Summary of Concept Generation, Decision Making and Concept Selection 

   Concept generation is one of the steps in conceptual design. There are four major topics in conceptual design, the first one is define problem. In problem definition, the real problems still need to be analyzed. By doing problem statement, we listed the problems and proceed to another tool in determining the real problems. Benchmarking, product dissection, house of quality and product design specification (PDS) are another tool used in determining the problem that looking for. The problem we looking here are the problem created by costumer, “what customer want” for the betterment of product. The problem will help in development the product specification and design. Yet the design and parameter development product stage are too far ahead after the information generation stage. In the information generation stage, all the source of info regarding to the research question and research objective are gathered. All the sources for the previous study are being collected to answer the problem in the previous stages. In this first step of development product the sources that available are like internet sources, customer survey, patents feedback, previous articles and journals, sales records, and other. Introduction to creative thinking After the information generation or gathering information then concept generation being introduce. In this step innovative products are the result of not only remembering useful design concepts but also recognizing potential concepts that arise in other disciplines. An engineer will use creative thinking and design processes methods assisting in the generating of new concepts. In this generating design concept, practical methods like brainstorming for enhance creativity, are now adapted. Practical methods like brainstorming is used widely in the every field especially those that deal with problem solving. Critical enhancing methods are offered in workplace seminars, and recruiters of new talent are including creativity as a high-value characteristic in job applicants. This chapter will explain in short about how the human brain is able to perform creatively, and how successful problem. Such as engineering designer, creative thinking is very important in design product to identify concepts that will achieve particular functions required by a product. Thinking about one’s own thought process as applied to a particular task is called cognitive. The creative thinking study has been studied by cognitive scientists and psychologists. In general terms, cognition is the act of human thinking. Thinking is the execution of cognitive processes like the activities of collecting, organizing, finding, and using knowledge. Study of the use of knowledge by humans applies in their activities call as cognitive psychology. This person helping us to understand a person’s thinking because cognitive processes are naturally influence by an individual’s perceptions and representations of knowledge. Skills for developing creative thinking come from sciences that study human thinking, actions, and behavior. It was thought that creativity was unable to be taught, copied, or mimicked. Individual creativity was a kind of genius that was nurtured and developed in those with the natural gift. Although, the possessor of cognitive thinking are born with it but this can also be attain by applying some technique and some understand to individual body function. Advancement in technology and medicine has made the study of creative thinking become more realistic. Modern technology such as MRI and other neuroscience scanner can be used to observe the brain activities. This is the best ways in reveling and identifying which part of the brain that response to the certain action. These types of brain respond depend on types or level of mind stated that used while doing those activities. Sigmund Freud has developed a topographical model of the mind consisting of three levels: The advantages of the Freud’s model 1. Used to explain personality of one person. 2. Used to explain personality types and their behaviors based on own training, experience, and beliefs. 3. Used to help explain the process by which problems are solved in a creative fashion. 4. Used to judge or to know which mind level ones person used by observing the actions of its owner. 5. Used to judge the part of the brain and the level of consciousness that the brain working. The personality and the ability of one person also can be judge by knowing the relationships of the brain hemispheres. Herrmann’s instrument Invented by Herrmann’s, is a standardized test instrument, name as Herrmann Brain Dominance Instrument (HBDITM). It is similar work in nature to the Kolb Learning Style Inventory or the MBTI personality classification instruments. This instrument is used to understanding of the physiology of the brain that revealed connections between the two hemispheres of the brain and within the same hemisphere. In comparison between gender, women are the one that have more connection or bridges between these of two hemispheres. These make women to being able to act as multitasking person. Information Processing and Computational Modeling In term of informational processing and computational processing, human brains have humongous capabilities in storage. Even though limited in processing capacities but brain has a lot of “processor” or input device that work in each of the connection inside of the brain. Human brain can only be able to process seven or eight elements at one time. These types of elements are chunked together until the packed data are small enough to being visualize. As per say, human brain are more superior than human made brain such computer, workstation and other. Human brain is more complex and complicated in term of storage, and retrieval information. The ability to combine associate past perfect (memories), past continuing (experience) and present (thought) make it special. These combinations will lead to some creative idea. It is called associationism. Humans process visual, auditory, tactile, and emotional input, at nearly the same time, and can also perform output activities like speaking or writing while processing input. Thinking Processes that Lead to Creative Ideas Creativity is a characteristic of a person based on what the person does. Researchers have discovered that, processes to develop a creative idea are the same processes that are routinely used like normal thinking. So that, the successful of someone use of thought processes and existing knowledge to produce creative ideas creative cognition. The good news about this view of creativity is that these strategies for achieving creative thinking can be accomplished by deliberate use of particular techniques, methods, or in the case of computational tools, software programs. The study of creativity usually focuses on both the creator and the created object. The first step is to study people who are considered to be creative and to study the development of inventions that display creativity. The assumption is that studying the thinking processes of the creative people will lead to a set of steps or procedures that can improve the creativity of the output of anyone’s thinking. Similarly, studying the development of a creative artifact should reveal a key decision or defining moment that accounts for the outcome. This is a promising path if the processes used in each case have been adequately documented. Creativity and problem solving Creativity is needed in designing and problem solving. Thinker that possesses creative thinking has the ability to combine the ideas and concepts to form useful thing that can solved the problems. This ability are very important and needed in job, even some job put these characteristic as requirement. On the other hand, only a small