test all aspects of a heuristic using personal on-the-job relevant intuition. A variable heuristic is not roadmap to a hidden treasure. It is a thought provoking set of symbols, words, and images that bring unexpected bits of memory to the fore for vetting.
Demonstration of applying a heuristic to a specific problem
An example of a variable heuristic is the object-attribute-function heuristic (OAF) unit of the heuristic innovation (HI) PSM. 4 An object that contains a problem is simplified to a single unwanted effect (the problem), the object, and a single active attribute of the object that is causal of the unwanted effect. The words attribute and causal, as well as function and effect, essentially are synonymous pairs allowing flexible voicing. In the O–A–F graphic, O is the object and A is an active attribute of the object that supports the original design function, F. If a failed function is involved, the F can show a strikethrough;
F. Solution concepts are found using this graphic by mentally visualizing what would happen if one of the two unions (–) were broken or an O or A were eliminated, altered, replaced or duplicated. An example OAF application for a single object follows.
A cook at risk
Consider this problem situation, which we can simply by reducing it to a single problem.
A cook has an egg in a pan of water being heated to boiling.
This situation poses several potential problems for the cook. Problems such as boiling too long ruining the egg; boiling to dryness risking egg explosion; contact with the hot pan risking personal injury; contacting spattering water risking personal injury; and possibly others. Spattering water looks like a single-object problem containing the heart of this problem situation. So I'll select it and see where application of the OAF heuristic leads.
Here's my Introspection. I noticed immediately that strictly, injury is not a designed function, and it is not a failed function. It's an undesired effect. Note the flexibility of voicing OAF, versus potential rigidity of its literal interpretation. Voice flexibility is the fodder of innovative thinking.
This situation's one-object O-A-F heuristic is ... I had to think a moment. F means function – a word having 'design' connotation. Effect is a word having causal connotation. Injury is a non-designed, unwanted effect. So I chose that latter and its metaphor:
Generic statement; object – attribute – unwanted effect
Metaphoric statement; spattered liquid – hot – to mar.
To injure could have been selected as the unwanted effect, but it seems to imply human presence. Whereas to mar is more generic and could also involve non-human contact. Contact seems to be the causal essence of this problem.
A simple image of spattering water comes to mind; that of an exploding bubble ejecting hot water drops from the boiling-water's surface, as illustrated in Fig. 1 .
Figure 1. The left image shows a volume of liquid (blue) with a rising bubble. The right image shows the bubble bursting through the surface of the liquid releasing a spray of hot droplets and vapor. Dashed lines indicate changing positions of a rising bubble
As I drew these two sketches I immediately realized how simplified they were and that caused me to rethink what they may indicate.
1. The left-hand image of Fig.1 shows a hole in a volume of liquid. I drew it without a boundary because hole is not an object. You can't pick up a hole; it's an attribute of an object's shape. That woke me up to realize that liquid can't have a hole in it, it would collapse if it were vacuous! Obviously, an equal volume of vapor must support the hole. So I now have two objects, liquid and vapor. They are different objects having the same molecular attributes but distinguished by different phase attributes. The arrow shows the bubble rising, implying, evidence of outward internal pressure. Fortunately, I had not colored in the hole in the sketch. Vapor has no color; it's invisible.
2. The right-hand image of Fig. 1 represents the bubble bursting through the liquid-gas (air) boundary and spewing droplets upwards and outwards as a cloud of condensing vapor. As drawn, this is a very simple rendition of a bubble bursting through the liquid-gas interface. For instance, when the surface ruptures, it destroys the boundary of more tightly bound liquid molecules, i.e., the molecular layer of surface tension. Furthermore, in this case, sudden expansion is a local cooling process – a driver of condensation. Also not shown is the roiling, boiling state of liquid in a pan being heated. The liquid's surface is anything but flat. Surface tension can be expected to momentarily bulge the liquid's surface exposing some lateral region of the bubble as it bursts. This is hinted slightly in the figure. The roiling chaotic motion of the liquid's irregular surface during boiling also produces globules of liquid in addition to a mist of vapor, not shown.
