Integrated Design Method 
 is a model that will help you understand 
and teach the methods of design.


by Craig Rusbult, Ph.D.

    This Detailed Overview of Design Method — which builds on the foundation of two other pages, An Introduction to Design and (especially) A Brief Overview of Design Method , that you should read before continuing in this page — examines the seven modes of design-action:


  2A. REMEMBER (gather old information) 
  2B. IMAGINE (generate new ideas) 
  2C. TEST (gather new information) 

  3. EVALUATE (and decide)


detailed diagram of Integrated Design Method

  Integrated Design Method (a detailed overview)

    To solve a problem, first you must recognize that a problem exists.  You recognize a problem (an opportunity for improvement) by understanding the actual current situation (the NOW-state), imagining a desirable future situation (the GOAL-state), and deciding that these two states (the actual NOW and the desired GOAL) don't match.

    interactions between modes of action
    Although 1A is "the first mode of action" in this model for a method, the method is not a rigid sequence of steps that must be followed in a particular order.  This is why the actions are called modes, not steps.
    Action in one mode often involves action in other modes.  For example, to DEFINE AN OVERALL OBJECTIVE by recognizing a problem you must actively SEARCH for information that already exists, in an effort to understand the current state;  you also DEFINE GOALS (for what is desirable) by IMAGINING a desirable future state and PREDICTING its properties, and you EVALUATE the current state and decide that it is less desirable than the imagined future state.  While you are blending these actions you can quickly shift back and forth between modes of action (usually not even recognizing that you are doing this), or you can take some time to thoroughly explore one or more modes.

    Notice that part of the IDM-diagram below is highlighted with a white background, so it will be easier for you to focus on the relevant actions:  based on old observations about product (from the SEARCH mode) you recognize a problem and define an overall objective so (in 1B) you can define goals for an improved product.    { The diagram below says "OVERALL GOAL" because it was made before I changed the term to OVERALL OBJECTIVE because this helps clarify the distinction between the OBJECTIVE in 1A and GOALS in 1B. }

note: In the verbal descriptions of IDM, each element of the IDM-diagram is highlighted in bold red print.  Each important concept that isn't in the diagram is in non-bold red print.

    A variety of goal criteria — based on personal values about what is important and what is good — can be used to decide whether a particular type of goal-state is desirable. The criteria are personal because "desirability is in the eye of the beholder."
    If the overall objective is an improved product (*), one way to define a goal-state (or a set of goal-states) is to specify the desired properties for a product in terms of its composition (what it is), functions (what it does), and performances (how well it performs each function).
    These three general criteria can be split into sub-criteria that are more specific and concrete.  For example, functions might be durability (Will it last?), appeal (Will people want it?), sales (Will people buy it?), and cost (Can we make it cheaply? Can we sell it for a profit?).  The corresponding questions about performance might be:  What level of functioning (for being durable, appealing, sellable, durable, or profitable) are you hoping for in your most optimistic dreams, and what level would you accept as minimally satisfactory?  These levels define your range of goals for performance.
    If some criteria are in conflict, how will you prioritize their relative importance?  For example, if increased durability requires increased cost, how will you balance these conflicting factors?  { It may be useful to think about "balancing" early, or it may be more useful to delay this until later when you are evaluating specific competitive options.  At that time, instead of trying to deal with vague generalities you may be able to use specific information, such as how much more it will cost to get a particular amount of increased durability. }

    * Designing Products and Strategies
    For simplicity, initially we'll focus on the design of products.  The design of strategies is analogous, except for one main difference:  While designing a strategy it is often useful to think about consequences, which are the properties of a future "state of being" that is the result of a strategy.  Other than this, each time you see "product" just replace it with "strategy" and you have a method for designing a strategy.  { But the design of a product is often accompanied by strategies, so the choice of a particular product has consequences associated with it, as discussed later. }

