THINKING ABOUT THINKING: Learning How to Learn, by drimhotep

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PDF generated using the open source mwlib toolkit. See for more information.PDF generated at: Wed, 18 Dec 2013 02:08:09 UTCTHINKING ABOUTTHINKING:Learning How to LearnCompiled by Marc Imhotep Cray, M.D. [12-13-V1]For Imhotep Virtual Medical SchoolContentsArticlesMetacognition 1Deductive reasoning 8Inductive reasoning 11Problem solving 16Decision-making 25Logical reasoning 33ReferencesArticle Sources and Contributors 34Image Sources, Licenses and Contributors 36Article LicensesLicense 37Metacognition 1MetacognitionMetacognition is defined as "cognition about cognition", or "knowing about knowing".[1] It can take many forms; itincludes knowledge about when and how to use particular strategies for learning or for problem solving. There aregenerally two components of metacognition: knowledge about cognition, and regulation of cognition.Metamemory, defined as knowing about memory and mnemonic strategies, is an especially important form ofmetacognition.[2] Differences in metacognitive processing across cultures have not been widely studied, but couldprovide better outcomes in cross-cultural learning between teachers and students.[3]Some evolutionary psychologists hypothesize that metacognition is used as a survival tool, which would makemetacognition the same across cultures. Writings on metacognition can be traced back at least as far as De Animaand the Parva Naturalia of the Greek philosopher Aristotle.[4]DefinitionsJ. H. Flavell first used the word "metacognition".[5] He describes it in these words:Metacognition refers to ones knowledge concerning one's own cognitive processes and products or anythingrelated to them, e.g., the learning-relevant properties of information or data. For example, I am engaging inmetacognition if I notice that I am having more trouble learning A than B; [or] if it strikes me that I shoulddouble check C before accepting it as fact.J. H. Flavell (1976, p. 232).A. Demetriou, in his theory, one of the neo-Piagetian theories of cognitive development, used the termhypercognition to refer to self-monitoring, self-representation, and self-regulation processes, which are regarded asintegral components of the human mind.[6] Moreover, with his colleagues, he showed that these processes participatein general intelligence, together with processing efficiency and reasoning, which have traditionally been consideredto compose fluid intelligence.Metacognition also thinks about one's own thinking process such as study skills, memory capabilities, and the abilityto monitor learning. This concept needs to be explicitly taught along with content instruction. Metacognitiveknowledge is about our own cognitive processes and our understanding of how to regulate those processes tomaximize learning. Some types of metacognitive knowledge would include: 1. Person knowledge (declarativeknowledge) which is understanding one's own capabilities. 2. Task knowledge (procedural knowledge) which is howone perceives the difficulty of a task which is the content, length, and the type of assignment. 3. Strategic knowledge(conditional knowledge) which is one's own capability for using strategies to learn information. Young children arenot particularly good at this; it is not until upper elementary where students start to develop the understanding ofstrategies that will be effective.Different fields define metacognition very differently. Metacognition variously refers to the study ofmemory-monitoring and self-regulation, meta-reasoning, consciousness/awareness andauto-consciousness/self-awareness. In practice these capacities are used to regulate one's own cognition, to maximizeone's potential to think, learn and to the evaluation of proper ethical/moral rules.In the domain of experimental psychology, an influential distinction in metacognition (proposed by T. O. Nelson &L. Narens) is between Monitoringmaking judgments about the strength of one's memoriesand Controlusingthose judgments to guide behavior (in particular, to guide study choices). Dunlosky, Serra, and Baker (2007) coveredthis distinction in a review of metamemory research that focused on how findings from this domain can be applied toother areas of applied research.In the domain of cognitive neuroscience, metacognitive monitoring and control has been viewed as a function of the prefrontal cortex, which receives (monitors) sensory signals from other cortical regions and through feedback loopsMetacognition 2implements control (see chapters by Schwartz & Bacon and Shimamura, in Dunlosky & Bjork, 2008).Metacognition is studied in the domain of artificial intelligence and modelling. Therefore, it is the domain of interestof emergent systemics. It has been used, albeit off the original definition, to describe one's own knowledge that wewill die. Writers in the 1990s involved with the musical "grunge" scene often used the term to describeself-awareness of mortality.[citation needed]ComponentsMetacognition is classified into three components:1. Metacognitive knowledge (also called metacognitive awareness) is what individuals know about themselves andothers as cognitive processors.2. Metacognitive regulation is the regulation of cognition and learning experiences through a set of activities thathelp people control their learning.3. Metacognitive experiences are those experiences that have something to do with the current, on-going cognitiveendeavor.Metacognition refers to a level of thinking that involves active control over the process of thinking that is used inlearning situations. Planning the way to approach a learning task, monitoring comprehension, and evaluating theprogress towards the completion of a task: these are skills that are metacognitive in their nature.Metacognition includes at least three different types of metacognitive awareness when considering metacognitiveknowledge:1. Declarative Knowledge: refers to knowledge about oneself as a learner and about what factors can influenceone's performance. Declarative knowledge can also be referred to as "world knowledge".2. Procedural Knowledge: refers to knowledge about doing things. This type of knowledge is displayed asheuristics and strategies. A high degree of procedural knowledge can allow individuals to perform tasks moreautomatically. This is achieved through a large variety of strategies that can be accessed more efficiently.3. Conditional knowledge: refers to knowing when and why to use declarative and procedural knowledge. It allowsstudents to allocate their resources when using strategies. This in turn allows the strategies to become moreeffective.Similar to metacognitive knowledge, metacognitive regulation or "regulation of cognition" contains three skills thatare essential.1. Planning: refers to the appropriate selection of strategies and the correct allocation of resources that affect taskperformance.2. Monitoring: refers to one's awareness of comprehension and task performance3. Evaluating: refers to appraising the final product of a task and the efficiency at which the task was performed.This can include re-evaluating strategies that were used.Similarly, maintaining motivation to see a task to completion is also a metacognitive skill. The ability to becomeaware of distracting stimuli both internal and external and sustain effort over time also involves metacognitive orexecutive functions. The theory that metacognition has a critical role to play in successful learning means it isimportant that it be demonstrated by both students and teachers.Students who demonstrate a wide range of metacognitive skills perform better on exams and complete work moreefficiently. They are self-regulated learners who utilize the "right tool for the job" and modify learning strategies andskills based on their awareness of effectiveness. Individuals with a high level of metacognitive knowledge and skillidentify blocks to learning as early as possible and change "tools" or strategies to ensure goal attainment. Swanson(1990) found that metacognitive knowledge can compensate for IQ and lack of prior knowledge when comparingfifth and sixth grade students' problem solving. Students with a high-metacognition were reported to have used fewerstrategies, but solved problems more effectively than low-metacognition students, regardless of IQ or priorMetacognition 3knowledge.Metacognologists are aware of their own strengths and weaknesses, the nature of the task at hand, and available"tools" or skills. A broader repertoire of "tools" also assists in goal attainment. When "tools" are general, generic,and context independent, they are more likely to be useful in different types of learning situations.Another distinction in metacognition is executive management and strategic knowledge. Executive managementprocesses involve planning, monitoring, evaluating and revising one's own thinking processes and products. Strategicknowledge involves knowing what (factual or declarative knowledge), knowing when and why (conditional orcontextual knowledge) and knowing how (procedural or methodological knowledge). Both executive managementand strategic knowledge metacognition are needed to self-regulate one's own thinking and learning.[7]Finally, there is no distinction between domain-general and domain-specific metacognitive skills. This means thatmetacognitive skills are domain-general in nature and there are no specific skills for certain subject areas. Themetacognitive skills that are used to review an essay are the same as those that are used to verify an answer to a mathquestion.Metacognitive experience is responsible for creating an identity that matters to an individual. The creation of theidentity with meta-cognitive experience is linked to the identity-based motivation (IBM) model. The identity-basedmotivation model implies that "identities matter because they provide a basis for meaning making and for action."[8]A person decides also if the identity matters in two ways with meta-cognitive experience. First, a current or possibleidentity is either "part of the self and so worth pursuing"[9] or the individual thinks that the identity is part of theirself, yet it is conflicting with more important identities and the individual will decide if the identity is or is not worthpursuing. Second, it also helps an individual decide if an identity should be pursued or abandoned.Usually, abandoning identity has been linked to meta-cognitive difficulty. Based on the identity-based motivationmodel there are naive theories describing difficulty as a way to continue to pursue an identity. The incrementaltheory of ability states that if "effort matters then difficulty is likely to be interpreted as meaning that more effort isneeded."[10] Here is an example, a woman who loves to play clarinet has come upon a hard piece. She knows thathow much effort she puts into learning this piece is beneficial. The piece had difficulty so she knew the effort wasneeded. The identity the woman wants to pursue is to be a good clarinet player, having a metacognitive experiencedifficulty pushed her to learn the difficult piece to continue to identify with her identity. The entity theory of abilityrepresents the opposite. This theory states that if "effort does not matter then difficulty is likely to be interpreted asmeaning that ability is lacking so effort should be suspended." Based on the example of the woman playing theclarinet, if she did not want to identify herself as a good clarinet player, she would not have put in any effort to learnthe difficult piece which is an example of using metacognitive experience difficulty to abandon an identity.[11]Relation to sapienceMetacognologists believe that the ability to consciously think about thinking is unique to sapient species and indeedis one of the definitions of sapience.[citation needed] There is evidence that rhesus monkeys and apes can make accuratejudgments about the strengths of their memories of fact and monitor their own uncertainty, while attempts todemonstrate metacognition in birds have been inconclusive.