COMPLEX PROBLEMS CLASS 3

1. COMPLEX PROBLEMSCLASS 3 Heuristics to the Rescue Alternatives to Purely Analytical Problem-Solving Methods

2. Heuristics as a Problem • Cognitive Biases -- “Implicit Heuristics” • Most of the early work on heuristics focused on showing poorly understood use of heuristics when thinking/using analytical methods (Kahneman & Tversky) -- • Strong Priors • Unwarranted Analogies • Representative Bias • Myopia • Control ...

3. Heuristics as a Problem-Solving Tool • “Heuristic” • helping to discover or learn; a method of education [learning] or computer programming in which the pupil or machine proceeds along empirical lines, using rules of thumb, to find solutions or answers (New World Dictionary, 2nd Ed.) • In this broad & positive sense, heuristics are problem- solving methods using mental shortcuts, trial & error, ... • Rules of Thumb • Empirical Searches & exploratory experiments (Trial & Error) • Intuition & experience

4. Non-analytic Based Disciplines • Does not imply lack of intelligent thought or complete absence of analytical methods, just not the core of problem solving • Significant Heuristic Content or Method • Biomedicine • Cognitive Psychology • Computer Science, Engineering, Stats, Math ...

5. Contrasting Analytical & Heuristic Methods • Math Example • Find point where y = 2x2 - 10x reaches a minimum • Analytical: Find derivative, set equal to zero and solve for x (dy/dx = 2x - 10); (0 = 2x - 10); (x = 5) • Heuristic: Numerical Search (trial & error): plug-in values for x until you find the minimum point • in case above, analytical approach more efficient • if equation not easily solved with analytics, numerical search more efficient or may be only possible

6. Heuristics in Analytics • Prior Class Analytical Toolkit • Analogy • Solving in parts • Backward-Forward • Transforming into known problem • Generalizing from specific solution • Although now integral part of analytical problem-solving techniques, these are really rules of thumb (heuristics) that people tried over time; they became widely known because they worked

7. Experimental Evidence on Heuristics • Bargaining & Ultimatum Game: • Fixed sum to split between Player A & Player B ; A makes offer, if B rejects, game ends-no deal; if B accepts, deal made • Analytics: A offer minimum, B accept • Experiments: • Few offers below 75:25 split, if made, rejected • Reasons: fear rejection, fairness so use “heuristics” (intuition …) • Extensions: time limits,multiple rounds, … • people who quickly agreed on 50:50 did the best • people who tried for very uneven results or bickered over small differences did the worst • Bottom Line? • Where “norms” are involved that are not easily model in analytics, using “intuition” can improve on pure analytics

8. Experimental Economics Evidence on Heuristics • “Focal Points” -- rule of thumb (heuristic) solutions to difficult strategic decisions • Divide the cities (location game): Harvard & Stanford MBA student pairs separately choose from list of cities with certain limitations; score bigger when less overlap • Results? • Geographic (East-West) focal points • Where to Meet in NYC? • Thomas Schelling experiments found GST focal point • Bottom Line? • “Intuition” can be a useful heuristic

9. Biomedical Examples • Rules of Thumb & Intuition • Patient presents with sore throat & upper respiratory symptoms • common cold virus • Infant presents with intestinal discomfort • gas • Patient presents with shortness of breath and fatigue • asthma • Search • Patient presents with fever severe sore throat, fatigue -- no other • “Rapid” Strep Test; • Symptoms recur frequently -- “overnight” strep culture • Lack of resolution -- blood tests (C-reactive protein; white blood cells …) • Start from low cost/most common and proceed

10. Example from Everyday Decisions • Heuristics that you employ or have seen others employ in everyday personal decisions • Rules of Thumb? • Searches? Take 15 minutes and write down list of heuristics from both personal and business experiences + rank by the ones that work well and not so well; indicate why • In general, these kinds of examples from are often called “Satisficing”