    Moving from Problem to PROJECT
  Problem solving occurs in three stages:  A) recognizing that a problem exists, that something could be improved and you have an opportunity to make it better;  B) deciding to pursue a solution for this problem;  C) trying to solve the problem by achieving an actual state that is a satisfactory match for the goal state.
    Stage A is discussed in Section 1A, and Stage C is the main theme of the sections that follow.  Between A and C, however, an important decision must be made in Stage B.  Why?  Because even when a problem has been recognized, this does not necessarily mean that a solution should be pursued.  Since there are many problems, but a limited amount of resources (people, time, money,...) available for solving problems, decisions about "what to do" are necessary.
    Moving from a problem (a potential project) to an actual project (in which you actively pursue a solution) is a strategy-question that requires evaluation and decision.  You must weigh the potential benefits and costs of one proposed project (to achieve one overall objective), compared with alternative projects (for other overall objectives), and decide whether this project is likely to be a wise investment of your resources.
    In making this decision, you ask questions about significance and practicality, about the potential benefits and costs, and the probability of a solution.  /  significance questions:  So what?  Why should we do it?  What are the potential benefits, and what are they worth?  /  practicality questions:  Is there hope?  Are there rational reasons to expect that our actions will lead to a solution, that this problem can be solved using available resources (people, time, money, materials,...) and your current knowledge and technology?  How much would a solution cost, in resources?  /  After comparing these questions for different potential projects, you ask "Is this project(s) the best use of our resources?" and you "go for it."

a preview:  The goal of Sections 2A and 2B is to help you improve your skills in finding the best ideas for designing a product (or a strategy), whether these ideas are old or new.   { a reminder: You can see the "big picture" quickly in A Brief Overview of Design Method. }

    2A. REMEMBER (SEARCH for old information)
  During a problem-solving project, an important part of solving a problem (by moving from an actual current state to a desired goal state) is understanding the current state more accurately and completely.  While you're trying to understand, you can also search for old (i.e., already existing) product-options that may be a potential solution, because it may not be necessary (or productive) to "re-invent the wheel."  This action is highlighted in white below, in a diagram that is streamlined by omitting the initial step of "Defining an Overall Objective:  you REMEMBER, you SEARCH for old information, for old observations about products that may be options for a solution.


     There is also semi-white (light gray) behind three elements:  generate (acquire or construct) a product, design an experimental system (involving the product), and do a physical experiment that produces observations about the product.  It is semi-white because when we ask "Are these actions necessary for remembering?" the answer is YES and NO.  Yes, it is necessary that someone did these actions in the past, to produce the observations.  No, you don't have to do the experiments, if the information (the observations, plus a description of the products and experiments) can be found with an information search of your own memory or our collective human memory (in books, websites,... and in other people).
    Knowing how to find information is a skill that will become increasingly valuable in the future due to the widespread use of modern information technologies.  Learning how to improve this skill — how to effectively use libraries, the internet, databases, and other sources — is a good investment of time.  Here are a few suggestions: be persistent (because a thorough search takes time, and usually diligence is rewarded), well informed (about the standard sources for finding particular types of information), creative (to find non-standard sources), and critical (to evaluate the reliability of sources).  Be sociable and ask for help from those who know about a particular field, and from reference librarians who can help you find sources and develop strategies for searching.  Often, libraries offer workshops showing you how to find information, and internet search services (and others) offer suggestions for effective searching.  { Later, maybe by the end of 2010, I'll provide links to web-pages about strategies for effective searching. }
    A third highlighting, aqua-colored, shows that searching generates empirical feedback when you compare old observations (from old experiments on an old product) with your desired goals for a product.  This feedback can stimulate and guide your search, when it is used for questions about action planning, for the long-term plans or just "what to do next."  You can ask:  Have I already found a satisfactory solution?  Do I have enough information?  Has my search been thorough, in breadth and depth?  Does it have enough breadth? (Have I checked a wide variety of old products whose compositions and functions are similar, in one or more ways, to my goal product? Should I continue searching for more old products?)  Is there enough depth? (Do I have enough data about compositions, and about the performances of each old product for each function? Should I continue to search for additional data? of what types? )  For action planning and product evaluation, information tables are useful.
    During your review of what is already known, you may find an old product (perhaps previously unknown to you) that satisfactorily achieves your goals, and you consider the problem to be solved.  If not, you can move into an "imagining" mode of action.