Those are the ideas that came to my mind as I voiced my way through analyzing the two figures. Now I can see a plausible solution concept. As the surface erupts exposing a bubble's former interior water the walls collapse into curved surfaces as a result of surface tension. And there in lies a solution concept. Surface tension!
It takes more energy to form tighter surface bonds in liquid than is needed for the amount of energy to keep liquid from evaporating. This energy is released during the chaotic eruption of a boiling surface. If we could reduce the amount of surface tension energy, we might mitigate the seriousness of spattering.
A single tiny drop of surfactant (e.g., soap) added onto the liquid's surface would spread into at least a monolayer, thus replacing the surface tension of the liquid-gas interface with the lower surface tension of the surfactant-gas interface.
This is not a thorough analysis since it is intended simply to illustrate introspection in mental problem solving. Further analysis and other solution concepts might be found if this train of thought was continued.
Review of heuristic application example
Now I'd like to review what I've illustrated with my voicing and graphic sketching; namely, how I applied a heuristic, the OAF heuristic, in analyzing and solving a problem of a potentially hazardous water-boiling situation. What were your thoughts as you read this discourse?
As you read (internally voiced) what I wrote you also voiced my description in your mental interpretation. Two things surely happened. You saw other possible interpretations, and you agreed with parts of mine. So why did we have differences? One reason is that we have different technical learning and practicing experiences from which to draw explanatory concepts from our long-term memories. Another reason is that the same written words and illustrative sketches are also interpreted using different personal experiences – our genes differ. Another possible reason (God forbid!) is that I'm wrong.
(Well, as they say, you get what you pay for. ☺.)
Such differences are the fun and enlightenment of brainstorming teams.
Another aspect of flexibility or variability of heuristics is the depth and scope of their utility – a totally personal phenomenon. The thought process that I related above, as my introspection of the boiling liquid problem, is an abbreviated version of the more detailed experience that I continued for a few moments.
While mulling the presence of vapor supporting a bubble it came to mind to consider how the vapor got there; i.e., how does a single bubble form? Instantly, I was drawn into a description of molecules in increasing chaotic motion in the solid-liquid interface layer of near boiling liquid (on the bottom of the pan). Molecules enter and leave the layer with nearly equal probabilities until boiling is eventually nucleated. Once the leaving-flux (number of molecules per unit area per unit time) exceeds the returning-flux a local environment is created where fluctuations of vapor density eventually nucleate a stable bubble – albeit one still attached to at the solid-liquid interface, but soon to separate from the solid and rise while expanding to the liquid-air interface.
The more I thought about this phenomenon the more I was drawn to postulating additional detail including molecular mean-free paths, energetics of surface-to-volume ratios of free energies, and statistics of molecular kinetics. However, that exceeds the purpose of this illustration. It suffices to show that variable heuristics offer a problem solver much freedom of modeling and testing ideas. They also allow plausible solution concepts to be proven after idea generation ceases. I have proved none of the ideas presented here.
By the way, another idea came to mind. Position a fine-mesh screen near or at the liquid-air surface to reduce a bubble's size so it doesn't release as much energy on bursting. A floating screen comes to mind.
A heuristic for applying heuristics: Generify; i.e., think metaphor.
1. Dehaene, Stanislas, 2014, 'Consciousness and the Brain – Deciphering How the Brain Codes Our Thoughts', Viking.
2. Altshuller, G.S., 1983, 'Creativity as an Exact Science', (Translated by Williams, A, 1988) Gordon and Breach Science Publishers.
3. Sickafus, Ed., 1997, 'Unified Structured Inventive Thinking – How to Invent', Ntelleck (Self published) ISBN 0-9659435-0-X.
2006, 'Heuristic Innovation' (Self published, a free copy can be downloaded at edsickafus.wordpress.com).
2014, Proceedings, ICSI, Jul. 16-18.
2015, 'Introspection—Insight—Innovation Problem Solving for Innovation', TRIZ FUTURE 2015, ETRIA, Berlin Germany, October 2015.
