3 Lessons of Ancient & Modern Philosophy for Creative People-Centered System Design

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    3 Lessons of Ancient & Modern Philosophy for Creative People-Centered System Design - Presentation Transcript

    1. Three Lessons of Ancient & Modern Philosophy for Creative People-Centered System Design David E. Goldberg Industrial & Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana, Illinois 61801 [email_address]
    2. 2007 & Creativity
      • Globalization shaking confidence.
      • Cheap, effective talent in Shanghai and Bangalore can be hired and managed at a distance.
      • Routine kaizen (continual improvement) outsourced to the countries that generate a million engineers per year (China & India).
      • Creativity is not a nicety, it is not an option.
      • It is about economic survival.
      • Category enhancement is not enough.
    3. Roadmap
      • 3 things I won’t be discussing.
      • Cold war mindset in an Internet world.
      • How I changed my mind.
      • 3 lessons from modern & ancient philosophy.
        • Searle and the construction of social reality.
        • Socrates/Plato and the importance of dialectic.
        • Aristotelian data mining and its application to social networks.
      • People-centered design at the center of this creativity revolution.
    4. 3 Things I Won’t Talk About
      • I absolutely positively won’t talk about the following three things:
        • GA scalability
        • Human & computer agency and the 4-quads
        • DISCUS, SAiNT & all that.
    5. GA Scalability & Efficiency
      • GAs don’t work. No proofs.
      • Wrong!!!
      • GAs were shown to scale well in 1993.
      • Wrote up the story of GA scalability in 2002.
      • Plays on two levels:
        • GAs as cool technology.
        • GAs as models of effective inventive/innovative processes.
      • Understand at both levels. Beware 1 st gen GA/EC.
    6. Billion-Variable Noisy Problem
    7. H-C Agency in 4 Quadrants (Kosorukoff & Goldberg, 2002) Selective agent Inventive agent human computational human computational Standard Genetic Algorithms Computer Aided Design (CAD) Interactive Genetic Algorithms Human Based Genetic Algorithms
    8. DISCUS
    9. Semantic Reflection: Keygraphs http://www-discus.ge.uiuc.edu High frequency Low frequency Keyword
    10. Social Reflection: IDM IDM = Influence Diffusion Method
    11. Cold War Institutions, Internet World
      • Large, centralized corporations, governments, and institutions (including universities).
      • Revolutions in 20 th century in transportation & communications give us radically different world because of transaction costs & network returns.
      • Neo-people-centered systems shaped by these forces.
      Ronald H. Coase (b. 1910)
    12. How I Changed My Mind
      • Wrote Change in Engineering Education (1993).
      • Started DISCUS research 2003.
      • Took Nextumi Chief Scientist slot 2004.
      • Presented Postmodern Systems Engineering (2004).
      • Recent influences, Pink’s A Whole New Mind and Price’s tech visionary (TV) research.
    13. Cold-War View of Humans
      • During the Cold War, humans were an obstacle to the proper functioning of a system.
      • Tom Wolfe’s, The Right Stuff, plot: tension between pilots and techies who would eliminate them.
      • Cold War view: Humans are error in the loop, and error is to be eliminated.
    14. Postmodern Systems Engineering
      • In internet world, human beings are integral part of the system.
      • The major innovations of our time involve people in the loop as major actors:
        • Google: Search as human preference machine.
        • MSOffice: PPT, Word, Excel as human productivity engine.
      • Postmodern view: Not error in the loop. Humans ARE the loop.
      • Brute facts of physics not dominant in postmodern systems.
      • Examples:
        • What are the “physics” for Ebay?
        • What equations of motion govern Google?
        • What constitutive relations for MSOffice.
    15. Chief Scientist in a Web Startup
      • Interested in commercial potential of genetic algorithms.
      • Asked to evaluate an idea inspired by my book on genetic algorithms.
      • Ended up as co-founder and chief scientist.
      • Unsettling to design product that has not existed.