a comparison:  In 2A you REMEMBER and observe (in physical experiments) for old products, in 2B you INVENT and predict (in mental experiments) for new products.

    an I.O.U. for the reader:  I'm continuing to post this page that is still "under construction" because it's better than what was here before I began the revision (and there are many ideas that I think you'll find interesting and useful) but I wanted to put in this disclaimer.  I'm not sure when it will be finished: maybe sometime in 2011 but not earlier.  Until then, at various places in the next few sections I'll comment about the incompleteness so you can get an idea of what is worth reading (quite a bit of it) and what is in rough form.  /  Eventually it will be re-structured to fit the new mode-numberings (beginning with 3A-3B-...) in my Overview of Design Method.

The first part of 2B is in fairly good shape, although I haven't done a "second draft revision" of it yet.

    2B. IMAGINE (invent new ideas)

    To design, you must generate and evaluate.
    As shown below in white, you can generate ideas for a product in two ways — by selecting an old product (i.e., a product that already exists, that you have found by searching) or by inventing a new product — and then you do a mental experiment in which you make predictions about the product.  The aqua-area shows that your invention of new options can be guided by the goal-directed feedback you get by comparing goals with predictions.


    We'll look at these actions in two phases: generating new options, and making predictions so you can get feedback.

    Generating New Options
  To generate an option, you select an old option (that you remembered by searching in 2A) or invent a new option.  Whether or not an existing idea seems satisfactory, you can ask, "Could we obtain a better product by continuing the process of inventing?"
    How can you invent?  Since a "new idea" is usually a variation of an old idea, invention by revision (by modifying an old product to generate a new product) is usually a useful strategy.
    During invention by revision, a useful sub-strategy is analysis.  While you're in a SEARCHING mode of action, reviewing the properties of old products, think about the different parts of each product's composition, and how changes in these parts affect the product's functions and performances.  Search for cause-effect relationships by asking, "How do changes in what it is affect what it does and how well it does?"  While you're analyzing a variety of products, comparing their compositions and functions and functional performances, thinking about similarities and differences, you can search for correlational patterns and scientific principles.  The motivation for scientific theorizing can be simply to construct new knowledge for its own sake, or to construct the specific types of knowledge that will help you invent a new product.
    Stimulated and guided by these ideas, you can systematically modify an old product in ways that will generate a new product with the properties you want.  Or you can modify by combining parts from several old products.  Or look for ideas in other areas;  look at old products, parts, or principles that originally were intended to achieve goals different than your goals, and think creatively about how you can adapt a product that has been "imported from another area" to help you achieve your own goals.

    While you are "inventing by modifying" you are guided by your goals.  The purpose of a goal-directed invention (or selection) is to find a product whose properties will match your desired goal-properties.  Let's look at invention in science and design.
    In science, the main strategy for goal-oriented invention of a theory is a process of retroduction that combines logic with imagination.  In contrast with cause-to-effect deduction that asks, "If this is a true theory, then what will the observations be?", retroduction asks a reversed effect-to-cause question in the past tense: "These were the observations, so what could be the correct theory?"  The essence of retroductive inference is doing thought-experiments, over and over, each time "trying out" a different theory that is being proposed (by selection or invention) with the goal of producing predictions that match the known observations.  Basically, you are trying to find a theory that, if true, would explain what has been observed.  To do this, you are using a process of logic combined with creative imagining (for inventing a new theory) or remembering (for selecting an old theory).
    In design, a similar process is used to find a satisfactory product or strategy.  You do thought-experiments, over and over, each time "trying out" a different product that is being proposed (by selection or invention) with the goal of finding a product whose properties will match the goal-properties you want.
    During science you search for a theory which will make predictions that match the known observations.  During design you search for a product which will have properties that match your known goal-properties.  Do you see the parallels, the similarities (and differences) in process?