number of people are blessing with a gifted to possess creative thinking, but that can be cultivated and enhanced with study and practice. Usually the creative person senses the total structure of the idea but with limited number of its details. There ensues a slow process of clarification and exploration as the entire idea takes shape. A characteristic of the creative process is that initially the idea is only imperfectly understood. Engineers, by nature and training, usually value order and explicit detail and abhor chaos and vague generality. Thus, we need to train ourselves to be sensitive and sympathetic to these aspects of the creative process. We need also to recognize that the flow of creative ideas cannot be turned on upon command. Therefore, we need to recognize the conditions and situations that are most conducive to creative thought. We must also recognize that creative ideas are elusive, and we need to be alert to capture and record our creative thoughts. Aids to Creative Thinking Aids to creative thinking is the step to ensure the positive outcomes in applying methods found to be useful for others. Creative thinking can be enhances by some positive steps. 6 Develop a creative attitude : To be creative it is essential to develop confidence that you can provide a creative solution to a problem. Although you may not visualize the complete path through to the final solution at the time you first tackle a problem, you must have self-confidence; you must believe that a solution will develop before you are finished. Of course, confidence comes with success, so start small and build your confidence up with small successes. Unlock your imagination : You must rekindle the vivid imagination you had as a child. One way to do so is to begin to question again. Scholars of the creative process have developed thought games that are designed to provide practice in unlocking your imagination and sharpening creative ability. Be persistent : We already have dispelled the myth that creativity occurs with a lightning strike. On the contrary, it often requires hard work. Most problems will not succumb to the first attack. They must be pursued with persistence. After all, Edison tested over 6000 materials before he discovered the species of bamboo that acted as a successful f lament for the incandescent light bulb. It was also Edison who made the famous comment, “Invention is 95 percent perspiration and 5 percent inspiration.” Develop an open mind : Having an open mind means being receptive to ideas from any and all sources. The solutions to problems are not the property of a particular discipline, nor is there any rule that solutions can come only from persons with college degrees. Ideally, problem solutions should not be concerned with company politics. Because of the NIH factor (not invented here), many creative ideas are not picked up and followed through. Suspend your judgment : We have seen that creative ideas develop slowly, but nothing inhibits the creative process more than critical judgment of an emerging idea. Engineers, by nature, tend toward critical attitudes, so special forbearance is required to avoid judgment at an early stage of conceptual design. Set problem boundaries : We place great emphasis on proper problem definition as a step toward problem solution. Establishing the boundaries of the problem is an essential part of problem definition. Experience shows that setting problem boundaries appropriately, not too tight or not too open, is critical to achieving a creative solution. Creative thinking process and problem solving can be described in terms of a simple four-stage model. Instruction for improving creativity is to fill the mind and imagination with the context of the problem and then relax and think of something else. As you work on the problem the preconscious will hand up into your conscious mind a picture of what the solution might be. This called as insight. Insight process occur when the mind has restructure a problem in such a way that the previous impediments to solutions are eliminated, and unfulfilled constraints. These communications is in term of images. Barriers to Creative Thinking Mental block is a mental wall that prevents the solver from correctly perceiving a problem or conceiving its solution. A mental block is an event that inhibits the successful use of normal cognitive processes to come to a solution. There are many different types of mental blocks. Creative Thinking Methods Creativity improvement methods are aimed at improving problem solver characteristics. The improvement steps of problem solver are. Sensitivity: The ability to recognize that a problem exists Fluency: The ability to produce a large number of alternative solutions to a problem Flexibility: The ability to develop a wide range of approaches to a problem Originality: The ability to produce original solutions to a problem Brainstorming Developed by Alex Osborn, brainstorming is the most common method to stimulate creative magazine advertisements, but it has been widely adopted in other areas such as design. The word brainstorming has come into general usage in the language to denote any kind of idea generation. It is orchestrated process where broad experience and knowledge of groups for individuals. The brainstorming process is to overcome many of the mental blocks by team members. Active participation of different individuals in the idea generation process overcomes most perceptual, intellectual, and cultural mental blocks. Success brainstorming session is a promising step of rapid, free-flowing ideas. To achieve a good brainstorming session, it is important to carefully define the problem at the start. Time is very important here. It is also necessary to allow a short period, quietly and supportive team before go through the problem and on their own before starting the group process. The evaluation of your ideas should be done at a meeting on a day soon after the brainstorming session. This removes any fear that criticism or evaluation is coming soon on another day. Evaluation can also on the day after the idea generation session allows incubation time for more ideas to generate and time for reflection what was proposed. The evaluation meeting should begin by adding to the original list any new ideas realized by the team members after the incubation period. Then the team evaluates each of the ideas. Hopefully, some of the wild ideas can be converted to realistic solutions. conceptual design is generate from brainstorming process. Rreverse brainstorming is used in approach possible limitations or shortcomings of the product. SCAMPER is a list of brainstorming proposed to help the brainstorming process for breaking up the normal thought pattern by using a checklist to help develop new ideas. Idea generating techniques beyond brainstorming This section presents simple methods that address other mental blocks to creativity. There are four methods for SCAMPER. 