(Also the blog; Theories of Problem Solving: edsickafus.wordpress.com)
Introspection-Insight-Innovation Slides Oct. 2015 (Nov. 3, 2015)
See ==> (2) Sickafus' Papers and Presentations
Glossary 8_4.Oct.15 (Nov. 4, 2015)
Glossary by Ed Sickafus Updated: 11/04/2015 9:30 AM
Words from Structured Problem-Solving Methodologies
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Active attributes are the distinguishing characteristics or properties of an object (think adjective) that are supporting a function. They distinguish two objects having otherwise similar inactive attributes.
bilevel model of the brain1,2;
The bilevel model of a thinking brain arose from research at the turn of this century by cognitive scientists. They discovered, using brain imaging with fMRI (functional magnetic resonance imaging) that in solving problems the brain functions at two levels, the conscious and subconscious levels. The subconscious finds ideas and submits them to the conscious for vetting and voicing.
Brainstorming, as used in this blog and by individuals or in teams, is the rapid process of generating ideas in response to a problem. It is done without mental crutches of any kind, such as, computers, handbooks, etc. It simply uses quick thinking, neither with filters, equations, numbers, nor other pseudo-logical constraints.
contact (think interaction);
Contact, or interaction, is the metaphorical contact of objects where a function of the objects can be described. If the attributes of one or more of the objects involve a field then action at a distance allows the function to extend beyond the physical boundaries of the objects.
For a theory to be viable it must be predictive and falsifiable. Falsification is the process of posing a contradiction of the theory that, if proven, falsifies the theory.3
function (or effect);
A function (think infinitive), or an effect, is a desired design feature of contacting objects, or a single object, that modifies or prevents modification of attributes. Functions include to change elevation, to modify color, to adjust location, to react force, and many others.
Heuristics are specific mental tools and tricks of problem solving. Some have been own through generations of problem solvers. Some are self-created. Some are taught in academia and others in on-the-job experiences. A few examples follow:
In problems having repetitions and other redundancies, reduce the number of repeating units to zero and then to infinity to understand how much repetition is required to get a handle on the problem – a form of simplification. It applies also in problems have implications of size, number, intensity, and scaling – consider all scalars, vectors, and tensor concepts.
• minimize number of objects;
This is a simplification process to reach a single problem by eliminating the objects that do not contain the principle problem.
Object, Attribute, Function; OAF is a graphic heuristic that unites the three words into a logically, self-consistent functional group. All of USIT's heuristics are now bundled into this one efficient, graphic symbol. BlogGlossary.doc 2:40 PM 8/24/2015 2/3 O – A – \ F – A – O / O – A – Three objects are shown interacting at a metaphorical point of contact. Objects provide one attribute each that actively support a function (a desired function, F, or a mal-function, F). When selecting objects, attributes, and functions in a particular problem to fit this diagram, each must satisfy the self consistency of the diagram.
Simplify a problem statement, a process, any complexity, etc. This is the number one consideration in every phase of problem solving.
• step through the alphabet
to recall a name or other attribute by which you may know a sought after object.
I3 is a problem-solving methodology that avoids the logic contained in heuristics commonly found in structured problem-solving methodologies.
A not so strict word in practices of problem solving is logic. Sometimes plausible may be a more appropriate word. I will not stress the issue here. I often use plausible as a personal preference. I prefer plausible as a less proven concept than possible.
Metaphors are used to describe adjectives and nouns in problem situation descriptions in order to avoid restrictive words that concretize an idea.
Plausible substitutes for possible when credibility of an idea in question has not had validation of independent proof.
Point is the very effective simplification of the shape, volume, surface, or extent of an object's contact. All of an object's unchanging attributes that extend beyond its point of interest are redundant and are reduced to the point.
A problem is an unanswered question. When answered the problem disappears. I don't know if this definition is original, but I began using it in the early 1980s to satisfy the need for breadth in its application and simplicity of its understanding. It immediately raises the issue of what is the definition of question? Since the basic interest here is how the brain solves problems, a question is a disturbance arriving on the brain's neural network that causes immediate action to determine whether it is benign or dangerous. If dangerous, the brain initiates instant action to remove its host body from the danger. The question is answered when the disturbance is nullified. This can happen entirely in the control of the unconscious. If benign, the brain begins to process the disturbance according to its properties and the brain's resources. Often a problem is encountered first as a logical issue. Later it may become a neuronal issue.