    As explained above, goal-directed creative invention is guided by critical evaluation.  But it can be useful to weaken the connection between invention and evaluation, at least temporarily, by "playing games" that shift the balance in favor of creativity for awhile.  This is the strategy in a two-phase technique of brainstorm and edit.  First, during a brainstorming phase you minimize critical restraints to encourage a free flow of creative ideas.  Later, in an editing phase these ideas are critically checked for quality.  During the creative phase you can feel comfortable thinking freely — trying to see in new ways and imagine new possibilities without critical restraints — because you have the security of knowing that any impractical ideas can be modified or discarded during the critical phase.  { If brainstorming is done in a group, a supportive environment is important, since comments like "that's a stupid idea" will not encourage colleagues to think or speak freely. }
    The purpose of a brainstorm-and-edit srategy is to allow the effective operation of both creative thinking and critical thinking.  Both are needed for effective design, but being overly critical, especially in the early stages of generating ideas, can stifle creativity.  We shouldn't hinder the motion of a car by driving with the brakes on, and we shouldn't hinder the flow of creativity by thinking with restrictive criticism.  A car needs brakes, and creativity needs the constraints provided by evaluation, but brakes or constraints shouldn't be used constantly.  { But it would be more accurate and more useful to consider the function of goal-directed evaluation to be steering, not braking, when it is used effectively. }

    I've described two general approaches: tightly focused goal-directed invention guided by evaluation (to produce GOOD ideas) and free-wheeling invention with minimal constraints (to produce LOTS of ideas).  Which approach is better?  It depends on the people and the situation.  Both approaches can be useful, and both can be used in synergistic combination, using each at different times in the process of design. // IOU -- here are ideas to organize, revise, and use: naive ignorance occasionally allows out-of-box creative idea, but more often "a good way to bet" is knowing old info so you can have ideas for revision, and avoid wasting time by re-inventing the wheel / use components of past ideas / hopes for [visions of, dreams of, ideas about informed / // smooth shifting from one to another in ways that seem effortless and typically unconscious //

    In this area of Thinking Skills, the resource-pages have more information about creative thinking and critical thinking and combining them in productive thinking.

The section below, about predictions, is EXTREMELY ROUGH; part of it is the original, part is imported from another page, and part is new (comments to myself, the start of ideas,...).

    Making Predictions and Getting Feedback
    Here, the goal-directed ------ is to eventually get predictive feedback. goal is accurate truth, to get ------
    After imagining a new product you can make predictions by asking "What properties would it have?"  To begin exploring this question, a common strategy is to ask "What would happen if we used the new product in old experiments?"  Based on knowledge from the SEARCH mode, select an experiment that has been done with old products.  Then run this experiment mentally with the new product, using "if..., then..." logic:  if the modified product was in this experimental situation, then we would observe ___ .   To fill in the blank with a prediction, do a mental experiment

construct a detailed mental model of the experimental system (of the product in its experimental context) and — by using everything you know about patterns and principles (that are based on your knowledge of precedents, on what you know has happened with old products in this experiment or in similar experiments) and all you know about the new product — ask "What will happen?" to produce predictions about the product.  basically, --- simplified (from scientific-method.htm) { As explained in An Overview of Scientific Method, predictions involve some combination of theory-based deductive logic and experience-based inductive logic. }
    Now you can evaluate the product by comparing goals with predictions to produce predictive feedback when you ask, "How well does this new product match my goals for the product?"   Predictive feedback can stimulate several types of action.  You may decide that the new product is a satisfactory solution to the problem.  Or you may want to continue your search by doing goal-oriented invention of products, design of experiments, or testing of products.  These three actions require creative-and-critical thinking, as described in the next three subsections.