1. Six Key Questions 2. Five Whys 3. Checklists 4. Fantasy or Wishful Thinking Random input technique Edward de Bono is developer for this methods that see the patterns, by cutting across thought patterns. Think about a problem solution and need for a new idea. In order to force the brain to introduce a new thought, all you have to do is to introduce a new random word. The word can be found by turning at random to a page any book and at random selecting a word or word chooses is closely related to the problem. De Bono points out that this forced relationship from a random word works because the brain is a self-organizing patterning system that is very good at making connections even when the random word is very remote from the problem subject. the random input technique does not apply only to random words but works with objects or pictures. The principle is the willingness to look for unconventional inputs and use these to open up new lines of thinking. Synectics: An Inventive Method Based on Analogy Many problems are solved by analogy method where designer recognizes the similarity between the design under study and a previously solved problem to create solution either it is creative solution depends on the degree to which the analogy leads to a new and different design. This most likely will not be a creative design, and it may not even be a legal design, depending on the patent situation of the older product. Synectics came from the Greek word synektiktein, meaning joining together of different things into unified connection which is a methodology for creativity based on reasoning by analogy that was first described in the book by Gordon. He assumes psychological components of the creative processes are more important in generating new and inventive ideas than the intellectual processes. This notion is counterintuitive to engineering students, who are traditionally very well trained in the analysis aspects of design. To applied Synectics need a highly trained facilitator that proceeds in stages. The first stage of Synectics is to understand the problem until it become familiar. In the second phase searches for creative solutions bases on four types of analogies which are: 1. Direct analogy : searches for the closest physical analogy to the current situation. This is a common approach that we have all used at one time or another. The method takes the form of a similarity in physical behavior, similarity in geometrical configuration, or in function. Analogies are not necessarily the result of complex mental model restructuring of ideas if they are from the same domain. 2. Fantasy analogy : disregards all problem limitations and laws of nature, physics, or reason the designer imagines or wishes for the perfect solution to a problem. 3. Personal analogy : imagines that he or she is the device being designed, associating his or her body with the device. 4. Symbolic analogy : least intuitive because using symbolic analogy the designer replaces the specific of the problem with symbols and then uses manipulation of the symbols to discover solutions to the original problem. Concept map A very useful tool for the generation, association, recording and organizing information. Concept map is having close relation the mind map. A concept map is good for generating and visual method for ideas during brainstorming. The problem or issue is placed at the center of the sheet. Then the team is asked to think about what concepts, ideas, or factors are related to the problem. CREATIVE METHODS FOR DESIGN Creativity technique to a design task is to generate as many ideas as possible. Once there are a lot of for alternative designs exists, these alternatives can be reviewed more critically. the goal becomes sorting out infeasible ideas. Refinement and evaluation of ideas The objective of creative idea evaluation is not to winnow down the set of ideas into a single or very small number of solutions. The evaluation methods considered as primary purpose evaluation step in concept generation is the identification of creative, feasible, yet still practical ideas. Convergent thinking dominates this process. The type of thinking used in refining the set of creative ideas is more focused than the divergent type of thinking that was used in generating creative ideas. Here we use convergent thinking to clarify concepts and arrive at ideas that are physically realizable. A quick way to do this is to group the ideas into three categories based on the judgment of the team as to their feasibility. 1. feasible Ideas “What about this idea makes it not feasible?” 2. potential Ideas with research “What would have to change for this idea to become 3. unfeasible Ideas that have no chance feasible? ” Checking concept ideas for feasibility is a critical step in the design process. Time is a valuable and limited resource for team so they cannot spend on developing design solutions with a low probability of success. Time here play important roles, too late for elimination involved high cost and too early, the team have immature information for elimination. The more ambitious the design task, the more likely this is to be true. A valuable strategy used by successful teams is to document ideas and the rationale made for choosing to pursue them or not. An alternate strategy for classifying concepts is to group the ideas according to critical-to-quality engineering characteristics. The team discusses the concepts within the class objective of seeing how they can be combined or rearranged into more completely developed solutions. Team elaborate on ideas, force fit and combine ideas to create a new idea. First ideas are grouped into categories. concepts are synthesized by combining ideas from the different categories. Force filling results in further consolidation of the ideas. The overall objective is to come out of this session with several well developed design concepts. The above example is idealized. It uses only visual design elements to represent ideas, but mechanical design is more complex because functionality is the prime consideration in the generation of concepts. Normally this procedure will take two or three times as long as the first brainstorming session, but it is worth it. Generating design concepts Applying creative idea generation is an good way to proceed to a feasible design solution. However, in engineering generating a feasible conceptual design dificult compare to find one or two good concept ideas. This means that all of the creativity available to an engineer or designer is called on several times in the design process and is used to arrive at alternative concepts for a small portion of an overall design task. Thus, all the creativity-enhancing methods are valuable to engineering designers during the conceptual design process. Systematic methods reflect a common model of the design process consistent with the ultimate goal of the designer. The task of the designer is to find the best of all possible candidate solutions to a design task. Generative design is a design strategy that creates many feasible alternatives to a given product design specification (PDS). The altenative of the solution can be generated by discreate funtion with n spaces of concepts. Creative idea generation methods can help a design team to find designs in different areas of the space but are not as reliable as engineering design requires. There are a lot of design space that available and easier to be understand for exampel TRIZ. The key idea to remember in design is that it is benefi cial in almost every situation to develop a number of alternative designs that rely on different means to accomplish a desired behavior. Function Decomposition And Synthesis The decompositions are useful for understanding the design task and allocating resources to it. QFD’s House of Quality decomposes is on of the application that combining product engineering characteristics that contribute to customers’ perceptions of quality. There are other ways to decompose a product for ease of design. Second function of fecomposition is work as representational strategy common in early stages of concept generation. This stage of concept generation is declining strategy where the solution is search till the subfunction. This feature of the functional decomposition method is called solution-neutrality . Physical decomposition Physical decomposition means separating the product or subassembly directly into its subsidiary subassemblies and components and accurately describing how these parts work together to create the behavior of the product. That mean the product are being dismantle and classified according to the function and being list on the organizational chart or tree diagram. There are few step for appling of this stage. 