[12] A 2007 study has provided some evidence formetacognition in rats,[13] but further analysis suggested that they may have been following simple operantconditioning principles, or a behavioral economic model.Metacognition 4Metacognitive strategiesMetacognitive-like processes are especially ubiquitous when it comes to the discussion of self-regulated learning.Being engaged in metacognition is a salient feature of good self-regulated learners[citation needed]. Groups reinforcingcollective discussion of metacognition is a salient feature of self-critical and self-regulating social groups[citationneeded]. The activities of strategy selection and application include those concerned with an ongoing attempt to plan,check, monitor, select, revise, evaluate, etc.Metacognition is 'stable' in that learners' initial decisions derive from the pertinent fact about their cognition throughyears of learning experience. Simultaneously, it is also 'situated' in the sense that it depends on learners' familiaritywith the task, motivation, emotion, and so forth. Individuals need to regulate their thoughts about the strategy theyare using and adjust it based on the situation to which the strategy is being applied. At a professional level, this hasled to emphasis on the development of reflective practice, particularly in the education and health-care professions.Recently, the notion has been applied to the study of second language learners in the field of TESOL and appliedlinguistics in general (e.g., Wenden, 1987; Zhang, 2001, 2010). This new development has been much related toFlavell (1979), where the notion of metacognition is elaborated within a tripartite theoretical framework. Learnermetacognition is defined and investigated by examining their person knowledge, task knowledge and strategyknowledge.Wenden (1991) has proposed and used this framework and Zhang (2001) has adopted this approach and investigatedsecond language learners' metacognition or metacognitive knowledge. In addition to exploring the relationshipsbetween learner metacognition and performance, researchers are also interested in the effects ofmetacognitively-oriented strategic instruction on reading comprehension (e.g., Garner, 1994, in first languagecontexts, and Chamot, 2005; Zhang, 2010). The efforts are aimed at developing learner autonomy, interdependenceand self-regulation.Metacognition helps people to perform many cognitive tasks more effectively. Strategies for promotingmetacognition include self-questioning (e.g. "What do I already know about this topic? How have I solved problemslike this before?"), thinking aloud while performing a task, and making graphic representations (e.g. concept maps,flow charts, semantic webs) of one's thoughts and knowledge. Carr, 2002, argues that the physical act of writingplays a large part in the development of metacognitive skills.Strategy Evaluation matrices (SEM) can help to improve the knowledge of cognition component of metacogntion.The SEM works by identifying the declarative (Column 1), procedural (Column 2) and conditional (Column 3 and 4)knowledge about specific strategies. The SEM can help individuals identify the strength and weaknesses aboutcertain strategies as well as introduce them to new strategies that they can add to their repertoire.A regulation checklist (RC) is a useful strategy for improving the regulation of cognition aspect of onesmetacognition. RCs help individuals to implement a sequence of thoughts that allow them to go over their ownmetacogntion. King (1991) found that fifth-grade students who used a regulation checklist outperformed controlstudents when looking at a variety of questions including written problem solving, asking strategic questions, andelaborating information.Metacognitive strategies training can consist of coaching the students in thinking skills that will allow them tomonitor their own learning. Examples of strategies that can be taught to students are word analysis skills, activereading strategies, listening skills, organizational skills and creating mnemonic devices.Metacognition 5Meta-Strategic KnowledgeMeta-Strategic Knowledge (MSK) is a sub-component of metacognition that is defined as general knowledge abouthigher order thinking strategies. MSK had been defined as general knowledge about the cognitive procedures thatare being manipulated. The knowledge involved in MSK consists of making generalizations and drawing rulesregarding a thinking strategy and of naming the thinking strategy.[14]The important conscious act of a meta-strategic strategy is the conscious awareness that one is performing a formof higher order thinking. MSK is an awareness of the type of thinking strategies being used in specific instances andit consists of the following abilities: making generalizations and drawing rules regarding a thinking strategy, namingthe thinking strategy, explaining when, why and how such a thinking strategy should be used, when it should not beused, what are the disadvantages of not using appropriate strategies, and what task characteristics call for the use ofthe strategy.[15]MSK deals with the broader picture of the conceptual problem. It creates rules to describe and understand thephysical world around the people who utilize these processes called Higher-order thinking. This is the capability ofthe individual to take apart complex problems in order to understand the components in problem. These are thebuilding blocks to understanding the big picture (of the main problem) through reflection and problem solving.[16]Characteristics of Theory of Mind: Understanding the mind and the "mental world":1.1. False beliefs: understanding that a belief is only one of many and can be false.2. Appearancereality distinctions: something may look one way but may be something else.3.3. Visual perspective taking: the views of physical objects differ based on perspective.4.4. Introspection: children's awareness and understanding of their own thoughts.Mental Illness and MetacognitionSparks of InterestIn the context of mental health, metacognition can be loosely defined as the process that "reinforces one's subjectivesense of being a self and allows for becoming aware that some of one's thoughts and feelings are symptoms of anillness.[17]" The interest in metacognition emerged out of a concern for an individuals ability to understand theirown mental status compared to others as well as the ability to cope with the source of their distress.[18] Theseinsights into an individual's mental health status can have a profound affect on the over-all prognosis and recovery.Metacognition brings many unique insights into the normal daily functioning of a human being. It also demonstratesthat a lack of these insights compromises normal functioning. This leads to less healthy functioning. In the Autismspectrum, there is a profound inability to feel empathy towards the minds of other human beings.[19] In people whoidentify as alcoholics, there is a belief that the need to control cognitions is an independent predictor of alcohol useover anxiety. Alcohol may be used as a coping strategy for controlling unwanted thoughts and emotions formed bynegative perceptions.[20] This is sometimes referred to as self medication.ImplicationsWells and Matthews theory proposes that when faced with an undesired choice, an individual can operate in two distinct modes: object and Metacognitive. Object mode interprets perceived stimuli as truth, where Metacognitive mode understands thoughts as cues that have to be weighted and evaluated. They are not as easily trusted. There are targeted interventions unique of each patient, that gives rise to the belief that assistance in increasing metacognition in people diagnosed with schizophrenia is possible through tailored psychotherapy. With a customized therapy in place clients then have the potential to develop greater ability to engage in complex self-reflection.[21] This can ultimately be pivotal in the patient's recovery process. In the Obsessive Compulsive Disorder spectrum, cognitive formulations have greater attention to intrusive thoughts related to the disorder. "Cognitive Self-Consciousness" areMetacognition 6the tendencies to focus attention on thought. Patients with OCD exemplify varying degrees of these intrusivethoughts. Patients also suffering from Generalized Anxiety Disorder also show negative thought process in theircognition.[22]With any metacognition strategy, the general consensus is to believe that they are good. But in all actuality somemay be very harmful. Cognitive-Attentional Syndrome (CAS) characterizes a Metacognitive model of emotiondisorder. CAS is consistent with the constant with the attention strategy of excessively focusing on the source of athreat. This ultimately develops through the clients own beliefs. Metacognitive therapy attempts to correct thischange in the CAS. One of the techniques in this model is called Attention Training (ATT). It was designed todiminish the worry and anxiety by a sense of control and cognitive awareness. Also ATT trains clients to detectthreats, test how controllable reality appears to be.[23]Works of art as metacognitive artifactsThe concept of metacognition has also been applied to reader-response criticism. Narrative works of art, includingnovels, movies and musical compositions, can be characterized as metacognitive artifacts which are designed by theartist to anticipate and regulate the beliefs and cognitive processes of the recipient, for instance, how and in whichorder events and their causes and identities are revealed to the reader of a detective story. As Menakhem Perry haspointed out, mere order has profound effects on the aesthetical meaning of a text. Narrative works of art contain arepresentation of their own ideal reception process. They are something of a tool with which the creators of the workwish to attain certain aesthetical and even moral effects.[24]References[1] Metcalfe, J., & Shimamura, A. P. (1994). Metacognition: knowing about knowing. Cambridge, MA: MIT Press.[2] Dunlosky, J. & Bjork, R. A. (Eds), Handbook of Metamemory and Memory. Psychology Press: New York.[3] Wright, Frederick. APERA Conference 2008. 14 Apr. 2009. http:/ / www. apera08. nie. edu. sg/ proceedings/ 4. 24. pdf. . >[4] Oxford Psychology Dictionary;metacognition[5][5] Nisbet, Shucksmith (1984). The Seventh Sense (p6) SCRE Publications[6] Demetriou, A., Efklides, A., & Platsidou, M. (1993). The architecture and dynamics of developing mind: Experiential structuralism as aframe for unifying cognitive developmental theories. Monographs of the Society for Research in Child Development, 58, Serial Number 234.[7][7] Hartman, 2001.[8] Oyserman & Destin 1011, 2010.[9] Oyserman & Destin 1013, 2010.[10] Oyserman & Destin 1014, 2010.[11] Oyserman, D., & Destin, M. (2010) Identity-Based Motivation: Implications for Intervention. The Counseling Psychologist, 38 (7),10011043.[12] Metacognition: Known unknowns (http:/ / www. newscientist. com/ channel/ being-human/ mg19225821.600-metacognition-known-unknowns. html). Issue 2582 of New Scientist magazine, subscribers only.[13] Rats Capable Of Reflecting On Mental Processes (http:/ / www. sciencedaily. com/ releases/ 2007/ 03/ 070308121856. htm)[14] Zohar, A., & Ben David, A. (2009). Paving a clear path in a thick forest: A conceptual analysis of a metacognitive component.Metacognition And Learning, 4(3), 177-195. doi:10.1007/s11409-009-9044-6[15] Veenman, M. V. J. (2006). Metacognition: Definitions, constituents, and their intricate relation with cognition. Symposium organized byMarcel V. J. Veenman, Anat Zohar, and Anastasia Efklides for the 2nd conference of the EARLI SIG on Metacognition (SIG 16), Cambridge,UK, July 1921.[16] Beer, N., & Moneta, G. B. (2012). Coping and perceived stress as a function of positive metacognitions and positive meta-emotions.Individual Differences Research, 10(2), 105116.[17] Lysaker, P. H., Dimaggio, G., Buck, K. D., Callaway, S. S., Salvatore, G., Carcione, A., & ... Stanghellini, G. (2011). Poor insight inschizophrenia: Links between different forms of metacognition with awareness of symptoms, treatment needed, and consequences of illness.Comprehensive Psychiatry, 52(3), 253-260.[18] Semerari, A., Carcione, A., Dimaggio, G., Falcone, M., Nicol ` o, G., Procacci, M., & Alleva, G. (2003). How to evaluate Metacognitivefunction in psychotherapy? The Metacognition Assessment Scale and it's applications. Clinical Psychology & Psychotherapy, 10, 238261.[19] Lysaker, P. H., Gumley, A., & Dimaggio, G. (2011). Metacognitive disturbances in people with severe mental illness: Theory, correlateswith psychopathology and models of psychotherapy. Psychology And Psychotherapy: Theory, Research And Practice, 84(1), 1-8.doi:10.1111/j.2044-8341.2010.02007.xMetacognition 7[20] Spada, M. M., Zandvoort, M., & Wells, A. (2007). Metacognitions in problem drinkers. Cognitive Therapy And Research, 31(5), 709-716.doi:10.1007/s10608-006-9066-1[21] Lysaker, P. H., Buck, K. D., Carcione, A., Procacci, M., Salvatore, G., Nicol, G., & Dimaggio, G. (2011). Addressing metacognitivecapacity for self-reflection in the psychotherapy for schizophrenia: A conceptual model of the key tasks and processes. Psychology AndPsychotherapy: Theory, Research And Practice, 84(1), 58-69.[22] Jacobi, D. M., Calamari, J. E., & Woodard, J. L. (2006). Obsessive-Compulsive Disorder Beliefs, Metacognitive Beliefs and ObsessionalSymptoms: Relations between Parent Beliefs and Child Symptoms. Clinical Psychology & Psychotherapy, 13(3), 153-162.doi:10.1002/cpp.485[23] Wells, A., Fisher, P., Myers, S., Wheatley, J., Patel, T., & Brewin, C. R. (2009). Metacognitive therapy in recurrent and persistentdepression: A multiple-baseline study of a new treatment. Cognitive Therapy And Research, 33(3), 291-300. doi:10.1007/s10608-007-9178-2[24][24] Lng 1998, p. 88.Further readingAnnual Editions: Educational Psychology. Guilford: Dushkin Pub., 2002. Print. Barell, J. (1992), Like an incredibly hard algebra problem: Teaching for metacognition In A. L. Costa, J. A. Bellanca, & R. Fogarty (eds.) Ifminds matter: A foreword to the future, Volume I (pp.257266). Palatine, IL: IRI/Skylight Publishing, Inc. Beck, G. M. (1998) The Impact of a Prescriptive Curriculum on the Development of Higher Order Thinking Skills in Children, UnpublishedMA dissertation, University of Leicester. Brown, A. (1987). Metacognition, executive control, self-control, and other mysterious mechanisms. In F. Weinert and R. Kluwe (Eds.),Metacognition, Motivation, and Understanding (pp.65116). Hillsdale, NJ: Erlbaum. Burke, K. (1999), The Mindful School: How to Assess Authentic Learning (3rd ed.), SkyLight Training and Publishing, USA. ISBN1-57517-151-1 Carr, S.C. (2002). "Assessing learning processes: Useful information for teachers and students". Intervention in School and Clinic 37:156162. Chamot, A. (2005). The Cognitive Academic Language Learning Approach (CALLA): An update. In P. Richard-Amato and M. Snow (eds),Academic Success for English Language Learners (pp.87101). White Plains, NY: Longman. Dunlosky, John & Metcalfe, Janet (2009). Metacognition. Los Angeles: SAGE. ISBN 978-1-4129-3972-0 Fisher, Peter & Wells, Adrian (2009). Metacognitive Therapy: Distinctive Features. London: Routledge. ISBN 978-0-415-43499-7 Flavell, J. H. (1976). Metacognitive aspects of problem solving. In L. B. Resnick (Ed.), The nature of intelligence (pp.231236). Hillsdale,NJ: Erlbaum Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, v34n10 p906-11 Oct 1979. Hartman, H. J. (2001). Metacognition in Learning and Instruction: Theory, Research and Practice. Dordrecht: Kluwer Academic Publishers Niemi, H. (2002). Active learninga cultural change needed in teacher education and schools. Teaching and Teacher Education, 18, 763-780. Rasekh, Z., & Ranjbary, R. (2003). Metacognitive strategy training for vocabulary learning, TESL-EJ, 7(2), 1-18. Shimamura, A. P. (2000). "Toward a cognitive neuroscience of metacognition". Consciousness and Cognition 9: 313323. H. S. Terrace & J. Metcalfe (Eds.), The Missing Link in Cognition: Origins of Self-Reflective Consciousness. New York: Oxford UniversityPress. Metcalfe, J., & Shimamura, A. P. (1994). Metacognition: knowing about knowing. Cambridge, MA: MIT Press. Papaleontiou-Louca, Eleonora (2008). Metacognition and