      • Surprised by what I spend time thinking about.
      • Philosophical reflection most helpful tool.
      • This inspired idea for course teaching creative modeling techniques
    16. Category Creators v. Enhancers
      • Premium is on category creators —those who creates new categories of product and service.
      • This requires different skill set.
      • Right-brained thinking: integrative, creative, intuitive.
      • MFA + Engineer vs. MBA + Engineer.
    17. Course: Creative Modeling for TVs
      • Introduction
      • Models of creativity
      • Brainstorming
      • What is a model? What is a TV?
      • Construction of engineering reality.
      • 2 techniques from Athens
      • Visualization and napkintalk
      • Canonical models
      • Facebook
      • Qual-quant shift
      • Little models
      • Tales from the trenches.
      • Squeezing little models.
      • Mixed, patched, and meta-models
    18. 3 Lessons from Philosophy
      • Course covers qualitative and quantitative modeling appropriate to new category creation:
        • Dialectic in creative modeling.
        • Aristotelian data mining in creative modeling.
        • The construction of engineering reality.
      • Will mention approach of little models to get a flavor of first-quant move from qual.
    19. Tabula Rasa: Curse & Blessing of Category Creator
      • How do we design when we don’t know how to talk about what we are designing?
      • Let’s start at the human beginnings of conceptual clarity.
      • Let’s start at the beginning of formal philosophy.
      • Let’s start with two key techniques from Athens.
    20. What Examples of New Thought?
      • Clearest examples are from philosophy.
      • Presocratic  Socrates  Plato  Aristotle.
      • Mechanisms of the new thought:
        • Socratic dialectic
        • Aristotelian data mining
    21. Socrates and Dialectic
      • Socrates was a pain in the neck.
      • Walked around Athens asking everyone impossible questions.
      • Then proved their answers were wrong, but rarely gave an answer himself.
      • Nonetheless, Socrates’s method was useful.
      • Conversation trying to probe what things really are (or might be).
      • Questions were the rights ones. Whitehead’s famous remark.
      Socrates (470-399 BCE)
    22. The Probing of Dialectic
      • Questions directed at the essence of things.
      • What is the meaning of a common phrase? “What is virtue?”
      • Answers often betray our lack of knowledge and understanding.
      • Examine answers critically, often with more questions.
      • Ask penetrating questions about the answers.
    23. What’s This Got to Do with Products?
      • Questions & conversation is at roots of all new products.
      • Research on tech visionaries shows that problem finding is the main activity of successful TVs
      • Spark of insight may come as flash, but dialectic necessary in new product creation.
      • Three roles of questions:
        • Probe customers.
        • Probe organizational hurdles.
        • Probe product developers.
    24. Probing Customers
      • Focus groups are immensely powerful.
      • Can be informal conversations with potential customers.
      • Can be formal focus groups behind the one-way mirror or over web.
      • The surprise of Nextumi: Search not solved problem.
    25. Tactics of Dialectic
      • Critical:
        • Equivocation: use of term in different senses.
        • Question begging: assuming the conclusion.
        • Infinite regress: infinite sequence implying incoherence.
        • Loss of contrast & emptiness: distinction with little or no difference.
      • Creative:
        • Definition: Seek essential distinctions.
        • Analogies: Certain dialectical similarities.
        • Thought experiments: Hypothetical w/ true premises that does not follow.
      http://www.amazon.com/Practice-Philosophy-Handbook-Beginners-3rd/dp/0132308487
    26. Aristotelian Data Mining
      • Called The Philosopher by some.
      • Amazing range and scope of work.
      • Created many of basic categories of college curriculum.
      • Founded a school the Lyceum.
      • We have 1/3 his output (2000 pages in 30 books).
      • Categories and Metaphysics.
      • Method very modern:
        • Empirical search for data.
        • Considered attributes, which he named.
        • Classified data according to his attributes.
      • Can we break this down?