The step are: 

1. Define the physical system in total and draw it as the root block of a tree diagram. 

2. The decomposition diagram will be hierarchical. 

3. Identify and defi ne the first major subassembly of the system described by the root block and draw it as a new block below the root. 

4. Identify the physical connections between the subassembly represented by the newly drawn block and higher level blocks of the hierarchy in the decomposition diagram. The connection of the blocks are check for the misplace. 

5. Identify and draw in the physical connections between the subassembly and any other subassemblies on the same hierarchical level of the diagram’s structure. 

6. Examine subassembly block in the now complete level of the diagram. It can be decomposed into more than one distinct and significant component. If the block under examination cannot be decomposed in a meaningful way, move on to check the other blocks at the same level of the diagram hierarchy. 

7. End the process when there are no more blocks anywhere in the hierarchical diagram that can be physically decomposed in a meaningful way. Some parts of a product are secondary to its behavior. Those include fasteners, nameplate, bearings, and similar types. 

Axiomatic Design 

Introduction 

This method had been introduced by Nam P.Suh, a mechanical engineering professor at MIT. At the beginning he was to identify a method to make a design theory which would make it possible to answer such questions as: Is this a good design? Why is this design better than others? How many features of the design must satisfy the needs expressed by the customers? When is a candidate design complete? What can be done to improve a particular design? When is it appropriate to abandon a design idea or modify the concept? Professor Nam Suh and his colleagues at MIT have developed a basis for design that is focused around two design axioms. This section will introduce Suh’s axioms and how they are used to structure design creation and the improvement of existing designs. This Axiomatic Design can be run with a design process that involves design spaces to describe different steps in generating design concept. 

There are four elements that considered in this design. 

 • Consumer Attributes (CAs)—Variables that characterize the design in the consumer domain. CAs is the customer needs and wants that the completed design must fulfill. 

 • Functional Requirements (FRs)—Variables that characterize the design in the functional space. These are the variables that describe the intended behavior of the device. The FRs is much like the function block titles defined for functional decomposition. 

• Design Parameters (DPs)—Variables that describe the design in the physical solution space. DPs are the physical characteristics of a particular design that has been specified through the design process. 

 • Process Variables (PVs)—Variables that characterize the design in the process (manufacturing) domain. PVs are the variables of the processes that will result in the physical design described by the set of DPs. The Axioms An axiom is an accurate observation of the world but it is still not provable. An axiom must be a general truth for which no exceptions or counterexamples can be found. Axioms stand accepted, based on the weight of evidence, until other-wise shown to be faulty. Suh has proposed two conceptually simple design axioms in Axiomatic Design. Axiom 1 is named the independence axiom. It can be stated in a number of ways. 

 • An optimal design always maintains the independence of the functional requirements of the design. In an acceptable design the design parameters (DPs) and functional requirements 

 • (FRs) are related in such a way that a specific DP can be adjusted to satisfy its corresponding FR without affecting other functional requirements. Generating Concept Figure 1 Figure above is an example how this method used to generate a concept. Begin with mapping one set of variables to another set of variables. All of the designer specification will be surely fulfill the need of the customer and the concept will be widely generated. As the figure 1 above, the needs of the customer were arranged hierarchal order to the FR (functional requirement). And each function that needs to be expanded will be made the third level of the map. Strength and Weakness The strengths are rooted in the mathematical representation chosen by Suh. They are, in brief: • Mathematically based — Axiomatic Design is built with a mathematical model of axioms, theories, and corollaries. This meets the need of the design theory and methodology community to incorporate rigor in the field.

