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Decision making theories

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This is a series of Capacity Building documents that was prepared by the Sudanese Youth Leadership Development Program.
هذه مجموعة من المقالات في مجالات تدريبية متعددة مناسبة للجمعيات الطوعية تم تطويرها بين عامي 2003-2008 للبرنامج السوداني لإعداد القيادات الشبابية

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Decision making theories

  1. 1. DECISION MAKING THEORY Implications for Academic Advising Tina Brazil (TAB291@gmail.com) Jim Levin (JL7@psu.edu)
  2. 2. Importance: Curriculum of Academic Advising • “Academic Advising …….. This curriculum includes …….. decision-making ……..” (NACADA, 2006). • “ … use complex information …. reach decisions … “ (NACADA, 2006).
  3. 3. Definition • Rational decisions are ones that advances the welfare of the decision maker effectively and logically based on everything the decision maker knows and feels (Brown, 2005, p. 54) • Criteria for rational decisions (Hastie & Dawes, 2001, p.18) – It is based on the decision maker’s current assets. Assets include not only money, but physiological state, psychological capacities, social relationships, and feelings. – It is based on the possible consequences of the choice. – When these consequences are uncertain, their likelihood is evaluated according to the basic rules of probability theory. – It is a choice that is adaptive within the constraints of those probabilities and the values or satisfactions associated with each of the possible consequences of choice.
  4. 4. Problems with Qualitative Decision Making Methods • Heuristics: speculative formulation serving as a guide in the investigation or solution of a problem (Keren and Teigen, 2004) – Representation bias: decision making by recalling a memory or experience that is similar to the present decision making experience – Availability bias: decision making by the primacy and/or by predicting “easily conceivable outcomes” – Anchor & adjustment bias: decision making by what is familiar and conceivable
  5. 5. Models • Goals, Options and Outcomes (GOO) • The Personalist Approach • Lens Model • Simple Utility Equation • Additive Linear Multi-Attribute Utility Theory (MAUT)
  6. 6. Definition of “Utility” • “The consumption utility of an option is broadly defined here as the benefit the option delivers.” (Hsee, 1999, p. 555) • Furthermore, it is assumed that the decision maker should choose the option that delivers the greatest utility or benefit. (Hsee, 1999) • When making decisions, we think about what option will derive the highest utility. (Hastie and Dawes, 2001, p. 200)
  7. 7. Goals, Options, and Outcomes • What do I want? (goals) • What can I do? (options) • What might happen? (outcomes) (Brown, 2005, p. 9) • In Practice – Goals & options can be listed – Difficult to predict outcomes • Quantitative methods can be used (simple probability theory) – Research indicates that quantitative methods predict outcomes better than qualitative methods (Hastie & Dawes, 2001)
  8. 8. The Personalist Approach (first approximation of quantifiable decision making OPTIONS OUTCOMES Science Liberal Arts Class enjoyment - - + + + + Academic success + + + + + Career security + + - - Total (algebraic) + + + + + + + Fig 1. The pluses and minuses are assigned on an arbitrary scale decided by the decision maker. (Brown, 2005)
  9. 9. Lens Model Concept • The decision maker is trying to see a “distal” true state of something through a “proximal lens” of cues. These cues represent information or characteristics that the decision maker uses to make a decision (Hastie and Dawes, 2001) • An algebraic model of probability that measures and assigns a scaled weight to the importance of each piece of information (cue) available to the decision maker (Hastie and Dawes, 2001) • Research: “experts correctly select variables that are important in making predictions, but that a probability model combines these variables in a way that is superior to the global judgments of these very same experts.” (Hastie and Dawes, 2004, p. 58) • Probability models have been derived from the Lens Model Concept.
