Argumentation as Engineering and Vice Versa

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David E. Goldberg presentation at fPET-2010 discussing engineering as argumentation using Toulmin's model and argumentation as engineering considering Gary Klein's naturalistic decision making and mental simulation.

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Argumentation as Engineering and Vice Versa

  1. 1. Argumentation as Engineeringand Vice Versa<br />David E. GoldbergIllinois Foundry for Innovation in Engineering EducationUniversity of Illinois at Urbana-ChampaignUrbana, Illinois 61801 USAdeg@illinois.edu<br />
  2. 2. Vice Versa Came First<br />Provocative title interesting, but vice versa came first.<br />Developed economy of models argument as part of Design of Innovation. <br />Found Toulmin’s model useful as theoretical way to connect formal and informal engineering modeling.<br />Started teaching this in senior design and it proved helpful.<br />
  3. 3. Roadmap<br />What Engineers Know & How They Know It.<br />Engineering modeling lesson from a life in genetic algorithms.<br />A demarcation problem. <br />An economy of models & a modeling spectrum.<br />Lessons from senior design & the missing basics.<br />Qualitative modeling as missing skill.<br />Toulmin as a way to articulate & unify engineering modeling.<br />Tales from the trenches: Toulmin & tortillas.<br />Argumentation as engineer: Homo habilis & Gary Klein.<br />
  4. 4. Engineering vs. Scientific Knowledge<br />Vincenti distinguishes engineering knowledge from science with examples from aeronautical engineering history.<br />Suggests engineering is not merely applied science.<br />Two Vincenti cases:<br />Control volume models.<br />Flush riveting.<br />Quantitative & qualitative models that are different because of their usage.<br />Can we go beyond distinctive historical exemplars?<br />
  5. 5. A Life in Genetic Algorithms<br />Met John Holland in 1980 upon return to Michigan for PhD.<br />Did dissertation applying GAs to gas pipeline optimization and rule learning.<br />Needed better understanding to improve GAs.<br />Received criticism for my “engineering style” of modeling.<br />Models not “proper” or “rigorous” but they were helping me design faster, more effective GAs.<br />Could I make rigorous defense of my method?<br />
  6. 6. The Science-Engineering Demarcation Problem<br />Engineers & scientists think in terms of models.<br />Scientists is in business of model making.<br />Engineer is in business of artifact making. Model making & usage is instrumental to that aim.<br />In era of technoscience, models themselves not necessarily distinct.<br />Engineers explicitly, necessarily & systematically use & develop range of models with different precision-accuracy and costs: an economy of models.<br />This economy of models fairly reliable demarcation of engineering modeling practice from science. <br />
  7. 7. An Economy of Modeling<br />Engineer/Inventor<br />ε, Error<br />Scientist/Mathematician<br />C, Cost of Modeling<br />
  8. 8. Spectrum of Models<br />Qual-Quant Divide<br />
  9. 9. Modeling Costs and Benefits<br />Engineer is economic modeler when marginal costs do not exceed marginal benefits: ΔC ≤ ΔB.<br />Benefit to what: To designed artifact.<br />3 points:<br />Calculation usually not explicit.<br />But modeling economy taught in pedagogy: e.g. Statics before Dynamics.<br />Uneconomic model use common engineering manager’s complaint: Modeling for modeling’s sake.<br />Scientist studies at frontiers: New model (error frontier) or better model.<br />Engineering modeling often cost improvement (lower error at given cost) or improvement of modeling for practice: e.g. FEM vs. analytical elasticity solutions.<br />
  10. 10. Approach of Design of Innovation<br />First part of DoI methodological. <br />Applied modeling methodology to selectorecombinative GA design problem.<br />Constructed little models, quantitative models of different facets, integrating them to design and tune GAs that scaled to large hard problems. <br />Quantitative analysis was prime concern.<br />Concern for modeling left (qualitative models) came with engineering education reform efforts.<br />