Theory of Mind. Newcastle: Cambridge Scholars Publishing. ISBN978-1-84718-578-5 Smith, J. D., Beran, M. J., Couchman, J. J., Coutinho, M. V. C., & Boomer, J. B. (2009). Animal metacognition: Problems and prospects,WWW (http:/ / psyc. queensu. ca/ ccbr/ Vol4/ SmithABS. html), Comparative Cognition and Behavior Reviews, 4, 4053. Wenden, A. L. (1987). "Metacognition: An expanded view on the cognitive abilities of L2 learners". Language Learning 37 (4): 573594. Wenden, A. (1991). Learner Strategies for Learner Autonomy. London: Prentice Hall. Zhang, L. J. (2001). Awareness in reading: EFL students' metacognitive knowledge of reading strategies in an input-poor environment.Language Awareness, WWW (http:/ / www. multilingual-matters. net), 11 (4), 268-288. Zhang, L. J. (2010). A dynamic metacognitive systems account of Chinese university students knowledge about EFL reading. TESOL Quarterly, WWW (http:/ / www. ingentaconnect. com/ content/ tesol/ tq/ 2010/ 00000044/ 00000002/ art00006), 44Metacognition 8(2), 320-353.External links The International Association for Metacognition (http:/ / www. personal. kent. edu/ ~jdunlosk/ metacog/ ) Metacognition (http:/ / www. instructionaldesign. org/ concepts/ metacognition. html) in Learning Concepts (http:/ /www. instructionaldesign. org/ concepts/ index. html) Metacognition: An Overview (http:/ / www. gse. buffalo. edu/ fas/ shuell/ cep564/ Metacog. htm) by Jennifer A. Livingston(1997) at Unskilled and Unaware of It: How Difficulties in Recognizing One's Own Incompetence Lead to Inflated Self-Assessments (http:/ / www.apa. org/ journals/ features/ psp7761121. pdf) by Justin Kruger, David Dunning, Cornell University, Journal of Personality andSocial Psychology, vol 77, no 6, p 1121-1134, American Psychological Association (1999) Metacognitive knowledge (http:/ / wik. ed. uiuc. edu/ index. php/ Metacognitive_knowledge) Metacognition in Computation overview (http:/ / www. cs. umd. edu/ ~anderson/ MIC/ ) links Developing Metacognition (http:/ / www. ericdigests. org/ pre-9218/ developing. htm) ERIC Digest Workshops on Metacognition and Self-Regulated Learning in Educational Technology (http:/ / www. andrew. cmu. edu/ user/ iroll/workshops/ index. html)Deductive reasoningDeductive reasoning, also deductive logic or logical deduction or, informally, "top-down" logic,[1] is the processof reasoning from one or more general statements (premises) to reach a logically certain conclusion.Deductive reasoning links premises with conclusions. If all premises are true, the terms are clear, and the rules ofdeductive logic are followed, then the conclusion reached is necessarily true.Deductive reasoning (top-down logic) contrasts with inductive reasoning (bottom-up logic) in the following way: Indeductive reasoning, a conclusion is reached reductively by applying general rules that hold over the entirety of aclosed domain of discourse, narrowing the range under consideration until only the conclusion is left. In inductivereasoning, the conclusion is reached by generalizing or extrapolating from initial information (and so induction canbe used even in an open domain, one where there is epistemic uncertainty. (Note, however, that the inductivereasoning mentioned here is not the same as induction used in mathematical proofs - mathematical induction isactually a form of deductive reasoning.)Simple exampleAn example of a deductive argument:1.1. All men are mortal.2.2. Aristotle is a man.3.3. Therefore, Aristotle is mortal.The first premise states that all objects classified as "men" have the attribute "mortal". The second premise states that"Aristotle" is classified as a "man" a member of the set "men". The conclusion then states that "Aristotle" must be"mortal" because he inherits this attribute from his classification as a "man".Deductive reasoning 9Law of detachmentThe law of detachment (also known as affirming the antecedent and Modus ponens) is the first form of deductivereasoning. A single conditional statement is made, and a hypothesis (P) is stated. The conclusion (Q) is then deducedfrom the statement and the hypothesis. The most basic form is listed below:1. PQ (conditional statement)2.2. P (hypothesis stated)3.3. Q (conclusion deduced)In deductive reasoning, we can conclude Q from P by using the law of detachment.[2] However, if the conclusion (Q)is given instead of the hypothesis (P) then there is no definitive conclusion.The following is an example of an argument using the law of detachment in the form of an if-then statement:1. If an angle satisfies 90Deductive reasoning 10Validity and soundnessDeductive arguments are evaluated in terms of their validity and soundness.An argument is valid if it is impossible for its premises to be true while its conclusion is false. In other words, theconclusion must be true if the premises are true. An argument can be valid even though the premises are false.An argument is sound if it is valid and the premises are true.It is possible to have a deductive argument that is logically valid but is not sound. Fallacious arguments often takethat form.The following is an example of an argument that is valid, but not sound:1.1. Everyone who eats carrots is a quarterback.2.2. John eats carrots.3.3. Therefore, John is a quarterback.The example's first premise is false there are people who eat carrots and are not quarterbacks but the conclusionmust be true, so long as the premises are true (i.e. it is impossible for the premises to be true and the conclusionfalse). Therefore the argument is valid, but not sound. Generalizations are often used to make invalid arguments,such as "everyone who eats carrots is a quarterback." Not everyone who eats carrots is a quarterback, thus provingthe flaw of such arguments.In this example, the first statement uses categorical reasoning, saying that all carrot-eaters are definitelyquarterbacks. This theory of deductive reasoning also known as term logic was developed by Aristotle, but wassuperseded by propositional (sentential) logic and predicate logic.Deductive reasoning can be contrasted with inductive reasoning, in regards to validity and soundness. In cases ofinductive reasoning, even though the premises are true and the argument is "valid", it is possible for the conclusionto be false (determined to be false with a counterexample or other means).EducationDeductive reasoning is generally thought of as a skill that develops without any formal teaching or training. As aresult of this belief, deductive reasoning skills are not taught in secondary schools, where students are expected touse reasoning more often and at a higher level. It is in high school, for example, that students have an abruptintroduction to mathematical proofs which rely heavily on deductive reasoning.References[1] Deduction & Induction, Research Methods Knowledge Base (http:/ / www. socialresearchmethods. net/ kb/ dedind. php)[2] Guide to Logic (http:/ / www. jgsee. kmutt. ac. th/ exell/ Logic/ Logic12. htm#25)Further reading Vincent F. Hendricks, Thought 2 Talk: A Crash Course in Reflection and Expression, New York: AutomaticPress / VIP, 2005, ISBN 87-991013-7-8 Philip Johnson-Laird, Ruth M. J. Byrne, Deduction, Psychology Press 1991, ISBN 978-0-86377-149-1jiii Zarefsky, David, Argumentation: The Study of Effective Reasoning Parts I and II, The Teaching Company 2002External links Deductive reasoning (http:/ / philpapers. org/ browse/ deductive-reasoning) at PhilPapers Deductive reasoning (https:/ / inpho. cogs. indiana. edu/ idea/ 636) at the Indiana Philosophy Ontology Project Deductive reasoning (http:/ / www. iep. utm. edu/ ded-ind) entry in the Internet Encyclopedia of PhilosophyInductive reasoning 11Inductive reasoningInductive reasoning (as opposed to deductive reasoning) is reasoning in which the premises seek to supply strongevidence for (not absolute proof of) the truth of the conclusion. While the conclusion of a deductive argument issupposed to be certain, the truth of an inductive argument is supposed to be probable, based upon the evidencegiven.[1]DefinitionThe philosophical definition of inductive reasoning is much more nuanced than simple progression fromparticular/individual instances to broader generalizations. Rather, the premises of an inductive logical argumentindicate some degree of support (inductive probability) for the conclusion but do not entail it; that is, they suggesttruth but do not ensure it. In this manner, there is the possibility of moving from general statements to individualinstances (for example, statistical syllogisms, discussed below).Though many dictionaries define inductive reasoning as reasoning that derives general principles from specificobservations, this usage is outdated.DescriptionInductive reasoning is inherently uncertain. It only deals in degrees to which, given the premises, the conclusion iscredible according to some theory of evidence, for example a many-valued logic, DempsterShafer theory, orprobability theory with rules for inference such as Bayes' rule. Unlike deductive reasoning, it does not rely onuniversals holding over a closed domain of discourse to draw conclusions, so it can be applicable even in cases ofepistemic uncertainty (technical issues with this may arise however; for example, the second axiom of probability isa closed-world assumption).[2]A statistical syllogism is an example of inductive reasoning:1.1. Almost all people are taller than 26inches2.2. Gareth is a person3.3. Therefore, Gareth is almost certainly taller than 26inchesAs a stronger example:100% of biological life forms that we know of depend on liquid water to exist.Therefore, if we discover a new biological life form it will probably depend on liquid water to exist.This argument could have been made every time a new biological life form was found, and would have been correctevery time; this does not mean it is impossible that in the future a biological life form that does not require watercould be discovered.As a result, the argument may be stated less formally as:All biological life forms that we know of depend on liquid water to exist.All biological life probably depends on liquid water to exist.Inductive reasoning 12Inductive vs. deductive reasoningUnlike deductive arguments, inductive reasoning allows for the possibility that the conclusion is false, even if all ofthe premises are true.[3] Instead of being valid or invalid, inductive arguments are either strong or weak, whichdescribes how probable it is that the conclusion is true.A classical example of an incorrect inductive argument was presented by John Vickers:All of the swans we have seen are white.Therefore, all swans are white.Note that this definition of inductive reasoning excludes mathematical induction, which is a form of deductivereasoning.CriticismInductive reasoning has been criticized by thinkers as diverse as Sextus Empiricus[4] and Karl Popper.[5]The classic philosophical treatment of the problem of induction was given by the Scottish philosopher David Hume.Hume highlighted the fact that our everyday habits of mind depend on drawing uncertain conclusions from ourrelatively limited experiences rather than on deductively valid arguments. For example, we believe that bread willnourish us because it has done so in the past, despite no guarantee that it will do so. Hume argued that it isimpossible to justify inductive reasoning: specifically, that it cannot be justified deductively, so our only option is tojustify it inductively. Since this is circular he concluded that it is impossible to justify induction.[6]However, Hume then stated that even if induction were proved unreliable, we would still have to rely on it. Soinstead of a position of severe skepticism, Hume advocated a practical skepticism based on common sense, wherethe inevitability of induction is accepted.[7]BiasesInductive reasoning is also known as hypothesis construction because any conclusions made are based on currentknowledge and predictions.[citation needed] As with deductive arguments, biases can distort the proper application ofinductive argument, thereby preventing the reasoner from forming the most logical conclusion based on the clues.Examples of these biases include the availability heuristic, confirmation bias, and the predictable-world bias.The availability heuristic causes the reasoner to depend primarily upon information that is readily available tohim/her. People have a tendency to rely on information that is easily accessible in the world around them. Forexample, in surveys, when people are asked to estimate the percentage of people who died from various causes, mostrespondents would choose the causes that have been most prevalent in the media such as terrorism, and murders, andairplane accidents rather than causes such as disease and traffic accidents, which have been technically "lessaccessible" to the individual since they are not emphasized as heavily in the world around him/her.The confirmation bias is based on the natural tendency to confirm rather than to deny a current hypothesis. Researchhas demonstrated that people are inclined to seek solutions to problems that are more consistent with knownhypotheses rather than attempt to refute those hypotheses. Often, in experiments, subjects will ask questions thatseek answers that fit established hypotheses, thus confirming these hypotheses. For example, if it is hypothesizedthat Sally is a sociable individual, subjects will naturally seek to confirm the premise by asking questions that wouldproduce answers confirming that Sally is in fact a sociable individual.The predictable-world bias revolves around the inclination to perceive order where it has not been proved to exist, either at all or at a particular level of abstraction. Gambling, for example, is one of the most popular examples of predictable-world bias. Gamblers often begin to think that they see simple and obvious patterns in the outcomes and, therefore, believe that they are able to predict outcomes based upon what they have witnessed. In reality, however, the outcomes of these games are difficult to predict and highly complex in nature. However, in general, people tendInductive reasoning 13to seek some type of simplistic order to explain or justify their beliefs and experiences, and it is often difficult forthem to realise that their perceptions of order may be entirely different from the truth.