11. Limits of Intuition • Fooled by Randomness (Taleb, 2002): Experiments show that people using simple observation (heuristic), repeatedly see patterns to outcomes where none exist

12. Robert Lucas on Limits of Simple Searches & Observations • Since the mid 1980s, companies like Microsoft, FedEx, Staples, MCI and many others have shown tremendous growth in earnings, market share, employment and other performance measures. With such a range of experience, why do we need theoretical models? Why can’t a company just send a fact-finding team to Staples, find out the strategies and structures which made them successful, and then go home and get their own company to do the same? This sounds easy enough, but it is not really operational ... Firms are just too complex -- there are too many things going on at once -- for getting all the facts to be either possible or useful. Faced with so much data, an observer who is unequipped with a theory sees what he wants to see, or what the successful company or management guru wants to show him. One needs some principles for deciding which facts are central and which are peripheral. This is exactly the purpose of to isolate some very limited aspects of a situation and focus on them to the exclusion of all others ... We need to make some hard choices about what to emphasize and what to leave out before we can think in an organized way at all.How do economies or companies succeed? • Adapted from Robert Lucas, 1994 Nobel Prize Winner to European Econometrics Society (Extension of science philosopher, Karl Popper)

13. Limits of Heuristics: Examples • Biomedical • Jerome Groopman -- Harvard Hematologist-Oncologist Second Opinions: Stories of Intuition and Choice from the Changing World of Medicine (2001) • Pediatrician’s rule of thumb > Infant has gas • Reality > Obstructed Bowel • Primary Physician rule of thumb > Woman has asthma • Reality > Leukemia • Production • Carnegie-Mellon Bicycle Production Example

14. General Lessons about Using Heursitics • Heuristics Most Successful Where • Outcomes conform to “typical” situations • Heuristics fail where nuances present • Causal relationships simple (e.g. virus-disease) • Heuristic approach in biomedicine not as successful in multi-factor problems, e.g. Neurological problems • Problem of a nature so that computational power can overwhelm the search problem by brute force, • e.g. Human Genome project; finding a numerical solution through searches; • Problem is time-sensitive • Problem beyond analytical limits

15. The Tradeoffs: analytics v. heuristics • Analytics -- “High Cost/Low Error” • Precise, Logical, • Costly in terms of time/mental demands; flexibility • Heuristics -- “Low Cost/High Error” • Low cost in terms of time/mental demands, flexibility • Difficult to assess; Subject to unclear biases… • CS: “Heuristics are bug ridden by definition. If they weren’t, they would be algorithms”

16. Analytical Heuristics: an oxymoron? • Methods different but the same? • rules of thumb & intuitive searches sometimes imitate analytical results (Day, AER) • Analytical Heuristics • Not all search random are led by intuition alone • Search can be guided by analytics & prior knowledge • Wright Brothers & entrepreneurship

17. Development of Flight: Bradshaw & Lienert (1991)

18. WRIGHT BROTHERS & ENTREPRENUERSHIP • One meaning of entrepreneurship: innovating and improving through combining or mutating products, processes … • Wright Brothers (See Related Websites & First Flight) • Did not just go through trial & error or use just intuition • Serious research on past efforts • Analytical reasoning regarding physics • Then Search through Experimentation in wind tunnels and full-scale

19. Critical Lessons • Effective use of heuristics will make you a better manager • Overuse of heuristics will create mistakes -- sometimes devastating mistakes • Employ heuristics when best suited to the situation -- not as a crutch to alleviate the need to think analytically • What is the decision environment • Time sensitivity, analytical tractability and completeness, search power, analytics guiding heuristics possible • Remember the implicit biases apply to heuristics too!

20. Mini-Assignment • Identify and be able to explain 2 examples of the use of heuristics to solve problems in a workplace setting involving rules of thumb, simple search, or the overlap of analytics & heuristics. Evaluate how well the heuristic seems to work and the reasons it does or does not seem to work very well.