  11. 11. Lessons from Senior Design<br />Coached 20 years of senior design.<br />Students <br />expect clean problems with well-defined data &<br />are Pavlovian dogs when it comes to Newton’s laws or Maxwell’s equations. <br />Real-world problems & data <br />are ill-defined;<br />come in form of narrative;<br />vary in feasibility & quality<br />Students have trouble making sense of problem & data.<br />Misled by their classroom experience of clean problems, with easy, single solution, and spend first half of course unlearning.<br />
  12. 12. The Missing Basics of Engineering<br />“The basics” = math, science, and engineering science.<br />Reflections on 20 years in industry-sponsored senior design.<br />After 4 years students don’t know how to<br />Question: Socrates 101.<br />Label: Aristotle 101.<br />Model conceptually: Hume 101 & Aristotle 102.<br />Decompose: Descartes 101.<br />Experiment/Measure: Bacon-Locke 101.<br />Visualize/draw: da Vinci-Monge 101.<br />Communicate: Newman 101<br />Call these the missing basics (MBs).<br />Using term “soft” accepts MBs as outside engineering.<br />Fundamental to engineering, organizational & learning prowess.<br />Socrates (470-399 BC)<br />12<br />© David E. Goldberg 2010<br />
  13. 13. How It Works: Key to Engineering<br />A key qualitative model in engineering is representation of causal chain of the way things work (or not):<br />As narrative.<br />Or diagram.<br />Or working prototype.<br />“This led to this led to this.”<br />Critical model, but students think “if no equation, no model.”<br />Field example.<br />
  14. 14. The Tortilla Problem<br />Interesting example in tortilla factory.<br />Company was using too much dusting flour relative to historical recollection.<br />Flour cost was rising.<br />Wanted students to study process and reduce dusting flour usage.<br />© David E. Goldberg 2009<br />
  15. 15. Students heard story.<br />Too much flour  gets in air  flour burns  falls on tortilla  customer mistakes for mold complaint.<br />Causal chain a model.<br />Students don’t recognize as model.<br />How can we help them?<br />Burnt-Flour-as-Mold Problem<br />15<br />
  16. 16. Help from Argumentation Theory<br />1958 book by philosopher Stephen Toulmin formed basis of argumentation theory.<br />How do people really make arguments?<br />How do people give reasons for what they think or do?<br />Form of reasoning ties together formal and informal engineering reasoning.<br />
  17. 17. Formal Reasoning: Logic<br />Modus ponens (modus ponendo ponens: mode that affirms by affirming): <br />if pthen q<br />pis true<br />thereforeqis true<br />Method of mathematical logic & formal reasoning.<br />Note: Once premises and rules in place, formal logic derives conclusions mechanistically.<br />Aristotle (384-322 BCE)<br />
  18. 18. Toulmin: Elements of a Human Argument<br />Like modus ponens:<br />Claim. A single statement advanced for the adherence of others.<br />Grounds. A statement about persons, conditions, events, or things that says support is available to provide a reason for a claim.<br />Warrant. A general statement that justifies using the grounds as a basis for the claim<br />Backing. Any support (specific instance, statistics, testimony, values, or credibility) that provides more specific data for the grounds or warrant.<br />Qualifier. A statement that indicates the force of the argument (words such as certainly, possibly, probably, usually, or somewhat).<br />Warrants can be generalizations, cause, sign, analogy, authority.<br />Backing can be anecdote, stats, testimony, credibility, and values.<br />Rieke, R. D & Sillars, M. O. (1997). Argumentation and critical decision making. New York: Longman.<br />
  19. 19. Back to the Tortillas: Burnt Flour Model<br />Grounds. Dusting flour is spread onto the moving dough on a continuous tortilla line.<br />Claim. Burnt black flour deposits is mistaken for mold, resulting in quality complaints<br />Warrant. Excess flour becomes airborne & burn in the oven, deposits (authority).<br />Qualifier. Sometimes<br />Backing. Client story & increased flour results in increased spot problem.<br />