    With either type of theory, the if-then inference is similar.  You think, "In this situation, IF the system behaves as expected (according to the theory), THEN we will observe ___" and what you put in the blank is your theory-based prediction.
    When scientists make an if-then inference, they can move from IF to THEN in a variety of ways.  They might remember what has happened in the past, and estimate how similarities and differences in situations (when comparing previous situations with the current situation) will translate into similarities and differences in observations.  Or, if a theory includes an equation or a model, they could substitute numbers into the equation and calculate, or "run the model" in their minds or in a physical simulation or computer simulation.  If predictions can be made using several methods, this will serve as a check on the predicting methods and a cross-check on the predictions.

The parts below will be moved and revised.  The first few paragraphs will be moved into 2C (for designing of physical experiments) and then I'll refer back to 2B.  The second big part, with the TABLE, will be moved into Section 3, and will become a main focal point (with links to it from 2B and probably other places, since it can be useful for inventing, action planning, experimental design, evaluation,...).

    Designing Mental Experiments
    The purpose of goal-oriented invention of products is to achieve the main objective of design, to obtain a product that satisfactorily meets your goals for desirable product-properties.  By contrast, the purpose of goal-oriented invention of experiments is to achieve a sub-objective, to obtain information that will help you achieve the main objective of design.
    In IDM the definition of "experiment" is intentionally broad, to cover a wide range of situations: a physical experiment is loosely defined as "an opportunity to gather information" whether this occurs in a lab or outside, whether there is an attempt to control variables or to observe a product "as it is" in an uncontrolled real-world setting.
    There are many possibilities.  You might want to focus on getting information about one property — such as a car's acceleration, gas mileage, consumer appeal, paint durability, or crash safety — or on testing one component of a multi-component product.  /  Or you could do an approximation experiment that has some characteristics (but not all) of an experiment that would be more realistic but less practical.  For example, an experiment might be run with a smaller scale-model of a large product, or a medical test might be done with lab animals instead of humans.

    Sometimes a visual organization of information is useful.  For example, the analytical grid below can summarize information (in the 20 yellow-green cells) about five products (in the top row) and four experiments (in the left column):


Product A

Product B

Product C

Product D

Product E

Experiment 1 (old)

Experiment 2 (old)

Experiment 3 (new)

Experiment 4 (new)

    Scanning horizontally across a row shows the information that is generated, in one type of experiment, about five different products.  Or you can scan vertically down a column, to see how the properties of one product are revealed in different experimental contexts.
    This grid shows products that are both old and new, and experiments that are old and new.  Each cell can contain observations (if an experiment has been done already) or predictions, or both.  You could think of it as a "product and experiments" grid, but — since the result of experiments is information about product-properties, and properties are your focus during evaluation — it is probably more useful to view it as a product-and-properties grid.
    For some products it is useful to make several grids, one for each design decision.  For a car, decisions would include the type of body, trunk, seats, doors, colors, engine, and transmission.  One grid could show possibilities for car bodies (with different shapes, sizes, materials,...) and experiments (ways to test each body for aerodynamic efficiency, consumer appeal, manufacturing cost,...) along with observations and/or predictions.  Other grids could show information about trunks, seats,...
    A grid is a useful way to summarize, in a clearly organized way, knowledge about products and experiments.  By scanning horizontally or vertically, you can focus your attention on a particular experiment or product.  By thinking critically and creatively about what you see in one scan — or in several (horizontal, vertical, or mixed) — you can search for patterns and principles, and for ways to improve a product by imagining (in 2B) how to revise an old option to make a new option.