Problem situation is used in contrast with problem statement to distinguish an ill-defined problem (situation) expressed in metaphors from a well-defined problem (statement) expressed in concrete engineering specifications.
(See problem situation)
Solution is any nullification of a specific logic or neural net disturbance. Some solution methodologies have heuristics for deciding when a question has been answered.
Solution concepts are the first ideas to flash into one's mind when brainstorming. They may be vague, not fully developed, and certainly not adorned with engineering improvements. They are the first fruits of mental problem solving.
A common tool for describing and simplifying a heuristic is to display it graphically; e.g., a flow chart, a branching tree diagram, a fish-bone diagram, and others.
Ideally a theory should have several characteristics: it should be descriptive of a single problem or a set of similar problems; it should be predictive; and it should be falsifiable. Descriptive means a theory should be precise rather than verbose. Predictive refers to independent substantiation, i.e., independent of its originating circumstances. Falsifiable refers to: "One of the requirements for a valid hypothesis is that it be falsifiable.3 There must be some way to find evidence, which could disprove the hypothesis."
In the practice of structured-problem solving the word theory needs a broader interpretation. It often is not mentioned specifically but may be inferred from individual methodologies. For example, play the role of lower level theory. From this observation it is evident that different collections of heuristics can constitute the theory of a particular methodology and thereby arise multiple, practical theories of problem solving.
Recent discoveries of cognitive scientists on how the brain works has shed new light on the relative functions of the conscious and subconscious in problem solving. The latter discovers ideas while the former voices them logically.
USIT, unified structured inventive thinking;
USIT is a problem-solving methodology begun as a fourth generation of methodologies designed to simplify the Russian methodology called TRIZ. Originally, USIT was based on the bilateral model of the brain where one half of the brain favors logical thinking and the other favors intuition. Since its start a continuous effort has persisted to further simplify it and find ways to blend in a more realistic model of the human brain. For example, hazy heuristics were introduced (E. Sickafus, 'Subconscious Problem Solving Using Hazy Heuristics', Int. J. Systematic Innovation, 2(4), 2013) as a technique for softening the constraints of typical USIT heuristics at that time. Since 2014 the bilateral model has been deprecated and the bilevel model adopted where one level emphasizes conscious thinking and the other level emphasizes subconscious thinking. The bilevel model of the brain arose from laboratory research of cognitive scientists around the beginning of this century. (Ref. S. Dehaene, "Consciousness and the Brain – Deciphering How the Brain Codes Our Thoughts", Viking, 2014.)
1) Dehaene, Stanislas, 'Conscious and the Brain – Deciphering How the Brain Codes Our Thoughts', Viking, 2014.
2) Sickafus, Ed, 'Introspection—Insight—Innovation Problem Solving for Innovation', www.edsickafus.wordpress.com.
3) Wikipedia and http://www.skeptic.com/ eskeptic/11-08-17
Essays on various aspects of structured problem-solving theory. [Vacant]
[Note (TN, Mar. 16, 2020): This section is written in grey fonts instead of usual black fonts. Problably as a draft.]
Examples illustrate how to apply problem-solving heuristics. They analyze why and how they work. And offer relative critiques of problem solving.
I. Book Sliding Off Table
I. Book Sliding Off Table
NOTE: Please attempt your solution to this problem before reading mine. Such practice reveals what you do and do not understand – a proven learning strategy. (Note also that no one is watching!)
So you're walking past your desk and drop a book on it for later use. However, the book slides off and lands on the floor. That's a problem situation. Can you find a problem and solution concepts to this problem by turning on and off attributes of these objects? Begin with a point of contact. Identify objects and their active and inactive attributes at your chosen point.