[8]TypesGeneralizationA generalization (more accurately, an inductive generalization) proceeds from a premise about a sample to aconclusion about the population.The proportion Q of the sample has attribute A.Therefore:The proportion Q of the population has attribute A.ExampleThere are 20 ballseither black or whitein an urn. To estimate their respective numbers, you draw a sample offour balls and find that three are black and one is white. A good inductive generalization would be that there are 15black, and five white, balls in the urn.How much the premises support the conclusion depends upon (a) the number in the sample group, (b) the number inthe population, and (c) the degree to which the sample represents the population (which may be achieved by taking arandom sample). The hasty generalization and the biased sample are generalization fallacies.Statistical syllogismA statistical syllogism proceeds from a generalization to a conclusion about an individual.A proportion Q of population P has attribute A.An individual X is a member of P.Therefore:There is a probability which corresponds to Q that X has A.The proportion in the first premise would be something like "3/5ths of", "all", "few", etc. Two dicto simpliciterfallacies can occur in statistical syllogisms: "accident" and "converse accident".Simple inductionSimple induction proceeds from a premise about a sample group to a conclusion about another individual.Proportion Q of the known instances of population P has attribute A.Individual I is another member of P.Therefore:There is a probability corresponding to Q that I has A.This is a combination of a generalization and a statistical syllogism, where the conclusion of the generalization isalso the first premise of the statistical syllogism.Inductive reasoning 14Argument from analogyThe process of analogical inference involves noting the shared properties of two or more things, and from this basisinferring that they also share some further property:P and Q are similar in respect to properties a, b, and c.Object P has been observed to have further property x.Therefore, Q probably has property x also.Analogical reasoning is very frequent in common sense, science, philosophy and the humanities, but sometimes it isaccepted only as an auxiliary method. A refined approach is case-based reasoning. For more information oninferences by analogy, see Juthe, 2005 [9].Causal inferenceA causal inference draws a conclusion about a causal connection based on the conditions of the occurrence of aneffect. Premises about the correlation of two things can indicate a causal relationship between them, but additionalfactors must be confirmed to establish the exact form of the causal relationship.PredictionA prediction draws a conclusion about a future individual from a past sample.Proportion Q of observed members of group G have had attribute A.Therefore:There is a probability corresponding to Q that other members of group G will have attribute A when nextobserved.Bayesian inferenceAs a logic of induction rather than a theory of belief, Bayesian inference does not determine which beliefs are apriori rational, but rather determines how we should rationally change the beliefs we have when presented withevidence. We begin by committing to a prior probability for a hypothesis based on logic or previous experience, andwhen faced with evidence, we adjust the strength of our belief in that hypothesis in a precise manner using Bayesianlogic.Inductive inferenceAround 1960, Ray Solomonoff founded the theory of universal inductive inference, the theory of prediction based onobservations; for example, predicting the next symbol based upon a given series of symbols. This is a formalinductive framework that combines algorithmic information theory with the Bayesian framework. Universalinductive inference is based on solid philosophical foundations[10] and can be considered as a mathematicallyformalized Occam's razor. Fundamental ingredients of the theory are the concepts of algorithmic probability andKolmogorov complexity.Inductive reasoning 15References[1] Copi, I. M., Cohen, C., & Flage, D. E. (2007). Essentials of logic (2nd ed.). Upper Saddle River, NJ: Pearson Education, Inc.[2] Bart Kosko, Fuzziness vs. Probability, International Journal of General Systems, vol. 17, no. 1, pp. 211-240, 1990.[3] John Vickers. The Problem of Induction (http:/ / plato. stanford. edu/ entries/ induction-problem/ ). The Stanford Encyclopedia of Philosophy.[4] Sextus Empiricus, Outlines Of Pyrrhonism. Trans. R.G. Bury, Harvard University Press, Cambridge, Massachusetts, 1933, p. 283.[5] Karl R. Popper, David W. Miller. "A proof of the impossibility of inductive probability." Nature 302 (1983), 687688.[6] Vickers, John. "The Problem of Induction" (http:/ / plato. stanford. edu/ entries/ induction-problem/ #2HumIndJus) (Section 2). StanfordEncyclopedia of Philosophy. 21 June 2010[7] Vickers, John. "The Problem of Induction" (http:/ / plato. stanford. edu/ entries/ induction-problem/ #IndJus) (Section 2.1). StanfordEncyclopedia of Philosophy. 21 June 2010.[8][8] Gray, Peter. Psychology. New York: Worth, 2011. Print.[9] http:/ / www. cs. hut. fi/ Opinnot/ T-93. 850/ 2005/ Papers/ juthe2005-analogy. pdf[10] Samuel Rathmanner and Marcus Hutter. A philosophical treatise of universal induction. Entropy, 13(6):10761136, 2011Further reading Herms, D. "Logical Basis of Hypothesis Testing in Scientific Research" (http:/ / www. dartmouth. edu/ ~bio125/logic. Giere. pdf) (PDF). Kemerling, G. (27 October 2001). "Causal Reasoning" (http:/ / www. philosophypages. com/ lg/ e14. htm). Holland, J. H.; Holyoak, K. J.; Nisbett, R. E.; Thagard, P. R. (1989). Induction: Processes of Inference, Learning,and Discovery. Cambridge, MA, USA: MIT Press. ISBN0-262-58096-9. Holyoak, K.; Morrison, R. (2005). The Cambridge Handbook of Thinking and Reasoning. New York: CambridgeUniversity Press. ISBN978-0-521-82417-0.External links Confirmation and Induction (http:/ / www. iep. utm. edu/ conf-ind) entry in the Internet Encyclopedia ofPhilosophy Inductive Logic (http:/ / plato. stanford. edu/ entries/ logic-inductive) entry in the Stanford Encyclopedia ofPhilosophy Inductive reasoning (http:/ / philpapers. org/ browse/ induction) at PhilPapers Inductive reasoning (https:/ / inpho. cogs. indiana. edu/ taxonomy/ 2256) at the Indiana Philosophy OntologyProject Four Varieties of Inductive Argument (http:/ / www. uncg. edu/ phi/ phi115/ induc4. htm) from the Department ofPhilosophy, University of North Carolina at Greensboro. Properties of Inductive Reasoning (http:/ / faculty. ucmerced. edu/ sites/ default/ files/ eheit/ files/ heit2000.pdf)PDF(166KiB), a psychological review by Evan Heit of the University of California, Merced. The Mind, Limber (http:/ / dudespaper. com/ the-mind-limber. html) An article which employs the film The BigLebowski to explain the value of inductive reasoning.Problem solving 16Problem solvingProblem-solving consists of using generic or ad hoc methods, in an orderly manner, for finding solutions toproblems. Some of the problem-solving techniques developed and used in artificial intelligence, computer science,engineering, mathematics, medicine, etc. are related to mental problem-solving techniques studied in psychology.DefinitionThe term problem-solving is used in many disciplines, sometimes with different perspectives, and often withdifferent terminologies. For instance, it is a mental process in psychology and a computerized process in computerscience. Problems can also be classified into two different types (ill-defined and well-defined) from whichappropriate solutions are to be made. Ill-defined problems are those that do not have clear goals, solution paths, orexpected solution. Well-defined problems have specific goals, clearly defined solution paths, and clear expectedsolutions. These problems also allow for more initial planning than ill-defined problems.[1] Being able to solveproblems sometimes involves dealing with pragmatics (logic) and semantics (interpretation of the problem). Theability to understand what the goal of the problem is and what rules there are key to solving the problem. Sometimesthe problem requires some abstract thinking and coming up with a creative solution.PsychologyIn psychology, problem solving refers to a state of desire for reaching a definite 'goal' from a present condition thateither is not directly moving toward the goal, is far from it, or needs more complex logic for finding a missingdescription of conditions or steps toward the goal.[2] In psychology, problem solving is the concluding part of alarger process that also includes problem finding and problem shaping.Considered the most complex of all intellectual functions, problem solving has been defined as a higher-ordercognitive process that requires the modulation and control of more routine or fundamental skills. Problem solvinghas two major domains: mathematical problem solving and personal problem solving where, in the second, somedifficulty or barrier is encountered.[3] Further problem solving occurs when moving from a given state to a desiredgoal state is needed for either living organisms or an artificial intelligence system.While problem solving accompanies the very beginning of human evolution and especially the history ofmathematics, the nature of human problem solving processes and methods has been studied by psychologists overthe past hundred years. Methods of studying problem solving include introspection, behaviorism, simulation,computer modeling, and experiment. Social psychologists have recently distinguished between independent andinterdependent problem-solving (see more [4]).[5]Clinical PsychologySimple laboratory-based tasks can be useful in explicating the steps of logic and reasoning that underlie problemsolving; however, they usually omit the complexity and emotional valence of "real-world" problems. In clinicalpsychology, researchers have focused on the role of emotions in problem solving (D'Zurilla & Goldfried, 1971;D'Zurilla & Nezu, 1982), demonstrating that poor emotional control can disrupt focus on the target task and impedeproblem resolution (Rath, Langenbahn, Simon, Sherr, & Diller, 2004). In this conceptualization, human problemsolving consists of two related processes: problem orientation, the motivational/attitudinal/affective approach toproblematic situations and problem-solving skills. Working with individuals with frontal lobe injuries,neuropsychologists have discovered that deficits in emotional control and reasoning can be remedied, improving thecapacity of injured persons to resolve everyday problems successfully (Rath, Simon, Langenbahn, Sherr, & Diller,2003).Problem solving 17Cognitive SciencesThe early experimental work of the Gestaltists in Germany placed the beginning of problem solving study (e.g., KarlDuncker in 1935 with his book The psychology of productive thinking ). Later this experimental work continuedthrough the 1960s and early 1970s with research conducted on relatively simple (but novel for participants)laboratory tasks of problem solving.[6] Choosing simple novel tasks was based on the clearly defined optimalsolutions and their short time for solving, which made possible for the researchers to trace participants' steps inproblem-solving process. Researchers' underlying assumption was that simple tasks such as the Tower of Hanoicorrespond to the main properties of "real world" problems and thus the characteristic cognitive processes withinparticipants' attempts to solve simple problems are the same for "real world" problems too; simple problems wereused for reasons of convenience and with the expectation that thought generalizations to more complex problemswould become possible. Perhaps the best-known and most impressive example of this line of research is the work byAllen Newell and Herbert A. Simon. Other experts have shown that the principle of decomposition improves theability of the problem solver to make good judgment.Computer Science and AlgorithmicsIn computer science and in the part of artificial intelligence that deals with algorithms ("algorithmics"), problemsolving encompasses a number of techniques known as algorithms, heuristics, root cause analysis, etc. In thesedisciplines, problem solving is part of a larger process that encompasses problem determination, de-duplication,analysis, diagnosis, repair, etc.EngineeringProblem solving is used in engineering when products or processes fail, so corrective action can be taken to preventfurther failures. It can also be applied to a product or process prior to an actual fail event, i.e., when a potentialproblem can be predicted and analyzed, and mitigation applied so the problem never actually occurs. Techniquessuch as Failure Mode Effects Analysis can be used to proactively reduce the likelihood of problems occurring.Forensic engineering is an important technique of failure analysis that involves tracing product defects and flaws.Corrective action can then be taken to prevent further failures.Reverse engineering attempts to discover the original problem-solving logic used in developing a product by takingit apart.Cognitive Sciences: Two SchoolsIn cognitive sciences, researchers' realization that problem-solving processes differ across knowledge domains andacross levels of expertise (e.g. Sternberg, 1995) and that, consequently, findings obtained in the laboratory cannotnecessarily generalize to problem-solving situations outside the laboratory, has led to an emphasis on real-worldproblem solving since the 1990s. This emphasis has been expressed quite differently in North America and Europe,however. Whereas North American research has typically concentrated on studying problem solving in separate,natural knowledge domains, much of the European research has focused on novel, complex problems, and has beenperformed with computerized scenarios (see Funke, 1991, for an overview).EuropeIn Europe, two main approaches have surfaced, one initiated by Donald Broadbent (1977; see Berry & Broadbent, 1995) in the United