    Or you may notice a knowledge gap when you ask, "Do the experiments provide satisfactory information about all important properties of every product-option, or is there something else that we want to know but don't know?", and this will inspire the design of new experiments (testing, in 2C) to generate the desired knowledge.
    Noticing a knowledge gap is an example of an action decision.  Each cell in a product-and-experiment grid can be filled with predictions or observations, or neither, or both.  For each cell, for each combination of product and experiment, you can decide what is the best use of your time:  Should you invest the time that is needed to make a quick-and-rough prediction?  to make a careful prediction?  to use a computer simulation for making a careful prediction?  to collect observations by running a simple small-scale experiment, or an elaborate large-scale experiment?
    One important function of mental experiments is to let you explore, quickly and cheaply, a wide variety of experimental possibilities.  One objective of this exploration is to search for tests that seem capable of providing useful information, that may be worth doing as physical experiments in a TESTING mode of action, in 2C.

a detailed review:  First, if you haven't done this already, it will be useful to see "the big picture" in A Brief Overview of Design Method.  When you compare the next three modes of action, you'll see that they are similar yet different:  In 2A, observations are done physically with OLD OPTIONS (that already exist);  in 2B, predictions are done mentally with NEW OPTIONS.  In both 2A and 2B, you do mental evaluations.  In 2A and 2C the process is similar but not identical;  the results are also similar, but in 2A you get OLD OBSERVATIONS (about OLD OPTIONS), while in 2C you get NEW OBSERVATIONS (about either OLD OPTIONS or NEW OPTIONS).  { At the end of 2C, there will be an overview-review. }

2C will be fairly short, and needs fixing; the first paragraph was in 2B, and the remainder will be revised.

    Goal-Oriented Testing of Products (Why?)
    Information generated in the mind (by prediction) is usually less reliable than information generated in the real world (by observation).  Therefore, it is often useful to "do a reality check" by converting a mental experiment into a physical experiment, to find out whether the "knowledge" being constructed in your mind is accurate and true, whether it corresponds to reality.

    2C. TEST (gather new information)
  The modes of TESTING (to produce new experimental information) and SEARCHING (to gather old information that was produced in the past) are similar.  But they always differ in timing, in whether observations are made in the past or present.  Another important difference is that in SEARCHING you can often take advantage of the experimental work done by others.  Here is the TESTING mode:

    As discussed above, the process of goal-oriented testing begins in the mind, with ideas for products and experiments.  The design of an experimental system (which I'm defining as "a product operating in an experimental context") is the active integration of two activities: generating ideas by selecting an old combination (of experiment and product) or inventing a new combination, and evaluating these ideas.  In this way, action in the IMAGINE mode can lead to decisions about action in the TEST mode.
    After you decide what to do, you must get your hands on a product and whatever else is needed to run the experiment.  If the product is old, if it already exists somewhere, you have the option of acquiring it (if possible) or constructing it (if necessary).  But if the product is truly new, it does not exist and cannot be acquired from other sources, so you must construct it by modifying an old product or building it from scratch.
    After you have assembled everything in the experimental system, you can do a physical experiment and collect observations about the product.  Then you can evaluate the product by comparing goals with observations to produce empirical feedback when you ask, "How well do the observed properties of this product match my goals for the product?"
    Or you can evaluate the experiment.  Based on a careful analysis of your observations, you may want to consider what you have done as a "pilot experiment" whose main function is to guide you toward the design of a modified experiment that is more sophisticated or is done on a larger scale, that will generate information which is more useful because it is more accurate, precise, or complete.  Or perhaps the original intention was for the experiment to be part of a series of experiments that become progressively more sophisticated.

a review:  You can make your own review by re-reading the overview preview for 2A-2B-2C and then scrolling through the sections, stopping to re-read (but now you're reading as a new person who knows more than you did in your previous reading) any parts you want.

Section 3 will be expanded by importing the "analytical TABLE" from 2B, which will have a modified and expanded discussion, to fit into the new section and link it with more ideas from different places.  Probably the section will have a short introduction, the table and discussion, then the current ending (solution, continue,...).

    3. EVALUATE (and decide)
  The previous three modes (REMEMBER, IMAGINE, and TEST) produce information to use in the EVALUATION mode:

     For each option (for each potential product, whether it is old or new, existing in the mind or in reality), designers use all available information (from all experiments, old and new, mental and physical) to evaluate this product by comparing goals with predictions (to produce predictive feedback) or comparing goals with observations (to produce empirical feedback) for the purpose of assigning an intrinsic status that is an estimate of the extent to which this option achieves the desired goals.  Usually there is a competition between different options, so a relative status (an estimate for the quality of a particular option with respect to other options) is also assigned.