      Aristotle (384-322 BCE)
    27. A Hierarchy of Things
      • In Aristotelian data mining we seek list of essential qualities that delineate products or services.
      • Notion of genus and species comes from Aristotle.
      • Things divided into groups of like kinds.
      • Can be subdivided further.
      • At the bottom are particulars or substances.
      • 10 categories: substance, quantity, quality, relation, place, time, position, state, action, and passion.
    28. Aristotelian Product Spaces
      • Consider space of existing products that are related or similar.
      • Look for different exemplars that represent different types of products.
      • Taking viewpoint of the customer here.
      • May need separate decomposition for design.
      • Consider, for example, social networking space.
    29. MySpace
    30. Facebook
    31. Flickr
    32. Yahoo
    33. What Common? What Different?
      • Can we make a list of attributes that separate the space?
      • Can use J. S. Mills methods or methods of modern data mining.
      • Attributes:
        • Mode of communication (email-IM-mobile-wall)
        • Sense of community (ind-group-friends)
        • Gate to community (edu filter-anybody)
        • What shared (text-docs-photos)
    34. Some Techniques with Attributes
      • Dimensionalization of spaces very helpful to high-level thought.
      • Listen to discussions and try to dimensionalize quickly.
      • 3 techniques that come to mind:
        • Cartesian products
        • Expert systems
        • Hypertrophy of the dimensions
    35. Dimension Hypertrophy
      • Are there missing categories on the dimensions?
      • Did we capture all the modes of things shared?
      • Example, text-photos-docs: What about videos?
      • Is that a good idea?
    36. YouTube & $1.65 Billion Buyout
    37. Canonical vs. Creative Analysis
      • Example: Porter’s five forces.
      • NPD models.
      • Design models.
      • Need ability to do qualitative analysis on the fly.
      • Need engineers who can do this routinely.
      http://www.berg-marketing.dk/GIF/five_forces.gif
    38. Construction of Engineering Reality
      • Mill Prof of Philosophy of Berkeley.
      • Philosopher of language and mind.
      • Early work took off from Austin’s work on speech acts.
      • What does language have to do with it?
      • His book, The Construction of Social Reality (Free Press, 1995) , critical to our study.
      • Helps us understand social and institutional facts, separate physics from the social.
      John R. Searle (b. 1932)
    39. Brute vs. Institutional Facts
      • There are objects in the world that don’t depend on observers, brute facts, e.g. mountains, trees, atoms.
      • There are other objects that depend entirely on people and their interactions, institutional facts, money, chess, football .
      • Engineers study a lot about physical world and world of brute facts.
      • Much design about getting brute facts right, e.g. a screwdriver.
      • Are engineered objects mere brute facts?
      Screwdriver
    40. Epistemology versus Ontology
      • Epistemology: The study of knowledge.
      • Ontology: The study of what exists.
      • Our ontology:
        • Live in world of physical particles in fields of force.
        • Some of these living systems with consciousness evolved.
        • These systems are intentional: Think and act toward other objects.
    41. Objectivity versus Subjectivity
      • Have existence versus knowing, as well as objective versus subjective.
      • Examples:
        • Mountain: existence  objective
        • Pain in toe: existence  subjective
        • Pain in toe: knowledge  objective
      • Ontological subjectivity does not prevent epistemological objectivity.
    42. Intrinsic versus Observer-Relative
      • Intrinsic feature: Something that exists independent of observer (observer independent).
      • Example: A mountain.
      • Observer-relative feature: Something exists only relative to an observer.
      • Example: A screwdriver.
        • Physical existence is intrinsic.
        • Screwdriverness is observer-relative.
      • Engineered objects are at least partially observer-relative.
      • Why spend all time on the physics in curriculum?
      Fujiyama
    43. Structure of Social Universe
      • Mind creates an objective social reality.
      • Example, money:
        • Trivial physics: money not money because of material existence.
        • Money, money because of our intentions.
      • Other examples: language, government, universities.