Kingdom and the other one by Dietrich Drner (1975, 1985; see Drner & Wearing, 1995) in Germany. The two approaches share an emphasis on relatively complex, semantically rich, computerized laboratory tasks, constructed to resemble real-life problems. The approaches differ somewhat in their theoretical goals and methodology, however. The tradition initiated by Broadbent emphasizes the distinction between cognitiveProblem solving 18problem-solving processes that operate under awareness versus outside of awareness, and typically employsmathematically well-defined computerized systems. The tradition initiated by Drner, on the other hand, has aninterest in the interplay of the cognitive, motivational, and social components of problem solving, and utilizes verycomplex computerized scenarios that contain up to 2,000 highly interconnected variables (e.g., Drner, Kreuzig,Reither & Studel's 1983 LOHHAUSEN project; Ringelband, Misiak & Kluwe, 1990). Buchner (1995) describes thetwo traditions in detail.North AmericaIn North America, initiated by the work of Herbert A. Simon on "learning by doing" in semantically rich domains(e.g. Anzai & Simon, 1979; Bhaskar & Simon, 1977), researchers began to investigate problem solving separately indifferent natural knowledge domains such as physics, writing, or chess playing thus relinquishing their attemptsto extract a global theory of problem solving (e.g. Sternberg & Frensch, 1991). Instead, these researchers havefrequently focused on the development of problem solving within a certain domain, that is on the development ofexpertise (e.g. Anderson, Boyle & Reiser, 1985; Chase & Simon, 1973; Chi, Feltovich & Glaser, 1981).Areas that have attracted rather intensive attention in North America include: Reading (Stanovich & Cunningham, 1991) Writing (Bryson, Bereiter, Scardamalia & Joram, 1991) Calculation (Sokol & McCloskey, 1991) Political decision making (Voss, Wolfe, Lawrence & Engle, 1991) Problem Solving for Business (Cornell, 2010) Managerial problem solving (Wagner, 1991) Lawyers' reasoning (Amsel, Langer & Loutzenhiser, 1991) Mechanical problem solving (Hegarty, 1991) Problem solving in electronics (Lesgold & Lajoie, 1991) Computer skills (Kay, 1991) Game playing (Frensch & Sternberg, 1991) Personal problem solving (Heppner & Krauskopf, 1987) Mathematical problem solving (Plya, 1945; Schoenfeld, 1985) Social problem solving (D'Zurilla & Goldfreid, 1971; D'Zurilla & Nezu, 1982) Problem solving for innovations and inventions: TRIZ (Altshuller, 1973,1990,1995]Characteristics of Difficult ProblemsAs elucidated by Dietrich Drner and later expanded upon by Joachim Funke, difficult problems have some typicalcharacteristics that can be summarized as follows: Intransparency (lack of clarity of the situation) commencement opacity continuation opacity Polytely (multiple goals) inexpressiveness opposition transience Complexity (large numbers of items, interrelations and decisions) enumerability connectivity (hierarchy relation, communication relation, allocation relation) heterogeneityProblem solving 19 Dynamics (time considerations) temporal constraints temporal sensitivity phase effects dynamic unpredictabilityThe resolution of difficult problems requires a direct attack on each of these characteristics that are encountered.[7]Problem-Solving StrategiesProblem-solving strategies are the steps that one would use to find the problem(s) that are in the way to getting toones own goal. Some would refer to this as the problem-solving cycle. (Bransford & Stein, 1993) In this cycle onewill recognize the problem, define the problem, develop a strategy to fix the problem, organize the knowledge of theproblem, figure-out the resources at the user's disposal, monitor one's progress, and evaluate the solution foraccuracy. Although called a cycle, one does not have to do each step in order to fix the problem, in fact those whodont are usually better at problem solving.[citation needed] The reason it is called a cycle is that once one is completedwith a problem another usually will pop up. Blanchard-Fields (2007) looks at problem solving from one of twofacets. The first looking at those problems that only have one solution (like math problems, or fact based questions)which are grounded in psychometric intelligence. The other that is socioemotional in nature and are unpredictablewith answers that are constantly changing (like whats your favorite color or what you should get someone forChristmas).The following techniques are usually called problem-solving strategies:[citation needed] Abstraction: solving the problem in a model of the system before applying it to the real system Analogy: using a solution that solves an analogous problem Brainstorming: (especially among groups of people) suggesting a large number of solutions or ideas andcombining and developing them until an optimum solution is found Divide and conquer: breaking down a large, complex problem into smaller, solvable problems Hypothesis testing: assuming a possible explanation to the problem and trying to prove (or, in some contexts,disprove) the assumption Lateral thinking: approaching solutions indirectly and creatively Means-ends analysis: choosing an action at each step to move closer to the goal Method of focal objects: synthesizing seemingly non-matching characteristics of different objects into somethingnew Morphological analysis: assessing the output and interactions of an entire system Proof: try to prove that the problem cannot be solved. The point where the proof fails will be the starting point forsolving it Reduction: transforming the problem into another problem for which solutions exist Research: employing existing ideas or adapting existing solutions to similar problems Root cause analysis: identifying the cause of a problem Trial-and-error: testing possible solutions until the right one is foundProblem solving 20Problem-Solving Methodologies Eight Disciplines Problem Solving GROW model How to Solve It KEPNERandFOURIE Incident and Problem Investigation Kepner-Tregoe Problem Solving and Decision Making PDCA (plandocheckact) Productive Thinking Model RPR Problem Diagnosis (rapid problem resolution) Thinking Dimensions - Problem Solving TRIZ (in Russian: Teoriya Resheniya Izobretatelskikh Zadatch, "theory of solving inventor's problems")Notes[1] Schacter, D.L. et al. (2009). Psychology, Second Edition. New York: Worth Publishers. pp. 376[2] "In each case "where you want to be" is an imagined(or written) state in which you would like to be. We might use the term 'ProblemIdentification' or analysis in order to figure out exactly what the problem is. After we have found a problem we need to define what theproblem is. In other words, a distinguished feature of a problem is that there is a goal to be reached and how you get there is not immediatelyobvious.", What is a problem? in S. Ian Robertson, Problem solving, Psychology Press, 2001, p.2[3] Bernd Zimmermann, On mathematical problem solving processes and history of mathematics (http:/ / www. icme-organisers. dk/ tsg18/S12BerndZimmermann. pdf), University of Jena[4] https:/ / sites. google. com/ site/ markrubinsocialpsychresearch/ -independent-interdependent-problem-solving-scale[5] Rubin, M., Watt, S. E., & Ramelli, M. (2012). Immigrants social integration as a function of approach-avoidance orientation andproblem-solving style. International Journal of Intercultural Relations, 36, 498-505.[6] For example Duncker's "X-ray" problem; Ewert & Lambert's "disk" problem in 1932, later known as Tower of Hanoi.[7][7] Altshuller, Genrich (1973). Innovation Algorithm. Worcester, MA: Technical Innovation Center.ISBN0-9640740-2-8. Altshuller, Genrich (1984). Creativity as an Exact Science. New York, NY: Gordon & Breach.ISBN0-677-21230-5. Altshuller, Genrich (1994). And Suddenly the Inventor Appeared. translated by Lev Shulyak. Worcester, MA:Technical Innovation Center. ISBN0-9640740-1-X. Amsel, E., Langer, R., & Loutzenhiser, L. (1991). Do lawyers reason differently from psychologists? Acomparative design for studying expertise. In R. J. Sternberg & P. A. Frensch (Eds.), Complex problem solving:Principles and mechanisms (pp. 223-250). Hillsdale, NJ: Lawrence Erlbaum Associates. ISBN978-0-8058-1783-6 Anderson, J. R., Boyle, C. B., & Reiser, B. J. (1985). "Intelligent tutoring systems". 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WebKaizen, Better Faster Cheaper Problem Solving for Business. Omaha NE: PrevailDigital Publishing. ISBN978-0-9831102-1-7. Drner, D. (1975). Wie Menschen eine Welt verbessern wollten [How people wanted to improve the world]. Bildder Wissenschaft, 12, 48-53. Drner, D. (1985). Verhalten, Denken und Emotionen [Behavior, thinking, and emotions]. In L. H. Eckensberger& E. D. Lantermann (Eds.), Emotion und Reflexivitt (pp. 157-181). Mnchen, Germany: Urban &Schwarzenberg. Drner, D. (1992). ber die Philosophie der Verwendung von Mikrowelten oder "Computerszenarios" in derpsychologischen Forschung [On the proper use of microworlds or "computer scenarios" in psychologicalresearch]. In H. Gundlach (Ed.), Psychologische Forschung und Methode: Das Versprechen des Experiments.Festschrift fr Werner Traxel (pp. 53-87). Passau, Germany: Passavia-Universitts-Verlag. Drner, D., Kreuzig, H. W., Reither, F., & Studel, T. (Eds.). (1983). Lohhausen. Vom Umgang mitUnbestimmtheit und Komplexitt [Lohhausen. On dealing with uncertainty and complexity]. Bern, Switzerland:Hans Huber. Drner, D., & Wearing, A. (1995). Complex problem solving: Toward a (computer-simulated) theory. In P. A.Frensch & J. Funke (Eds.), Complex problem solving: The European Perspective (pp. 65-99). Hillsdale, NJ:Lawrence Erlbaum Associates. Duncker, K. (1935). Zur Psychologie des produktiven Denkens [The psychology of productive thinking]. Berlin:Julius Springer. Ewert, P. H., & Lambert, J. F. (1932). Part II: The effect of verbal instructions upon the formation of a concept.Journal of General Psychology, 6, 400-411.* DZurilla, T. J., & Goldfried, M. R. (1971). Problem solving andbehavior modification. Journal of Abnormal Psychology, 78, 107-126. D'Zurilla, T. J., & Nezu, A. M. (1982). Social problem solving in adults. In P. C. Kendall (Ed.), Advances incognitive-behavioral research and therapy (Vol. 1, pp.201274). New York: Academic Press. Eyferth, K., Schmann, M., & Widowski, D. (1986). Der Umgang von Psychologen mit Komplexitt [On howpsychologists deal with complexity]. Sprache & Kognition, 5, 11-26.Problem solving 22 Frensch, P. A., & Funke, J. (Eds.). (1995). Complex problem solving: The European Perspective. Hillsdale, NJ:Lawrence Erlbaum Associates. Frensch, P. A., & Sternberg, R. J. (1991). Skill-related differences in game playing. In R. J. Sternberg & P. A.Frensch (Eds.), Complex problem solving: Principles and mechanisms (pp. 343-381). Hillsdale, NJ: LawrenceErlbaum Associates. Funke, J. (1991). Solving complex problems: Human identification and control of complex systems. In R. J.Sternberg & P. A. Frensch (Eds.), Complex problem solving: Principles and mechanisms (pp. 185-222). Hillsdale,NJ: Lawrence Erlbaum Associates. Funke, J. (1993). Microworlds based on linear equation systems: A new approach to complex problem solvingand experimental results. In G. Strube & K.-F. Wender (Eds.), The cognitive psychology of knowledge (pp.313-330). Amsterdam: Elsevier Science Publishers. Funke, J. (1995). Experimental research on complex problem solving. In P. A. Frensch & J. Funke (Eds.),Complex problem solving: The European Perspective (pp. 243-268). Hillsdale, NJ: Lawrence ErlbaumAssociates. Funke, U. (1995). Complex problem solving in personnel selection and training. In P. A. Frensch & J. Funke(Eds.), Complex problem solving: The European Perspective (pp. 219-240). Hillsdale, NJ: Lawrence ErlbaumAssociates. Goldstein F. C., & Levin H. S. (1987). Disorders of reasoning and problem-solving ability. In M. Meier, A.Benton, & L. Diller (Eds.), Neuropsychological rehabilitation. London: Taylor & Francis Group. Groner, M., Groner, R., & Bischof, W. F. (1983). Approaches to heuristics: A historical review. In R. Groner, M.Groner, & W. F. Bischof (Eds.), Methods of heuristics (pp. 1-18). Hillsdale, NJ: Lawrence Erlbaum Associates. Halpern, Diane F. (2002).Thought & Knowledge. Lawrence Erlbaum Associates. Worldcat Library Catalog(http:/ / worldcat. org/ oclc/ 50065032& tab=holdings) Hayes, J. (1980). The complete problem solver. Philadelphia: The Franklin Institute Press. Hegarty, M. (1991). Knowledge and processes in mechanical problem solving. In R. J. Sternberg & P. A. Frensch(Eds.), Complex problem solving: Principles and mechanisms (pp. 253-285). Hillsdale, NJ: Lawrence ErlbaumAssociates. Heppner, P. P., & Krauskopf, C. J. (1987). An information-processing approach to personal problem solving. TheCounseling Psychologist, 15, 371-447. Huber, O. (1995). Complex problem solving as multi stage decision making. In P. A. Frensch & J. Funke (Eds.),Complex problem solving: The European Perspective (pp. 151-173). Hillsdale, NJ: Lawrence ErlbaumAssociates. Hbner, R. (1989). Methoden zur Analyse und Konstruktion von Aufgaben zur kognitiven Steuerungdynamischer Systeme [Methods for the analysis and construction of dynamic system control tasks]. Zeitschrift frExperimentelle und Angewandte Psychologie, 36, 221-238. Hunt, E. (1991). Some comments on the study of complexity. In R. J. Sternberg, & P. A. Frensch (Eds.), Complexproblem solving: Principles and mechanisms (pp. 383-395). Hillsdale, NJ: Lawrence Erlbaum Associates. Hussy, W. (1985). Komplexes Problemlsen - Eine Sackgasse? [Complex problem solving - a dead end?].Zeitschrift fr Experimentelle und Angewandte Psychologie, 32, 55-77. Kay, D. S. (1991). Computer interaction: Debugging the problems. In R. J. Sternberg & P. A. Frensch (Eds.),Complex problem solving: Principles and mechanisms (pp. 317-340). Hillsdale, NJ: Lawrence ErlbaumAssociates. Kluwe, R. H. (1993). Knowledge and performance in complex problem solving. In G. Strube & K.