    Often, the design of a new product is accompanied by strategies.  For example, making and selling a musical CD (a product) requires the coordinating of many strategies — for artistic research and development (writing and arranging songs, and deciding which ones to record), for rehearsing and performing, engineering and production, financing and manufacturing, marketing and distributing — to achieve objectives that are practical, artistic, and financial.

zb, one option may be do-able with manufacturing facilities you have now, but another option would require new --- and financing, hiring new people or laying off workers you already pay/hire, but might offer potential for bigger profits (or more good done), or time, different futures, strategies for business and for personal living for people who are making decision, or moving to another location, many interactions and possible consequences possible, and all (or at least major) should be considered, and being willing to accept uncertainties is often required when making decisions, especially those involving strategies; for example, choosing one product over another is a strategy for business, consider consequences for self and for others, much environmental ethics is about considering overall costs (whole other set of questions that won't be examined here)

use "analytical grid" here? yes

    Based on their estimates of status, designers can make several types of decisions.
    solution:  They may decide that one option is sufficiently satisfactory, so they consider the problem to be solved.  { But a "solution" decision often leads to a new phase of problem solving that is an extension of the old problem, such as deciding how to acquire or manufacture the product, or how to market, distribute, and sell it. }
    continue:  Or they may try to develop a product that is more satisfactory, with continuing "research & development" action in the SEARCH, IMAGINE, and TEST modes.  Perhaps the evaluation process has narrowed the focus of this R & D to revising a few key aspects of a few product-options that seem especially promising.  Or there may be an effort to search for new options, or to generate more information about existing options.
    abandon:  Maybe things are not going well and they abandon the search for a solution because progress has been slow, or because despite good progress they decide that working on another project is likely to be more productive.
    delay:  Or they can abandon the process of R & D temporarily, intending to return later, so they can work on other projects for awhile.  Or perhaps a delay will be productive without an investment of their own time and effort.  This could occur, for example, if at a later time they will be able to use information or technology that in the future will be available due to the work of others.
    settle:  Sometimes, however, an immediate decision is required even though none of the available options is totally satisfactory, so they "settle for less" and choose the best available option, if this is better than a delay.  Or perhaps, even if an immediate decision isn't absolutely necessary, a delay would cause significant disadvantages.   { When it seems wise to make a quick decision, even though evaluation indicates that only a conclusion of "inconclusive" is warranted, maybe they can make the decision in a way that takes into account the uncertainties. }
    re-aim:  Based on what they have learned during the process of R & D, they may decide to modify the goal-state.  Maybe their original goals now seem impractical and too difficult to achieve, so the standards for a satisfactory product are lowered and they are willing to settle for less.  Or the standards can be raised (or changed) if they have recognized or invented new possibilities for a product with characteristics that are better (or just different) than what they initially could imagine.  Or perhaps, based on revised estimates of relative importance, there is a change in the "weighting emphasis" for different types of goal-criteria, which leads to revised strategies for achieving an optimal balance between conflicting criteria.
    diversify:  But a change of plans isn't limited to only revising goals for the current project.  Designers can also generate new projects based on new Overall Objectives, for related "spinoffs" or for ideas unrelated to the current project.

    As discussed earlier, many results of evaluation involve not just the product, but also decisions about action, about "what to do next."  This important aspect of design is discussed later, in The Generation and Evaluation of Actions.

    In a step that is optional — that is not necessary when developing a product, but is often helpful — a designer evaluates a theory by comparing predictions with observations to produce hypothetico-deductive feedback.  This reality check is the key step in theory evaluation, in deciding whether "the way things are (according to theory-based predictions)" correspond to "the way things really are (as indicated by observations)."