  20. 20. Tradeoff: Improve Backing or Solve Problem<br />In resource limited environment, often face decision:<br />Should you improve warrant and backing?<br />Or should you work on solving the problem?<br />Can be difficult choice.<br />Key query: If assume correctness of warrant/backing & wrong, will you fail to solve problem?<br />Tortilla problem: Students took explanation as true because it didn’t affect investigation (reducing dusting flour usage  reduces this side effect).<br />
  21. 21. Argumentation as Engineering<br />Outline of argument:<br />Argument is an externalization of human thought processes believed to be correct. <br />Artifacts are first fully expressive externalization of human thought processes.<br />Research on naturalistic decision making suggest that mental simulation processes similar to those of argumentation.<br />Therefore, first tech artifacts may be thought of as first expressive evidence of argument-like processes in human ancestors. <br />OK to think oral or written arguments as offspring of first engineering efforts. <br />Go back 2.5mya.<br />
  22. 22. Tech Histories Don’t Go Back Far Enough<br />Let’s start 2.5mya.<br />Homo habilis: First tool maker, 4’-3” tall, 88 pounds, bipedal hominid.<br />Lived on open savanna (Lake Turkana)<br />Social.<br />Made and used stone flakes.<br />Did not speak (de Boer, 2005).<br />de Boer, B. (2005) The Evolution of Speech, in: Brown, K (Ed.) Encyclopedia of Language and Linguistics 2nd edition, Elsevier.<br />
  23. 23. Oldowan Tools<br />First discovered by Louis Leakey.<br />Used 2.5mya to 0.5 mya.<br />Know they were used by scavengers.<br />Scrape carcass clean of meat following kill by another animal.<br />First known fully expressive externalization of human thought.<br />
  24. 24. Homo Ergaster<br />Better tools around 1.6-1.7 mya.<br />Hand axes and cleaving tools with sharp edges.<br />Butchering of large animals. <br />Tamed fire.<br />Still not talking.<br />
  25. 25. Naturalistic Decision Making<br />Gary Klein has studied how those under pressure make decisions.<br />Naturalistic decision making.<br />Rational decision making used infrequently & not under pressure. <br />Cannot knowHomo habilismind.<br />Assumption:Homo habilismind was likely similar to our minds under pressure.<br />
  26. 26. The Role of Mental Simulation<br />Klein identifies different modes, recognition primed decision & constructive decision, for example.<br />Mental simulation is key to all.<br />Many decisions made with single simulation that shows adequacy.<br />Satisficing: First adequate solution chosen.<br />Imagined artifact simulated step by step.<br />
  27. 27. Object Then Made & Used<br />Steps:<br />Artifact imagined in context of use.<br />Simulated step by step.<br />Device created.<br />Used for simulated purpose. <br />Step by step imaging of adequacy a causal chain played out in mind. <br />Thus, creation of first tech artifacts are first, external fully expressive evidence of human argument-like reasoning.<br />Thus argumentation may be viewed as offspring of engineering. <br />
  28. 28. Bottom Line<br />Engineering as Argumentation<br />Engineers are broad-spectrum modelers.<br />Qual is part of the canon.<br />Toulmin’s model provides unifying framework for math, science, & qualitative modeling.<br />Introduction to students helpful in aligning behavior with needs of practice.<br />Makes “soft skills” part of engineering not something apart. <br />Argumentation as Engineering<br />Notion of argument as external representation of mental reasoning traces back to engineered artifacts. <br />Tech as first shared representation of output of mental simulation.<br />Packaging of mental constructs.<br />Sometimes forget ancient prehistory of engineered artifacts.<br />
  29. 29. For More Information<br />Illinois Foundry for Innovation in Engineering Education (iFoundry): www.ifoundry.illinois.edu<br />Philosophical writings on PhilSci archive: http://philsci-archive.pitt.edu/perl/search (author search for Goldberg).<br />This and related powerpoints: www.slideshare.net/deg511<br />

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