-F. Wender(Eds.), The cognitive psychology of knowledge (pp. 401-423). Amsterdam: Elsevier Science Publishers. Kluwe, R. H. (1995). Single case studies and models of complex problem solving. In P. A. Frensch & J. Funke(Eds.), Complex problem solving: The European Perspective (pp. 269-291). Hillsdale, NJ: Lawrence ErlbaumAssociates.Problem solving 23 Kolb, S., Petzing, F., & Stumpf, S. (1992). Komplexes Problemlsen: Bestimmung der Problemlsegte vonProbanden mittels Verfahren des Operations Research ? ein interdisziplinrer Ansatz [Complex problem solving:determining the quality of human problem solving by operations research tools - an interdisciplinary approach].Sprache & Kognition, 11, 115-128. Krems, J. F. (1995). Cognitive flexibility and complex problem solving. In P. A. Frensch & J. Funke (Eds.),Complex problem solving: The European Perspective (pp. 201-218). Hillsdale, NJ: Lawrence ErlbaumAssociates. Lesgold, A., & Lajoie, S. (1991). Complex problem solving in electronics. In R. J. Sternberg & P. A. Frensch(Eds.), Complex problem solving: Principles and mechanisms (pp. 287-316). Hillsdale, NJ: Lawrence ErlbaumAssociates. Mayer, R. E. (1992). Thinking, problem solving, cognition. Second edition. New York: W. H. Freeman andCompany. Mller, H. (1993). Komplexes Problemlsen: Reliabilitt und Wissen [Complex problem solving: Reliability andknowledge]. Bonn, Germany: Holos. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall. Offord, Paul (2011). RPR: A Problem Diagnosis Method for IT Professionals. Essex, England: Advance SevenLimited. ISBN978-1-4478-4443-3. Paradies, M.W., & Unger, L. W. (2000). TapRooT - The System for Root Cause Analysis, Problem Investigation,and Proactive Improvement. Knoxville, TN: System Improvements. Putz-Osterloh, W. (1993). Strategies for knowledge acquisition and transfer of knowledge in dynamic tasks. In G.Strube & K.-F. Wender (Eds.), The cognitive psychology of knowledge (pp. 331-350). Amsterdam: ElsevierScience Publishers. Rath J. F.; Langenbahn D. M.; Simon D.; Sherr R. L.; Fletcher J.; Diller L. (2004). The construct of problemsolving in higher level neuropsychological assessment and rehabilitation. Archives of Clinical Neuropsychology,19, 613-635. doi: 10.1016/j.acn.2003.08.006 (http:/ / dx. doi. org/ 10. 1016/ j. acn. 2003. 08. 006) Rath, J. F.; Simon, D.; Langenbahn, D. M.; Sherr, R. L.; Diller, L. (2003). Group treatment of problem-solvingdeficits in outpatients with traumatic brain injury: A randomised outcome study. NeuropsychologicalRehabilitation, 13, 461-488. Riefer, D.M., & Batchelder, W.H. (1988). Multinomial modeling and the measurement of cognitive processes.Psychological Review, 95, 318-339. Ringelband, O. J., Misiak, C., & Kluwe, R. H. (1990). Mental models and strategies in the control of a complexsystem. In D. Ackermann, & M. J. Tauber (Eds.), Mental models and human-computer interaction (Vol. 1, pp.151-164). Amsterdam: Elsevier Science Publishers. Schaub, H. (1993). Modellierung der Handlungsorganisation. Bern, Switzerland: Hans Huber. Schoenfeld, A. H. (1985). Mathematical Problem Solving. Orlando, FL: Academic Press. Sokol, S. M., & McCloskey, M. (1991). Cognitive mechanisms in calculation. In R. J. Sternberg & P. A. Frensch(Eds.), Complex problem solving: Principles and mechanisms (pp. 85-116). Hillsdale, NJ: Lawrence ErlbaumAssociates. Stanovich, K. E., & Cunningham, A. E. (1991). Reading as constrained reasoning. In R. J. Sternberg & P. A.Frensch (Eds.), Complex problem solving: Principles and mechanisms (pp. 3-60). Hillsdale, NJ: LawrenceErlbaum Associates. Sternberg, R. J. (1995). Conceptions of expertise in complex problem solving: A comparison of alternativeconceptions. In P. A. Frensch & J. Funke (Eds.), Complex problem solving: The European Perspective (pp.295-321). Hillsdale, NJ: Lawrence Erlbaum Associates. Sternberg, R. J., & Frensch, P. A. (Eds.). (1991). Complex problem solving: Principles and mechanisms.Hillsdale, NJ: Lawrence Erlbaum Associates.Problem solving 24 Strau, B. (1993). Konfundierungen beim Komplexen Problemlsen. Zum Einflu des Anteils der richtigenLsungen (ArL) auf das Problemlseverhalten in komplexen Situationen [Confoundations in complex problemsolving. On the influence of the degree of correct solutions on problem solving in complex situations]. Bonn,Germany: Holos. Strohschneider, S. (1991). Kein System von Systemen! Kommentar zu dem Aufsatz "Systemmerkmale alsDeterminanten des Umgangs mit dynamischen Systemen" von Joachim Funke [No system of systems! Reply tothe paper "System features as determinants of behavior in dynamic task environments" by Joachim Funke].Sprache & Kognition, 10, 109-113. Van Lehn, K. (1989). Problem solving and cognitive skill acquisition. In M. I. Posner (Ed.), Foundations ofcognitive science (pp. 527-579). Cambridge, MA: MIT Press. Voss, J. F., Wolfe, C. R., Lawrence, J. A., & Engle, R. A. (1991). From representation to decision: An analysis ofproblem solving in international relations. In R. J. Sternberg & P. A. Frensch (Eds.), Complex problem solving:Principles and mechanisms (pp. 119-158). Hillsdale, NJ: Lawrence Erlbaum Associates. Wagner, R. K. (1991). Managerial problem solving. In R. J. Sternberg & P. A. Frensch (Eds.), Complex problemsolving: Principles and mechanisms (pp. 159-183). Hillsdale, NJ: Lawrence Erlbaum Associates. Wisconsin Educational Media Association. (1993). "Information literacy: A position paper on informationproblem-solving." Madison, WI: WEMA Publications. (ED 376 817). (Portions adapted from Michigan StateBoard of Education's Position Paper on Information Processing Skills, 1992). Blanchard-Fields, F. (2007). "Everyday problem solving and emotion: An adult developmental perspective.Current Directions in Psychological Science". Current Directions in Psychological Science 16 (1): 2631. doi:10.1111/j.1467-8721.2007.00469.x (http:/ / dx. doi. org/ 10. 1111/ j. 1467-8721. 2007. 00469. x). Bransford, J. D., & Stein, B. S (1993). The ideal problem solver: A guide for improving thinking, learning, andcreativity (2nd ed.). Ghasempour,Z., Md Nor Bakar and Jahanshahloo G. R. (2013). Innovation in Teaching and Learning throughProblem Posing Tasks and Metacognitive Strategies (http:/ / naturalspublishing. com/ files/ published/vw6qz9526444la. pdf). Condell; Wade,Galway,McBride,Gormley,Brennan,Somasundram (2010). Springer Science+Business Media2010: 223233 http:/ / journals1. scholarsportal. info. myaccess. library. utoronto. ca/ tmp/7323702971461253957. pdf (http:/ / journals1. scholarsportal. info. myaccess. library. utoronto. ca/ tmp/7323702971461253957. pdf) |url= missing title (help). External links Computer skills for information problem-solving: Learning and teaching technology in context (http:/ / www.ericdigests. org/ 1996-4/ skills. htm) Problem solving - Elementary level (http:/ / wik. ed. uiuc. edu/ articles/ p/ r/ o/Problem_solving-Elementary_level_320d. html)Decision-making 25Decision-makingSample flowchart representing the decisionprocess to add a new article to Wikipedia.Decision making can be regarded as the cognitive process resulting inthe selection of a course of action among several alternative scenarios.Every decision making process produces a final choice. The output canbe an action or an opinion of choice.OverviewHuman performance in decision terms has been the subject of activeresearch from several perspectives. From a psychological perspective, it is necessary to examineindividual decisions in the context of a set of needs, preferences anindividual has and values they seek. From a cognitive perspective, the decision making process must beregarded as a continuous process integrated in the interaction with the environment. From a normative perspective, the analysis of individual decisions is concerned with the logic of decision makingand rationality and the invariant choice it leads to.Yet, at another level, it might be regarded as a problem solving activity which is terminated when a satisfactorysolution is reached. Therefore, decision making is a reasoning or emotional process which can be rational orirrational, can be based on explicit assumptions or tacit assumptions. Decisions are likely to be involuntary andfollowing the decision, we spend time analyzing the cost and benefits of that decision. This is known as "RationalChoice Theory," which encompasses the notion that we maximize benefits and minimize the costs.Some have argued that most decisions are made unconsciously. Jim Nightingale, Author of Think Smart-Act Smart,states that "we simply decide without thinking much about the decision process." In a controlled environment, suchas a classroom, instructors might try to encourage students to weigh pros and cons before making a decision. Thisstrategy is known as Franklin's Rule. Because such a rule requires time, cognitive resources and full access torelevant information about the decision, Franklin's Rule may not best describe how people naturally make theirdecisions. [citation needed]Logical decision making is an important part of all science-based professions, where specialists apply theirknowledge in a given area to make informed decisions. For example, medical decision making often involvesmaking a diagnosis and selecting an appropriate treatment. SomeWikipedia:Avoid weasel words research usingnaturalistic methods shows, however, that in situations with higher time pressure, higher stakes, or increasedambiguities, experts use intuitive decision making rather than structured approaches, following a recognition primeddecision approach to fit a set of indicators into the expert's experience and immediately arrive at a satisfactory courseof action without weighing alternatives. Recent robust decision efforts have formally integrated uncertainty into thedecision making process. However, decision analysis, recognized and included uncertainties with a structured andrationally justifiable method of decision making since its conception in 1964.A major part of decision making involves the analysis of a finite set of alternatives described in terms of evaluative criteria. Information Overload is when there is a substantial gap between the capacity of information and the ways we adapt. The overload of information can be related to problems processing and tasking, which impacts decision making.[1] These criteria may be benefit or cost in nature. Then the problem might be to rank these alternatives in terms of how attractive they are to the decision maker(s) when all the criteria are considered simultaneously. Another goal might be to just find the best alternative or to determine the relative total priority of each alternative (for instance, if alternatives represent projects competing for funds) when all the criteria are considered simultaneously. Solving such problems is the focus of multi-criteria decision analysis (MCDA) also known as multi-criteria decisionDecision-making 26making (MCDM). This area of decision making, although it is very old and has attracted the interest of manyresearchers and practitioners, is still highly debated as there are many MCDA / MCDM methods which may yieldvery different results when they are applied on exactly the same data. This leads to the formulation of a decisionmaking paradox.Rational and irrational decision makingIn economics, it is thought that if humans are rational and free to make their own decisions, then they would behaveaccording to the rational choice theory. This theory states that people make decisions by determining the likelihoodof a potential outcome, the value of the outcome and then multiplying the two. For example, with a 50% chance ofwinning $20 or a 100% chance of winning $10, people more likely to choose the first option.However, in reality, there are some factors that affect decision making abilities and cause people to make irrationaldecisions, one of them being availability bias. Availability bias is the tendency for some items that are more readilyavailable in memory to be judged as more frequently occurring. For example, someone who watches a lot of moviesabout terrorist attacks may think the frequency of terrorism to be higher than it actually is.Information overloadInformation overload is "a gap between the volume of information and the tools we need to assimilate it." It isproven in some studies Wikipedia:Avoid weasel words that the more information overload, the worse the quality ofdecisions made. There are 5 factors concerning information overload: Personal Information Factors: personal qualifications, experiences, attitudes etc. Information Characteristics: information quality, quantity and frequency Tasks and Process: standardized procedures or methods Organizational Design: organizations' cooperation, processing capacity and organization relationship Information Technology: IT management, and general technologyHall, Ariss & Todorov with an assistant Rashar phinyor (2007) described an illusion of knowledge, meaning that asindividuals encounter too much knowledge it actually interferes with their ability to make rational decisions.[2]Problem analysis vs decision makingIt is important to differentiate between problem analysis and decision making. The concepts are completely separatefrom one another. Traditionally it is argued that problem analysis must be done first, so that the information gatheredin that process may be used towards decision making.Problem analysis Analyze performance, what should the results be against what they actually are Problems are merely deviations from performance standards Problem must be precisely identified and described Problems are caused by a change from a distinctive feature Something can always be used to distinguish between what has and hasn't been affected by a cause Causes to problems can be deducted from relevant changes found in analyzing the problem Most likely cause to a problem is the one that exactly explains all the factsDecision making Objectives must first be established Objectives must be classified and placed in order of importance Alternative actions must be developed The alternative must be evaluated against all the objectivesDecision-making 27 The alternative that is able to achieve all the objectives is the tentative decision The tentative decision is evaluated for more possible consequences The decisive actions are taken, and additional actions are taken to prevent any adverse consequences frombecoming problems and starting both systems (problem analysis and decision making) all over again There are steps that are generally followed that result in a decision model that can be used to determine an optimalproduction plan. In a situation featuring conflict, role-playing is helpful for predicting decisions to be made by involved parties.Decision planningMaking a decision without planning is fairly common, but does not often end well. Planning allows for decisions tobe made comfortably and in a smart way. Planning makes decision making a lot more simple than it is.Decision will get four benefits out of planning: 1. Planning give chance to the establishment of independent goals. Itis a conscious and directed series of choices. 