  10. 10. Simple Utility Equation • Decision tree in which each option represents a major branch, and from each branch stems the possible outcomes. Each of these outcomes is assigned a specific quantitative probability so that the sum of the outcomes stemming from one choice adds up to 1, or 100%. The probability for each outcome is multiplied by an assigned number that represents how the decision maker would feel about that outcome (Hastie and Dawes, 2001) • Utility = Σ (probabilityoutcome x valueoutcome)
  11. 11. Simple Utility Equation study play pass .2 fail .8 pass .7 fail .3 Value prob x value utility -100 -.3 .4 +100 .7 -100 -.8 -.6 +100 .2 Figure 2. Here, the two options are study and play. In this case, the utility of option “study” has a much higher utility value to the decision maker than does play. Utility = Σ (probabilityoutcome x valueoutcome)
  12. 12. Additive Linear Multi-Attribute Utility Theory (MAUT) • MAUT weighs all of the attributes and scales the attributes by importance to the decision maker. Each option is considered by assigning a scaled value to that option-attribute, according to its importance, and then adding up all of the scaled option-attributes to obtain the utility value for that option (Hastie and Dawes, 2001)
  13. 13. Example of MAUT • To predict the probability of a student graduating from ENGR, Dr. James Levin constructed a probability model (logic regressions) that used several quantitative and qualitative (which were quantified) student criteria as inputs, assigned a scaled value to each criterion, and produced an output that gave the probability of that student graduating from ENGR. (Levin & Wyckoff, 1998) • Similar models are in progress for SC (Levin, 2007)
  14. 14. Additive Linear Multi-Attribute Utility Theory (MAUT): ENGINEERING • Grad Engr/Not Grad Engr = -3.8 -.02 x 10 (nspts) -.01 x 520(satv) +.69 x 3.00(hsgpa) +.08 x 25(alg) +.07 x 12(chem-s) + .28 x 1(reas-g) +.20 x 1(gen) = .236 • Odds of Grad Engr/Not Grad Engr = e.236 = 2.72.236 = 1.27/1 • Probability = 1.27/2.27 = 56%
  15. 15. MAUT Example: SC   ALL LIFE MATH PHYSICAL M140_score         M40_score     0.3735   -0.055 CHEM score .0759  (<.0001) 0.0798   0.1282 (<.0001) -0.0013 ENGL score -0176  (.0231)       NONSCIpts -.0136 (<.0001) -0.0173   -0.0161 (<.0001) (<0.0493) CHEM_ATT   0.1555     -0.0819 MATH_ATT .1818  (.0429) 0.1678     -0.0776 PHYS_ATT  
  16. 16. MAUT Example cont: SC BIOL_ATT     -0.7194   -0.0358 KNOW_ALOT         KNOW_SOME .8433  (.0138)       CERTAIN -.2241  (.0929)       COMPLETELY  CERTAIN .2727  (.0171)   1.7525   -0.0104 Study_hs .0142  (.0138)     0.0288 -0.0469 Study_psu         Gender         HS_GPA .8618  (<.0001) 0.9413 2.1597   (<.0001) -0.0111 SAT_VERB .0022 (.0111)     0.0033 -0.0565 SAT_MATH         REMEDIAL_MATH -1.2080 (<.0001) -1.1434   -1.3727 (<.0001) -0.0179 BASIC MATH SCORE        
  17. 17. References • Brown, Rex, Rational Choice and Judgment: Decision Analysis for the Decider, John Wiley & Sons, Inc., Hoboken, New Jersey, 2005. • Hastie, Reid and Robyn M. Dawes, Rational Choice in an Uncertain World: The Psychology of Judgment and Decision Making, Sage Publications, Thousand Oaks, California, 2001. • Hsee, Christopher K. (1999). Value Seeking and Prediction-Decision Inconsistency: Why Don’t People Take What They Predict They’ll Like the Most? Psychonomic Bulletin & Review, 6 (4), 555-561. • Keren, Gideon and Karl H. Teigen, “Yet Another Look at the Heuristics and Biases Approach.” Blackwell Handbook of Judgment and Decision Making, Blackwell Publishing Ltd., Malden, MA, 2004. • Levin, James and Wykoff, Jack,(1998). Effective Advising: Identifying Students Most Likely to Persist and Succeed in Engineering. Engineering Education, 75(11), 178-182. • Levin, James, (2007). Effective Advising: Identifying Students Most Likely to Persist and Succeed in Science in progress. • NACADA: National Academic Advising Association. (2006). NACADA concept of academic advising. Retrieved 5/2/07 from http://www.nacada.ksu.edu/Clearinghouse/AdvisingIssues/Concept-Advising.htm

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