     If what you think should happen (your predictions) is not the same as what actually happens (your observations) there is a mystery to be solved.  A mismatch could be due to a variety of causes, ranging from inaccurate observations to sloppy logic, and including the possibility that a faulty theory is being used to make predictions.  If a designer decides that a theory should be revised, this change can affect any future action in the IMAGINING mode, such as generating ideas for products, designing experiments, and doing mental experiments.



    At this time I've decided to work on other projects, so I'll just describe what eventually will be in this page.  There will be minor revisions (as described later) and six end-of-page sections, briefly outlined below:

    The Design of Strategies:  The same basic process is used for designing products and strategies (*).  In all current descriptions of IDM, the focus is restricted to products in order to increase the clarity and to decrease the need for awkward phrases like "product and/or strategy."  But most of the action that occurs when designing products also applies to the design of strategies, so you can just replace each reference to "product" with "strategy" and you'll have the design process for strategies.  But one major difference is a shift, when predicting or observing, from properties (of products) to consequences (of strategies).  Eventually, this section will examine the similarities and differences between the process of design for products and for strategies.  {* As described at the beginning of An Introduction to Design, strategies can be designed for a wide variety of situations, ranging from business to basketball, from romance to public policy. }  {back to Section 1B}

    Generating and Evaluating Actions:  A strategy can be the overall objective of design, as in the previous section.  But strategy planning also occurs within the process of design when — guided by feedback that enhances their awareness of differences between the current state and the goal state, and perhaps constrained by the limitations of deadlines and budgets — designers generate, evaluate, and execute actions that will help them make progress toward solving their problem.  Strategies for action begin during the planning for a design project.  Later, during the process of design, these preliminary plans can be modified by improvised planning.  { Currently, these ideas are much more fully developed in my model for Integrated Scientific Method, in the section about Problem-Solving Projects. }

    Modes of Action:  This will expand the earlier discussion (in Section 1A) about interactions between modes — with problem recognition requiring action in the modes of searching, imagining, evaluating, and defining goals — and will extend it to other interactions.

    Actions and Methods:  This section, which is especially important for education, will explore the relationships between thinking methods for problem solving (such as those summarized in IDM) and the thinking actions (such as various applications of creative and critical thinking) that are used during the process of problem solving.  We'll look at some actions, plus different perspectives on the relationships between methods and actions.  /  Here is a summary of the difference between a thinking process (which I'm calling a "method") and thinking skills: "What we call thinking skills are simpler cognitive operations such as observing, comparing, or inferring."  A thinking process "involves using a sequence of skills intended to achieve a particular outcome."  A process "orchestrates numerous skills" and is directed toward achieving an objective.  Compared with a skill, a process "is broader in scope, and takes a longer time to complete."  {from my page about Comparing "Thinking Skills" Frameworks which uses quotations from Dimensions of Thinking: A Framework for Curriculum and Instruction (1988) }

    Enriching IDM:  IDM can be supplemented with ideas from ISM (Integrated Scientific Method) — such as cultural-personal factors, thought styles, conceptual factors, and more — to produce an enriched model of IDM.  Even though IDM is simpler than ISM, design is not simpler than science, so enrichment is appropriate and useful.

    Also, there will be minor revisions in Section 3 — mainly by adding an "evaluation analysis" grid that has options on one axis, criteria [weighted for importance] on another axis, and evaluations in the cells) — and Section 4 (with miscellaneous comments), and in a few other places.

    Science and Design:  This will flow naturally from the THEORIZING mode.  And it will be an extension of a discussion that begins on the "Introduction to Design" page, which:  1) makes a distinction between the designing of products or strategies (which I'll call "design") and the designing of theories (which I'll call "science");  2) discusses some similarities and differences between engineering [as one type of design] and science;  3) suggests that it may be useful to consider one type of comparison (of predictions with observations) as being a useful form of feedback in science, while two other comparisons (of predictions with goals, or observations with goals) are useful for feedback in design, as shown below:






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