 • Vehicle to relate FRs and DPs—The representation of designs using FRs, DPs, and the design matrix [ A ] opens up their interpretation in mathematical ways more common to students of linear algebra. 

 • Powerful if the relationship is linear—the design matrix [A] is a powerful conceptual tool and is also a reminder that there may be some relationships of FRs and DPs that are understood to the point of mathematical expression. If others aren’t, it’s still a goal. 

 • Provides a procedure for decomposing decision process—reviewing the design matrix [A] can reveal natural partitions in the setting of FRs that will aid in ordering the efforts of the design team. 

 • Basis for comparing alternative designs—Axiomatic Design provides a metric (degree of independence of functional requirements) that can be used to differentiate between competing design concepts. Weaknesses of Axiomatic Design lie first in the fact that the axioms must be true in order to accept the methodology. There is no proof that the independence axiom is false, but there are examples of designs that are strongly coupled and are still good designs in the eyes of the user community. Other weaknesses are as follows: 

 • The design method describes a way to create new designs from FR trees to DPs. Yet the methodology is not prescribed as others (e.g., systematic design). This can lead to a problem with repeatability. 

 • Designs are usually coupled with this echoes some concern for the strength of Axiom 1 and also means that it will be difficult to decouple existing designs to create improvements. 

 • Axiom 2: Minimize Information Content is difficult to understand and apply. There are many approaches to interpreting Axiom 2. Some designers use it to mean complexity of parts, others use it to mean reliability of parts, still others have considered it to refer to the ability to maintain the tolerances on parts. Axiom 2 has not been used by the design community as much as Axiom 1, leading to questions about its usefulness or about the axiomatic approach in general. Decision making Decision-making is one of the defining characteristics of leadership. It’s core to the job description. Making decisions is what managers and leaders are paid to do. Yet, there isn’t a day that goes by that you don’t read something in the news or the business press that makes you wonder, “What were they thinking?” or “Who actually made that decision?” That’s probably always been the case, but it seems exponentially more so in the opening decade of the new millennium where everything seems marked with, “too big, too fast, too much, and too soon.” The reality seems to be that most organizations aren’t overrun by good decision makers, yet alone great ones. When asked, people don’t easily point to what they regard as great decisions. Stories of bad decisions and bad decision-making come much more readily to mind. Some of that is due to our tendency to notice and recall exceptions vs. all the times things go as planned. For example, you’ve walked along side buildings more times than you could possibly count. Yet you remember vividly the one time you got nailed by a pigeon overhead. That’s how we are about bad decisions. We’re also that way because the really bad ones tend to really hurt. It’s not that people don’t have the capacity to make high-quality decisions in them. Decision-making is a distinctly human activity. It’s what that great, big frontal lobe is for. We all make decisions all the time. But the fact that we’re hard-wired to make decisions doesn’t by itself make us good decision-makers. That takes discipline: discipline to do at least four things all the time and well. 1. Realize when and why you need to make a decision. 2. Declare the decision: decide what the decision is, how you’ll work it, and who should be involved. 3. Work the decision: generate a complete set of alternatives, gather the information you need to understand the possibilities and probabilities, and ultimately make a choice that best fits your values. 4. Commit resources and act. Not everyone does those four things consistently or consistently well. We’ve worked with a lot of leaders and managers in some of the most widely regarded companies in the world and our observation is that most people don’t. In fact, the distribution generally looks something like this: • There are some really wretched decision makers. For them, a good outcome is usually a matter of luck. • There are a lot of people who are reasonably competent decision makers. Their decision processes aren’t great, but they’re not bad, and the outcomes they experience track accordingly. • There is a small group of people who could be described as “good decision makers” These people are proactive and decision oriented. They’re able to focus attention on what’s important and critical. They know how to break a decision down into logical parts. They know how to work each of those parts in a high quality way. They know how to deal with possibilities and probabilities. They’re able to see opportunities where others see problems. They’re able to make quality choices in the face of uncertainty. They’re able to turn thought into action. • There is a sprinkling of people we’d describe as great decision makers. Like other good decision makers, these people consistently make high quality decisions. Their “greatness”, a word that is probably way overused, comes from their ability to create the dynamics needed to ensure that the people in their organizations can do the same. Good and great decision makers expect high quality outcomes and they’re generally not disappointed. When they are, it’s usually because of some random thunderbolt or some unforeseen dynamic, not because they didn’t do a good job of working the problem. There are exceptions to this syllogism. But over the long-term, we think the good decision/good outcome connection holds up, and the outliers have either not been in the job long enough for their bad decisions to catch up, or have been extraordinarily lucky. Behavioural decision making theory Behavioural theories of choice seek both to explain past choices as a function of changesin exogenous variables, and given data on past choice behaviour, together with aprobability model of the data generating process, to predict future choices. It is in this sense that affective decision-making, ADM, is a behavioural theory of choice. A property shared by consumer demand analysis – see part one in Deaton and Muellbauer (1980), but not evident in other strategic models of choice behaviour such as Gul—Pessendorfer (2001), Bernheim—Rangel (2004) or Fudenberg—Levine (2006). ADM is a game-theoretic model of individual decision-making under risk anduncertainty, which generalizes expected utility, and where the probability weights –perceived risk – are