2. Planning provides a standard of measurement. It is a measurement ofwhether you are going towards or further away from your goal. 3. Planning converts values to action. You thinktwice about the plan and decide what will help advance your plan best. 4. Planning allows for limited resources to becommitted in an orderly way. Always govern the use of what is limited to you. (e.g. money, time, etc.)Analysis paralysisAnalysis paralysis is the state of over-analyzing (or over-thinking) a situation, or citing sources, so that a decision oraction is never taken, in effect paralyzing the outcome.Everyday techniquesDecision making techniques can be separated into two broad categories: Group decision making techniques andindividual decision making techniques: Group decision making techniques Consensus decision-making tries to avoid "winners" and "losers". Consensus requires that a majority approve agiven course of action, but that the minority agree to go along with the course of action. In other words, if theminority opposes the course of action, consensus requires that the course of action be modified to removeobjectionable features. Voting-based methods Range voting lets each member score one or more of the available options. The option with the highestaverage is chosen. This method has experimentally been shown to produce the lowest Bayesian regretamong common voting methods, even when voters are strategic.[citation needed] Majority requires support from more than 50% of the members of the group. Thus, the bar for action islower than with unanimity and a group of "losers" is implicit to this rule.[citation needed] Plurality, where the largest block in a group decides, even if it falls short of a majority. Delphi method is structured communication technique for groups, originally developed for collaborativeforecasting but has also been used for policy making Dotmocracy is a facilitation method that relies on the use of special forms called Dotmocracy Sheets to allowlarge groups to collectively brainstorm and recognize agreement on an unlimited number of ideas they haveauthored. Individual decision making techniques Pros and cons: listing the advantages and disadvantages of each option, popularized by Plato and BenjaminFranklin.[3] Contrast the costs and benefits of all alternatives. Also called Rational decision making. Simple prioritization: choosing the alternative with the highest probability-weighted utility for each alternative(see Decision analysis) Satisficing: examining alternatives only until an acceptable one is found.Decision-making 28 Elimination by Aspects: choosing between alternatives using Mathematical psychology Technique wasintroduced by Amos Tversky in 1972. It is a covert elimination process that involves comparing all availablealternatives by aspects. The decision-maker chooses an aspect; any alternatives without that aspect areeliminated. The decision-maker repeats this process with as many aspects as needed until there remains onlyone alternative Preference Trees: In 1979 Amos Tversky and Shmuel Sattach updated the elimination by aspects technique bypresenting a more ordered and structured way of comparing the available alternatives. This techniquecompared the alternatives by presenting the aspects in a decided and sequential order. It became a morehierarchical system in which the aspects are ordered from general to specific Acquiesce to a person in authority or an "expert", just following orders Flipism: flipping a coin, cutting a deck of playing cards, and other random or coincidence methods[4] Prayer, tarot cards, astrology, augurs, revelation, or other forms of divination Taking the most opposite action compared to the advice of mistrusted authorities (parents, police officers,partners ...) Opportunity cost: calculating the opportunity cost of each options and decide the decision Bureaucratic: set up criteria for automated decisions Political: negotiate choices among interest groups Participative decision-making (PDM): a methodology in which a single decision maker opens up the decisionmaking process to a group for a collaborative effort in order to take advantage of additional input. Use of a structured decision making methodIndividual decision making techniques can often be applied by a group as part of a group decision making technique.A need to use software for the decision making process is emerging for individuals and businesses. This is due toincreasing decision complexity, and increased requirements to consider additional stakeholders, categories, elements,or factors that impact important decisions.Decision making stagesDeveloped by B. Aubrey Fisher, there are four stages that should be involved in all group decision making. Thesestages, or sometimes called phases, are important for the decision making process to beginOrientation stage This phase is where members meet for the first time and start to get to know each other.Conflict stage Once group members become familiar with each other, disputes, little fights and arguments occur.Group members eventually work it out.Emergence stage The group begins to clear up vague opinions by talking about them.Reinforcement stage Members finally make a decision, while justifying themselves that it was the right decision.It is said that critical norms in a group improves the quality of decisions, while the majority of opinions (calledconsensus norms) do not. This is due to collaboration between one another, and when group members get used to,and familiar with, each other, they will tend to argue and create more of a dispute to agree upon one decision. Thisdoes not mean that all group members fully agree they may not want argue further just to be liked by other groupmembers or to "fit in".Decision-making 29Decision making stepsEach step in the decision making process may include social, cognitive and cultural obstacles to successfullynegotiating dilemmas. It has been suggested that becoming more aware of these obstacles allows one to betteranticipate and overcome them.[5] The Arkansas Program presents eight stages of moral decision making based on thework of James Rest:1.1. Establishing community: creating and nurturing the relationships, norms, and procedures that will influence howproblems are understood and communicated. This stage takes place prior to and during a moral dilemma2.2. Perception: recognizing that a problem exists3.3. Interpretation: identifying competing explanations for the problem, and evaluating the drivers behind thoseinterpretations4.4. Judgment: sifting through various possible actions or responses and determining which is more justifiable5.5. Motivation: examining the competing commitments which may distract from a more moral course of action andthen prioritizing and committing to moral values over other personal, institutional or social values6.6. Action: following through with action that supports the more justified decision. Integrity is supported by theability to overcome distractions and obstacles, developing implementing skills, and ego strength7.7. Reflection in action8.8. Reflection on actionOther decision making processes have also been proposed. One such process, proposed by Dr. Pam Brown ofSingleton Hospital in Swansea, Wales, breaks decision making down into seven steps:1.1. Outline your goal and outcome.2.2. Gather data.3.3. Develop alternatives (i.e., brainstorming)4.4. List pros and cons of each alternative.5.5. Make the decision.6.6. Immediately take action to implement it.7.7. Learn from and reflect on the decision.Cognitive and personal biasesBiases can creep into our decision making processes. Many different people have made a decision about the samequestion (e.g. "Should I have a doctor look at this troubling breast cancer symptom I've discovered?" "Why did Iignore the evidence that the project was going over budget?") and then craft potential cognitive interventions aimedat improving decision making outcomes.Here is a list of commonly debated biases in judgment and decision making. Selective search for evidence (a.k.a. Confirmation bias in psychology) (Scott Plous, 1993) People tend to bewilling to gather facts that support certain conclusions but disregard other facts that support different conclusions.Individuals who are highly defensive in this manner show significantly greater left prefrontal cortex activity asmeasured by EEG than do less defensive individuals. Premature termination of search for evidence People tend to accept the first alternative that looks like it mightwork. Cognitive inertia Unwillingness to change existing thought patterns in the face of new circumstances. Selective perception We actively screen-out information that we do not think is important. (See prejudice.) Inone demonstration of this effect, discounting of arguments with which one disagrees (by judging them as untrueor irrelevant) was decreased by selective activation of right prefrontal cortex. Wishful thinking a tendency to want to see things in a positive light, which can distort perception and thinking.Decision-making 30 Choice-supportive bias occurs when people distort their memories of chosen and rejected options to make thechosen options seem more attractive. Recency People tend to place more attention on more recent information and either ignore or forget moredistant information (see semantic priming). The opposite effect in the first set of data or other information istermed primacy effect.[6] Repetition bias A willingness to believe what one has been told most often and by the greatest number ofdifferent sources. Anchoring and adjustment Decisions are unduly influenced by initial information that shapes our view ofsubsequent information. Group think peer pressure to conform to the opinions held by the group. Source credibility bias A tendency to reject a person's statement on the basis of a bias against the person,organization, or group to which the person belongs. People preferentially accept statement by others that they like(see prejudice). Incremental decision making and escalating commitment We look at a decision as a small step in a process andthis tends to perpetuate a series of similar decisions. This can be contrasted with zero-based decision making (seeslippery slope). Attribution asymmetry People tend to attribute their own success to internal factors, including abilities andtalents, but explain their failures in terms of external factors such as bad luck. The reverse bias is shown whenpeople explain others' success or failure. Role fulfillment A tendency to conform to others' decision-making expectations. Underestimating uncertainty and the illusion of control People tend to underestimate future uncertainty becauseof a tendency to believe they have more control over events than they really do. Framing bias is best avoided by using numeracy with absolute measures of efficacy. Sunk-cost fallacy is a specific type of framing effect that affects decision making. It involves an individualmaking a decision about a current situation based on what they have previously invested in the situation.[7] Apossible example to this would be an individual that is refraining from dropping a class that that they are mostlikely to fail, due to the fact that they feel as though they have done so much work in the course thus far. Prospect theoryinvolves the idea that when faced with a decision making event, an individual is more likely totake on a risk when evaluating potential losses, and are more likely to avoid risks when evaluating potential gains.This can influence one's decision making depending if the situation entails a threat, or opportunity.[8]Reference class forecasting was developed to eliminate or reduce cognitive biases in decision making.Post-decision analysisEvaluation and analysis of past decisions is complementary to decision making; see also mental accounting andPostmortem documentation.Cognitive stylesInfluence of Myers-Briggs typeAccording to behavioralist Isabel Briggs Myers, a person's decision making process depends to a significant degree on their cognitive style.[9] Myers developed a set of four bi-polar dimensions, called the Myers-Briggs Type Indicator (MBTI). The terminal points on these dimensions are: thinking and feeling; extroversion and introversion; judgment and perception; and sensing and intuition. She claimed that a person's decision making style correlates well with how they score on these four dimensions. For example, someone who scored near the thinking, extroversion, sensing, and judgment ends of the dimensions would tend to have a logical, analytical, objective, critical, and empirical decision making style. However, someWikipedia:Avoid weasel words psychologists say that the MBTIDecision-making 31lacks reliability and validity and is poorly constructed.Other studies suggest that these national or cross-cultural differences exist across entire societies. For example,Maris Martinsons has found that American, Japanese and Chinese business leaders each exhibit a distinctive nationalstyle of decision making.