Points of contact
At least six points of contact are evident, each having two objects:
1. book and finger;
2. book and finger;
3. book and desk;
4. book and desk;
5. book and floor;
The image I thought to describe is a moving arm releasing a book, which strikes and slides off of a desk, then collides with and comes to rest on a floor.
When book is held, two fingers may be grasping it. (Other ways of holding book could be considered.) The heuristic simplify suggests reducing redundancy to a representative minimum. I chose one finger-book contact.
Attributes of Objects
1. strain in book and stress in finger;
2. strain in book and stress in finger,
3. strain of book and rigidity desk,
4. sliding of book on desk,
5. impact of book on floor,
Mal Function of Contacting Objects
1. to prevent localization of book and its grasp;
2. to allow uncontrolled slipping from grasp;
3. not to absorb energy of impact;
4. to redirect motion;
5. to damage book or floor:
This is my quick analysis. No attempt was made for comprehensive examination. Next, if not during the above process, come solution concepts. Read the above analyses by corresponding numbers and record first ideas that come to mind. Do not filter your ideas.
1. Tie a rope around the book; carry it in a pocket or a bag; use thick, soft compliant book jacket that produces depressions from finger tips, which reduce chance of accidental release; .
2. (See 1.) Secure book to wrist, glove, shirt sleeve, …;
3. Carry book in an energy absorbing jacket, bag, purse, attach a parachute, …;
4. Put rails on desk; have an 'in-box' for quick deliveries;
5. Use shock absorbing book cover; use balloon-type book casing with built-in miniature air-bag explosive and igniter; book tethered to sleeve with too short length to reach floor; let your pet dog carry the book; fasten book to your walking cane; …
Publications are papers delivered at technical meetings, and published in other resources, that focus on specific aspects of structured problem-solving methodologies.
The 'Snake Oil Effect'
The "SNAKE-OIL" Effect
… encountered along the road to introducing companies to structured problem solving
(for management and salespersons)
Originally published TRIZCON2002
Ed Sickafus, PhD
See Slides ==> PDF (44 slides, 4 slides/page)
Heuristic Innovation (textbook)
See ==> Table of Contents in HTML
Full text in PDF
Abstraction – the Essence of Innovation
Published: Int. J. Systematic Innovation, Vol. 1, pp. 23-30 (2009)
See ==> Full text in PDF
Brief Tutorial on USIT (presented ICSI2014)
See ==> Abstract and Table of Contents in HTML
Full text in PDF (48 pages, 344 KB)
Subconscious Problem Solving Using Hazy Heuristics
[Note (TN, Mar. 16, 2020): In the WordPress Web site, Dr. Sickafus posted the paper manuscript in PDF:
Int. J. Systematic Innovation, 2(4), p (2013) in PDF (8 pages)
The paper was actually published as:
International Journal of Systematic Innovation (IJoSI), 3(1) (2014)
and was presented as:
Keynote Lecture presented at the 5th International Conference of Systematic Innovation (ICSI 2014), held at San Jose, CA, USA, on Jul. 16-18, 2014
I found this paper very important, and translated it into Japanese together with two colleages and posted in my Web site:
Japanese translation by Toshio Takahara, Hideaki Kosha, and Toru Nakagawa,
posted in "TRIZ Home Page in Japan", Jul. 29, 2015
Obtaining the permission by Dr. Sickafus and by IJoSI, the original English paper was also posted in my Web site:
Subconscious Problem Solving Using Hazy Heuristics, Ed Sickafus, TRIZ Home Page in Japan, Aug. 25, 2015.
in PDF and in HTML .
In relation to this tanslation work, I and Dr. Sickafus had many Q&A and email discussions. Please see the communications in the following HTML page (as well as the HTML page mentioned above ).
Q&A on USIT and the OAF diagram (in relation to "Subconscious Problem Solving Using Hazy Heuristics" by Ed Sickafus), (Q: Toru Nakagawa; A: Ed. Sickafus, Jun.-Jul., 2015) (Further Discussions among Ed Sickafus, Toru Nakagawa, and Shahid S. Arshad, Aug. - Sept., 2015), ]
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