endogenous, as implied by optimism bias (Slovic 2000,Weinstein1980). In our model of individual decision-making there are two distinct psychologicalprocesses that mutually determine choice. This approach is inspired in part byKahneman (2003), who proposes two systems of reasoning that differ in several importantaspects, such as emotion. We call these systems of reasoning the rationalprocess and the emotional process. The rational process coincides with the expectedutility model. That is, for a given risk perception, i.e., perceived probability distribution,it maximizes expected utility. The emotional process is where risk perceptionis formed. In particular, the agent selects an optimal risk perception to balance twocontradictory impulses: (1) affective motivation and (2) a taste for accuracy. Thisis a definition of motivated reasoning, a psychological mechanism where emotionalgoals motivate agent’s beliefs, e.g., Kunda (1990), and is a source of psychologicalbiases, such as optimism bias. Affective motivation is the desire to hold a favourablepersonal risk perception – optimism – and is captured by the expected utility term. The desire for accuracy is the mental cost incurred by the agent for holding beliefs other than her base rate, given her desire for favourable risk beliefs. The base rate is the belief that minimizes the mental cost function of the emotional process. This is the agent’s correct risk belief, if her risks are objective such as mortality tables. As an application of affective decision-making, we present an example of thedemandfor insurance in a world with two states of nature: Bad and Good. The relevantprobability distribution in insurance markets is personal risk, hence the demand forinsurance may depend on optimism bias. Affective choice in insurance markets isdefined as the insurance level and risk perception which constitute a pure strategyNash Equilibrium of the ADM intrapersonal game. The systematic departure of the ADM model from the expected utility model allows for both optimism and pessimism in choosing the level of insurance, and shows,consistent with consumer research (Keller and Block 1996), that campaigns intendedto educate consumers on the loss size in the bad state can have the unintendedconsequence that consumers purchase less, rather than more,insurance. Hence, theADM model suggests that the failure of the expected utility model to explain somedata sets may be due to systematic affective biases. A decision is made on the basis of available facts. Great effort should be made to evaluate possible bias and relevance of the facts. It is important to ask the right questions to pinpoint the problem. Emphasis should be on prevention of arriving at the right answer to the wrong question. When you are getting facts from subordinates, it is important to guard against the selective screening out of unfavorable results. The status barrier between a superior and a subordinate can limit communication and transmission of facts. The subordinate fears disapproval and the superior is worried about of prestige. Remember that the same set of facts may be open to more than one interpretation. Of course, the interpretation of quality experts should be respected, blind faith in expert opinion can lead to trouble. Facts must be carefully weighed in an attempt to extract the real meaning: knowledge. In the absence of real knowledge, we must seek advice. It is good practice to check your opinions against the counsel of experienced associates. That should not be interpreted as a sign of weakness. Remember, however, that even though you do make wise use of associates, you cannot escape accountability for the results of your decisions. You cannot blame failures on bad advice; for the right to seek advice includes right to accept or reject it. Many people may contribute to a decision, but the decision maker bears the ultimate responsibility for its outcome. Also, advice must be sought properly if it is to be good advice. Avoid putting the adviser on the spot; make clear that you accept full responsibility for the final decision. To summarize this discussion of the behavioral aspects of decision making, we list the sequence of steps that are taken in making a good decision.  The objectives of a decision must be established.  The objectives are classified as to importance. (Sort out the musts and the wants.)  Alternative actions are developed.  The alternatives are evaluated against the objectives.  The choice of the alternative that holds the best promise of achieving all of the objectives represents the tentative decision.  The tentative decision is explored for future possible adverse consequences.  The effects of the final decision are controlled by taking other actions to prevent possible adverse consequences from becoming problems and by making sure that the actions decided on are carried out. Decision theory An important area of activity within the broad discipline of operations research has been the development of a mathematically based theory of decisions. Decision theoryis based on utility theory, which develops values, and probability theory, which assesses our stage of knowledge. Decision theory was first applied to business management situations and has now become an active area for research in engineering design. Normative and descriptive decision theory Most of decision theory is normative or prescriptive, i.e., it is concerned with identifying the best decision to take (in practice, there are situations in which "best" is not necessarily the maximal, optimum may also include values in addition to maximum, but within a specific or approximative range), assuming an ideal decision maker who is fully informed, able to compute with perfect accuracy, and fully rational. The practical application of this prescriptive approach (how people ought to make decisions) is called decision analysis, and aimed at finding tools, methodologies and software to help people make better decisions. The most systematic and comprehensive software tools developed in this way are called decision support systems. Since people usually do not behave in ways consistent with axiomatic rules, often their own, leading to violations of optimality, there is a related area of study, called a positive or descriptive discipline, attempting to describe what people will actually do. Since the normative, optimal decision often creates hypotheses for testing against actual behaviour, the two fields are closely linked. Furthermore it is possible to relax the assumptions of perfect information, rationality and so forth in various ways, and produce a series of different prescriptions or predictions about behaviour, allowing for further tests of the kind of decision-making that occurs in practice. In recent decades, there has been increasing