[10]Optimizing vs. satisficingHerbert A. Simon coined the phrase "bounded rationality" to express the idea that human decision making is limitedby available information, available time, and the information-processing ability of the mind. Simon also defined twocognitive styles: maximizers try to make an optimal decision, whereas satisficers simply try to find a solution that is"good enough". Maximizers tend to take longer making decisions due to the need to maximize performance acrossall variables and make tradeoffs carefully; they also tend to more often regret their decisions (perhaps because theyare more able than satisficers to recognise that a decision turned out to be sub-optimal).Combinatorial vs. positionalStyles and methods of decision making were elaborated by the founder of Predispositioning Theory, AronKatsenelinboigen. In his analysis on styles and methods Katsenelinboigen referred to the game of chess, saying thatchess does disclose various methods of operation, notably the creation of predispositionmethods which may beapplicable to other, more complex systems.[11]In his book Katsenelinboigen states that apart from the methods (reactive and selective) and sub-methods(randomization, predispositioning, programming), there are two major styles positional and combinational. Bothstyles are utilized in the game of chess. According to Katsenelinboigen, the two styles reflect two basic approachesto the uncertainty: deterministic (combinational style) and indeterministic (positional style). Katsenelinboigensdefinition of the two styles are the following.The combinational style is characterized by a very narrow, clearly defined, primarily material goal, and a program that links the initial position with the final outcome.In defining the combinational style in chess, Katsenelinboigen writes:The combinational style features a clearly formulated limited objective, namely the capture of material (the mainconstituent element of a chess position). The objective is implemented via a well-defined, and in some cases, uniquesequence of moves aimed at reaching the set goal. As a rule, this sequence leaves no options for the opponent.Finding a combinational objective allows the player to focus all his energies on efficient execution, that is, theplayers analysis may be limited to the pieces directly partaking in the combination. This approach is the crux of thecombination and the combinational style of play.The positional style is distinguished by a positional goal and a formation of semi-complete linkages between the initial step and final outcome.Unlike the combinational player, the positional player is occupied, first and foremost, with the elaboration of theposition that will allow him to develop in the unknown future. In playing the positional style, the player mustevaluate relational and material parameters as independent variables. ... The positional style gives the player theopportunity to develop a position until it becomes pregnant with a combination. However, the combination is not thefinal goal of the positional playerit helps him to achieve the desirable, keeping in mind a predisposition for thefuture development. The Pyrrhic victory is the best example of ones inability to think positionally."[12]The positional style serves to create a predisposition to the future development of the position; induce the environment in a certain way;Decision-making 32 absorb an unexpected outcome in ones favor; avoid the negative aspects of unexpected outcomes.Katsenelinboigen writes:"As the game progressed and defense became more sophisticated the combinational style of play declined. ... Thepositional style of chess does not eliminate the combinational one with its attempt to see the entire program of actionin advance. The positional style merely prepares the transformation to a combination when the latter becomesfeasible.[13]Neuroscience perspectiveThe anterior cingulate cortex (ACC), orbitofrontal cortex (and the overlapping ventromedial prefrontal cortex) arebrain regions involved in decision making processes. A recent neuroimaging study[14] found distinctive patterns ofneural activation in these regions depending on whether decisions were made on the basis of personal volition orfollowing directions from someone else. Patients with damage to the ventromedial prefrontal cortex have difficultymaking advantageous decisions.A recent study[15] of a Two-alternative forced choice task involving Rhesus monkeys found that neurons in theparietal cortex not only represent the formation of a decision but also signal the degree of certainty (or "confidence")associated with the decision. Another recent study[16] found that lesions to the ACC in the macaque resulted inimpaired decision making in the long run of reinforcement guided tasks suggesting that the ACC may be involved inevaluating past reinforcement information and guiding future action.Emotion appears to aid the decision making process: Decision making often occurs in the face of uncertainty aboutwhether one's choices will lead to benefit or harm (see also Risk). The somatic-marker hypothesis is aneurobiological theory of how decisions are made in the face of uncertain outcome. This theory holds that suchdecisions are aided by emotions, in the form of bodily states, that are elicited during the deliberation of futureconsequences and that mark different options for behavior as being advantageous or disadvantageous. This processinvolves an interplay between neural systems that elicit emotional/bodily states and neural systems that map theseemotional/bodily states.[17]Although it is unclear whether the studies generalize to all processing, subconscious processes have been implicatedin the initiation of conscious volitional movements. See the Neuroscience of free will.References[1][1] Kutty, Ambalika D., and Himanshu Kumar Shee. "Too much info!" Monash Business Review 3.3 (2007): 8+. Academic OneFile. Web. 3Mar. 2013.[2] Hall, C.C., Ariss, L. & Todorov, A. 2007. The illusion of knowledge: When more information reduces accuracy and increases confidence.Organizational Behavior and Human Decision Processes, 103: 277-290[3] Benjamin Franklin's 1772 letter to Joseph Priestley - (http:/ / www. procon. org/ view. background-resource.php?resourceID=1474)[4] Random Decision Making App for Windows 8 devices (http:/ / decisionmakingwheel. com)[5] The Role of Learning Theory in Building Effective College Ethics Curricula (http:/ / journals. naspa. org/ cgi/ viewcontent.cgi?article=1088& context=jcc). Pijanowski. 2009, p.6. Retrieved 2012-01-12.[6][6] Plous, 1993[7] Schacter, Gilbert, Wegner (2011)Psychology (2nd Edition), page 372, Worth Publishers[8] Schacter, Gilbert, Wegner (2011)Psychology (2nd Edition), page 373, Worth Publishers[9] Myers, I. (1962) Introduction to Type: A description of the theory and applications of the Myers-Briggs type indicator, ConsultingPsychologists Press, Palo Alto Ca., 1962.[10] Martinsons, Maris G., Comparing the Decision Styles of American, Chinese and Japanese Business Leaders. Best Paper Proceedings ofAcademy of Management Meetings, Washington, DC, August 2001 (http:/ / papers. ssrn. com/ sol3/ papers. cfm?abstract_id=952292)[11][11] Katsenelinboigen, Aron. The Concept of Indeterminism and Its Applications: Economics, Social Systems, Ethics, Artificial Intelligence, andAesthetics Praeger: Westport, Connecticut, 1997, p.6Decision-making 33[12] V. Ulea, The Concept of Dramatic Genre and The Comedy of A New Type. Chess, Literature, and Film. Southern Illinois University Press,2002, pp. 1718[13][13] Selected Topics in Indeterministic Systems Intersystems Publications: California, 1989, p. 21[14] Interactions between decision making and performance monitoring within prefrontal cortex (http:/ / www. nature. com/ cgi-taf/ DynaPage.taf?file=/ neuro/ journal/ v7/ n11/ abs/ nn1339. html)[15] Roozbeh Kiani and Michael N. Shadlen, Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (http:/ /www. sciencemag. org/ cgi/ content/ abstract/ 324/ 5928/ 759)[16] Kennerly, et al. (2006) (http:/ / www. nature. com/ neuro/ journal/ v9/ n7/ abs/ nn1724. html)[17] Nasir Naqvi, et al. " The Role of Emotion in Decision Making: A Cognitive Neuroscience Perspective (http:/ / www. doi/ abs/ 10. 1111/ j. 1467-8721. 2006. 00448. x?cookieSet=1& journalCode=cdir)", Current Directions in Psychological Science,Library resources aboutDecision making Resources in your library (http:/ / tools. wmflabs. org/ ftl/ cgi-bin/ ftl?st=wp& su=Decision-making)Logical reasoningInductive and deductive reasoningInformally, two kinds of logical reasoning can be distinguished inaddition to formal deduction: induction and abduction. Given aprecondition or premise, a conclusion or logical consequence and arule or material conditional that implies the conclusion given theprecondition, one can explain that: Deductive reasoning determines whether the truth of a conclusioncan be determined for that rule, based solely on the truth of thepremises. Example: "When it rains, things outside get wet. Thegrass is outside, therefore: when it rains, the grass gets wet."Mathematical logic and philosophical logic are commonlyassociated with this style of reasoning. Inductive reasoning attempts to support a determination of the rule. It hypothesizes a rule after numerousexamples are taken to be a conclusion that follows from a precondition in terms of such a rule. Example: "Thegrass got wet numerous times when it rained, therefore: the grass always gets wet when it rains." While they maybe persuasive, these arguments are not deductively valid, see the problem of induction. Science is associated withthis type of reasoning. Abductive reasoning selects a cogent set of preconditions. Given a true conclusion and a rule, it attempts toselect some possible premises that, if true also, can support the conclusion, though not uniquely. Example: "Whenit rains, the grass gets wet. The grass is outside and nothing outside is dry, therefore: maybe it rained."Diagnosticians and detectives are commonly associated with this type of reasoning.References T. Menzies. Applications of Abduction: Knowledge-Level Modeling. November 1996Article Sources and Contributors 34Article Sources and ContributorsMetacognition Source: Contributors: Aboluay, Adrian 1001, Ageekgal, Al001, Alan Pascoe, Alansohn, Allen3, Anchoress,AndreasPDemetriou, AndySimpson, Anthony Appleyard, Apeeze, Aps0626, Artbeck, AstroHurricane001, Baylaurel, Bearian, Beno1000, Blinky, the Magician, Bluepaladin, Caesura, Ciphergoth,CuriousOne, DBSBHAT, Darekun, DirectorG, Doorsrocklikerocks, Doulos Christos, Dreadstar, Dream of Nyx, Dysmorodrepanis, Ebear422, ElliottHoffman, Enigma55, Esn,EyebrowOnVacation, Fasten, Flyer22, Fraggle81, FrankTobia, Fuzzform, Gaberdine2, Gabriel Kielland, Gilderien, GwydionM, Hairy Dude, Haymouse, Hnbaofeng, Homonihilis, Ingroc,IvanLanin, JaGa, Jheald, Jlcass, Jm34harvey, John Abbe, Jprg1966, Julia93, Jusdafax, Karenjc, Kavedian, Khazar2, Kiefer.Wolfowitz, Kim Dent-Brown, Krellis, LarryQ, Larryjun, Letranova,Leuko, Ljz0010, Lova Falk, Lycurgus, MasonNation, Master shepherd, Maxim, Mingming, MistyMorn, Mlang.Finn, Moondyne, MrStrathmore, Myanw, Nabeth, Nagelfar, Nesbit, Nn123645,NymphadoraTonks, Ombudsman, Ott, PDH, Paulscho, Pearle, Pegasi195, Pfhorrest, Pgr94, Phdarts, Pinethicket, Profdhw, Proofreader77, Pseudomonas, Pundit, Rama, Ranveig, Rculatta,RedAlgorithm, Reinhardheydt, RichardF, Riley Huntley, Rjwilmsi, SMC, Seth Nimbosa, Sivar, Spangadoola, Specs112, Spiel496, Steve2011, Sydbarrett74, T@Di, THEN WHO WAS PHONE?,Tan wei ren, TennysonXII, Thenext20feet, Tim1357, Tobias Bergemann, Tomchiukc, Tomisti, Trickstar, Trusilver, Tzepish, Ulric1313, Vanischenu, Vanished User 1004, Vashti, Vaughn42sv,Viriditas, Wareh, Widr, Wikiloop, Wikipelli, Woohookitty, Xyzzyplugh, Yintan, ZachCrichfield, ZacharyTaylorJohnson, Zahra241, Zeimusu, 314 anonymous editsDeductive reasoning Source: Contributors: 2D, 8ty3hree, AaronY, Abdullais4u, AceMyth, Aequo, Agentnj, Aim Here, Allens,Alphachimp,, Andrewaskew, Archenzo, Arno Matthias, Azcolvin429, BD2412, Badelmann, Beclp, BenKovitz, BenMcLean, Bfeylia, 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Langner, Kborer, Kenji000, Kenyon, Kevinb, Kimberleyporter, Kirrages, Koavf, Kusyadi, Kwork2, LactoseTI, Lallallal,Leoremy, Lethe, Lord Bane, Lugia2453, Luke13579, Lumos3, MARVINWAGNER, MCTales, MaTT, Mankar Camoran, Marek69, Mauriziogeri2013, Meldor, Mhss, Michael Hardy, MindShifts,Mindmatrix, Mjhy0926, Mo ainm, Mohehab, MostlyListening, Mr.Z-man, Mrwojo, Msviolone, Mygerardromance, N4nojohn, Nasukaren, Neelix, NeuroBells123, Neverquick, Nichole773,Nightscream, Ninjagoat, Nishkid64, Noaa, O.Koslowski, OAC, Octahedron80, Oligocene, Oliver Simon, Oncenawhile, Ordning, OwenX, Oxford Dictionary, Paradoctor, Pasixxxx,PaulKincaidSmith, Pbroks13, Penagate, PhilHibbs, Philogo, Philosophy.dude, Piotrus, Pomte, Prbclj25, PresMan, Probios, Psiphiorg, Q1w2e3, Qwertytastic, R3m0t, Richard84041, RickNorwood, Rohit nit, Romanm, RossF18, RowanEvans,, Rucha58, Rupes, Rushbugled13, Ruud Koot, Ryanmcdaniel, S h i v a (Visnu), SEASONnmr, Saeed.Veradi, Sardanaphalus,ScienceApe, Sfmammamia, Sgilmore10, Shadro, ShangTsung87, Shawn in Montreal, SidP, Skc0001, Skomorokh, Snowded, Sopidex, SpikeToronto, Stbalbach, Stephen, Stephensuleeman,Stepp-Wulf, Stjeanp, Strikingstar, Svick, TakuyaMurata, Tangotango, Tariqabjotu, Technopat, TedPavlic, Teemu Leisti, Tesseract2, The Cunctator, The Ungovernable Force, The great kawa,TheGeomaster, TheNightFly, Thingg, Thunderboltz, Tide rolls, Tideflat, Tobias Bergemann, Toby Bartels, Tparameter, TriGen, Troy 07, Tziemer991, Van helsing, Vanished User 1004, Velho,Vinsfan368, ViolinGirl, VogelsangLorenzo, Wayne Slam, Widr, WikHead, Wiki13, WookieInHeat, Woz2, Xephras, YAZASHI, Youandme, Zarex, Zenwhat, ZephyrAnycon, , 496anonymous editsInductive reasoning Source: Contributors: 24ip, 90b56587, A157247, A8UDI, Aaronbrick, Abdullais4u, Albertttt, Alfakim,Anarchangel, Ancheta Wis, Andeggs,, Andres, Andy Marchbanks, Anthonzi, Antonielly, Archer3, Arno Matthias, Aubreybardo, AxelBoldt, Bagatelle, Banno, Bemoeial, Bender235,Bo Jacoby, BoNoMoJo (old), Bob 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useroij8h435jweih3, Velho, Voidvector, WLU, Wagino 20100516, Wandwiki, Watchpup32, Widr, Wikipediarules2221, Will.Oliver, Wolfgang Kufner, Yuckfoo, Zach425, Zigger, 505 anonymouseditsProblem solving Source: Contributors: 2D, 2spyra, AbsolutDan, Acdx, Action potential, Adapt, AdjustShift, Afewum, Agbormbai,Ahoerstemeier, Aleksd, Alex Kosorukoff, Alex Rio Brazil, Ancheta Wis, Andrew Gray, AndriuZ, Angr, Anna512, Antaeus Feldspar, Ap, Arjun G. 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