interest in what is sometimes called 'behavioral decision theory' and this has contributed to a re-evaluation of what rational decision-making requires (see for instance Anand, 1993). Definition of a decision problem. This area represents the heart of decision theory. The procedure now referred to as expected value was known from the 17th century. Blaise Pascal invoked it in his famous wager (see below), which is contained in his Pensées, published in 1670. The idea of expected value is that, when faced with a number of actions, each of which could give rise to more than one possible outcome with different probabilities, the rational procedure is to identify all possible outcomes, determine their values (positive or negative) and the probabilities that will result from each course of action, and multiply the two to give an expected value. The action to be chosen should be the one that gives rise to the highest total expected value. In 1738, Daniel Bernoulli published an influential paper entitled Exposition of a New Theory on the Measurement of Risk, in which he uses the St. Petersburg paradox to show that expected value theory must be normatively wrong. He also gives an example in which a Dutch merchant is trying to decide whether to insure a cargo being sent from Amsterdam to St Petersburg in winter, when it is known that there is a 5% chance that the ship and cargo will be lost. In his solution, he defines a utility function and computes expected utility rather than expected financial value (see[1] for a review). In the 20th century, interest was reignited by Abraham Wald's 1939 paper[2] pointing out that the two central procedures of sampling–distribution based statistical-theory, namely hypothesis testing and parameter estimation, are special cases of the general decision problem. Wald's paper renewed and synthesized many concepts of statistical theory, including loss functions, risk functions, admissible decision rules, antecedent distributions, Bayesian procedures, and minimax procedures. The phrase "decision theory" itself was used in 1950 by E. L. Lehmann. The revival of subjective probability theory, from the work of Frank Ramsey, Bruno de Finetti, Leonard Savage and others, extended the scope of expected utility theory to situations where subjective probabilities can be used. At this time, von Neumann's theory of expected utility proved that expected utility maximization followed from basic postulates about rational behavior. The work of Maurice Allais and Daniel Ellsberg showed that human behavior has systematic and sometimes important departures from expected-utility maximization. The prospect theory of Daniel Kahneman and Amos Tversky renewed the empirical study of economic behavior with less emphasis on rationality presuppositions. Kahneman and Tversky found three regularities — in actual human decision-making, "losses loom larger than gains"; persons focus more on changes in their utility–states than they focus on absolute utilities; and the estimation of subjective probabilities is severely biased by anchoring. Castagnoli and LiCalzi (1996),[citation needed] Bordley and LiCalzi (2000)[citation needed] recently showed that maximizing expected utility is mathematically equivalent to maximizing the probability that the uncertain consequences of a decision are preferable to an uncertain benchmark (e.g., the probability that a mutual fund strategy outperforms the S&P 500 or that a firm outperforms the uncertain future performance of a major competitor.). This reinterpretation relates to psychological work suggesting that individuals have fuzzy aspiration levels (Lopes & Oden),[citation needed] which may vary from choice context to choice context. Hence it shifts the focus from utility to the individual's uncertain reference point. Pascal's Wager is a classic example of a choice under uncertainty. It is possible that the reward for belief is infinite (i.e. if God exists and is the sort of God worshiped by evangelical Christians). However, it is also possible that the reward for non-belief is infinite (i.e. if a capricious God exists that rewards us for not believing in God). Therefore, either believing in God or not believing in God, when you include these results, lead to infinite rewards and so we have no decision-theoretic reason to prefer one to the other[citation needed]. (There are several criticisms of the argument.) Intertemporal choice This area is concerned with the kind of choice where different actions lead to outcomes that are realised at different points in time. If someone received a windfall of several thousand dollars, they could spend it on an expensive holiday, giving them immediate pleasure, or they could invest it in a pension scheme, giving them an income at some time in the future. What is the optimal thing to do? The answer depends partly on factors such as the expected rates of interest and inflation, the person's life expectancy, and their confidence in the pensions industry. However even with all those factors taken into account, human behavior again deviates greatly from the predictions of prescriptive decision theory, leading to alternative models in which, for example, objective interest rates are replaced by subjective discount rates. Competing decision makers Some decisions are difficult because of the need to take into account how other people in the situation will respond to the decision that is taken. The analysis of such social decisions is more often treated under the label of game theory, rather than decision theory, though it involves the same mathematical methods. From the standpoint of game theory most of the problems treated in decision theory are one-player games (or the one player is viewed as playing against an impersonal background situation). In the emerging socio-cognitive engineering, the research is especially focused on the different types of distributed decision-making in human organizations, in normal and abnormal/emergency/crisis situations. Signal detection theory is based on decision theory. Complex decisions Other areas of decision theory are concerned with decisions that are difficult simply because of their complexity, or the complexity of the organization that has to make them. In such cases the issue is not the deviation between real and optimal behaviour, but the difficulty of determining the optimal behaviour in the first place. The Club of Rome, for example, developed a model of economic growth and resource usage that helps politicians make real-life decisions in complex situation Evaluation Method Comparison Based on Absolute Criteria The evaluation of design concepts implies and involves both comparison and decision making. Evaluation techniques require a comparison between the concepts developed and the requirements they must meet along with decisions regarding how well they meet those requirements. It is recommended that you follow a procedural approach to evaluation in order to better determine which concepts will best meet your design goals. The concept evaluation stage represents the convergence stage of design development so we will start by evaluating the concepts developed for the lowest level of function decomposition. As we progress, we will begin to combine the best concepts into subsystems and then to evaluate the sub-systems using the same procedures. In this fashion, we will converge upon our “best” design. Feasibility Judgment The first step in the judgment of feasibility is to eliminate those concepts that are deemed “not feasible” under any conditions. Many times these judgments are based upon “gut feel”, however as trained engineers, our “gut feel” is usually rooted in technological knowledge. The not feasible concepts are not considered further but still remain recorded in your design notebooks as reference. Sometimes a concept is deemed as “conditionally feasible”. This occurs when it is determined that a concept is workable if something else happens. This “something else” may involve the obtaining of currently unavailable information or the development of some other component. Conditionally feasible concepts will require further determination. They may fail later evaluations such as technological readiness or more information may be learned which will determine their fate. The hardest concepts to evaluate are those where it is not immediately evident whether the idea is good or not, but the concept is “worth considering”. These ideas (along with the conditionally feasible) will be evaluated through the decision making techniques given below. Technology-readiness assessment The second major evaluation should be to determine the readiness of the technologies that may be used in the concept. These technologies can include but are not restricted to, materials, manufacturing techniques, theoretical principals. Examine each concept with regard to the following questions. While a single “no” response is not enough to exclude a concept, it does mean that the concept may require re-examination. Do reliable and reasonable manufacturing processes exist? Do appropriate material choices for the solution exist and are they readily available? While you may not be making material choices yet, be careful of counting on using a specific exotic or difficult to fabricate material. Does my team have sufficient technological expertise for the solution considered? Does the solution make use of mature and developed technologies? Do similar applications exist that demonstrate the technology’s readiness? Go/no-go screening This is a relatively easy procedure to implement. The first step is to return to the set of customer requirements developed during the early stages of design development. Transform each of the customer requirements into a yes/no question. For example, “is this concept light weight?” Apply the question to each of the surviving concepts. Answer each question as yes, no, or maybe. If the answers are “yes” or “maybe”, then the concept is a “Go”, and it proceeds to the next stage of evaluation; if the answer is “no”, then the concept is a “No-Go”. Before discarding the no-go concepts, make the following determination; can NoGo concepts be modified for a Go? If so, then the modified concepts are advanced to the next step. Pugh Concept Selection Method Pugh Concept Selection is a quantitative technique used to rank the multidimensional options of an option set. It is frequently used in engineering for making design decisions but can also be used to rank investment options, vendor options, product options or any other set of multidimensional entities. A basic decision matrix consists of establishing a set of criteria upon which the potential options can be decomposed, scored, and summed to gain a total score which can then be ranked. Importantly, the criteria are not weighted to allow a quick selection process. The advantage of this approach to decision making is that subjective opinions about one alternative versus another can be made more objective. Another advantage of this method is that sensitivity studies can be performed. An example of this might be to see how much your opinion would have to change in order for a lower ranked alternative to out rank a competing alternative. The first seven steps for using Pugh’s method during concept selection are: 1. Choose the criteria by which the concepts will be evaluated. These criteria should include the customer needs and other design requirements identified during Problem Definition. If the initial list exceeds 15 criteria, the least important criteria should be removed to simplify the evaluation. 2. Formulate the decision matrix. The criteria fill the row headings of the matrix, and the concepts fill the column headings. Refer to the self-cleaning blender example below for additional illustration. 3. Clarify the design concepts. The goal of this step is to make sure all team members understand each concept equally well. This step helps avoid conflict and can lead to new and improved concepts. 4. Choose the initial datum concept. The datum is the concept with which all other concepts are compared. It is important to choose one of the better concepts as the datum. Selecting a poor datum will cause all of the alternative concepts to look good and will unnecessarily delay decision-making. 5. Run the matrix. During this step, each concept is compared with the datum for each criterion. Typically, a three-level scale is used: better (+), worse (-), or about the same (S). Sometimes, much better (++) and much worse (--) are also used. Refer to the self-cleaning blender example below for additional illustration. 6. Evaluate the ratings. Once the comparison matrix is completed, determine the sum of the +, -, and S ratings for each concept. Do not allow quantification to unnecessarily constrain the evaluation. If two or more top concepts are close, the advantages and disadvantages of each should be discussed. Also, be careful about rejecting concepts with highly negative scores. The few positive elements from these concepts can often be used to improve other concepts. 7. Establish a new datum and rerun the matrix. The new datum is usually the concept that received the highest rating from the first round. Using a new datum to rerun the matrix provides a different perspective for each comparison that will help clarify the relative strengths and weaknesses of each concept. Ideally, the new datum will prove to be the clearly superior concept. However, in some cases there will be no clear winner. Regardless, the goal of the Pugh method is to produce the strongest concept or set of concepts for further development.