      • Object fits description because we think it does.
      • What is ontology of the social and the institutional?
    44. Building Blocks of Social Reality
      • Need 3 new elements:
        • Collective intentionality.
        • Assignment of function.
        • Constitutive rules.
    45. Collective Intentionality
      • Need the notion of “we intend together.”
      • Attempts to reduce to individual intention are complex.
      • Existence of biological organisms with collective intentionality suggests CI is a primitive.
      • Social insects as an example: ants or termites for example.
    46. Social vs. Institutional Facts
      • Social fact is any fact involving 2 or more agents w/ collective intentionality.
      • In Searle’s terms, ants create social facts.
      • Institutional facts go far beyond social facts as we shall soon see.
      Australian ant hill
    47. Assignment of Function
      • Use of objects as tools:
        • Monkey uses stick to get banana.
        • Man sits on rock.
      • Physical existence facilitates function, but function is observer relative.
      • All function assignment is observer relative.
      • Aside: What does this say about engineering design?
    48. Constitutive Rules
      • How to distinguish between brute facts and institutional facts.
      • Types of rules:
        • Some rules regulate: “Drive on rleft side of road.”
        • Some rules regulate and constitute: Rules of chess both regulate conduct of game and create it.
      • Constitutive rules form: X counts as Y in C.
      • “ Move two and over one” counts as a knight’s move in chess.”
    49. Web Life: Institutional Complexity
      • Go on Google, search for online book seller, sign in to Amazon.com using account ID, order a book, using a credit card, get recommendations from recommender system & order some of those books, too.
      • Get confirmation message via e-mail account, and books delivered by FedEx.
    50. Institutional Facts Don’t Wear Out
      • They get better with use.
      • Use reinforces collective intentionality.
      • Types of Institutional fact creation:
        • Emergence: early examples, money, marriage, property, emerged.
        • Designed: TCP/IP, Google, Ebay, other web companies are designed and go viral with widespread adoption..
    51. Some Engineering Artifacts Institutional
      • All engineering artifacts social.
      • Which objects have the big 3: collective intentionality, assigned function, X counts as Y in Z.
      • Example TCP/IP, 4-byte number counts as an address in TCP/IP network.
      • Generally, standards and protocols are designed institutional facts.
    52. Institutional/Physical Landscape
    53. Matters to People-Centered Design
      • The web has changed a lot.
      • Interconnected, software reconfigurable systems enable brave new world of institutional facts:
        • Easily constructed.
        • Easily propagated (viral marketing).
        • Easily iterated (systems upon systems).
      • Has led to need for discipline of “postmodern systems engineering.”
    54. Postmodern Design Principles
      • Systems thinking is key.
      • Remember the big 3:
        • Collective intentionality: viralness.
        • Assignment of function: designed.
        • Constitutive rules: X as Y in C, creating games.
      • Consider 4 types of status:
        • Symbolic: take care in use of words.
        • Deontic: e.g. rights and obligations of a user.
        • Honor: can be meaningful.
        • Procedural steps: systems view often requires interlocking steps.
      • Inventiveness all the way up.
    55. Bottom Line
      • Creativity is now imperative for advanced economies.
      • More category creation, less enhancement.
      • Curse & blessing of the tabula rasa.
      • Must recall how to think new thoughts & get epistemology and ontology of world right.
      • Leads to a new discipline of postmodern systems design.
      • People-centered systems at the heart of this revolution.
    56. More Information
      • TEE, the book. http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470007230.html
      • TEE, the blog. www.entrepreneurialengineer.blogspot.com
      • TEE, the course. http://online.engr.uiuc.edu/webcourses/ge498tee/index.html
      • MTV, the course. http://online.engr.uiuc.edu/webcourses/ge498tv/index.html
      • Engineering and Technology Studies at Illinois (ETSI) http://www-illigal.ge.uiuc.edu/ETSI ).
      • 2007 Workshop on Philosophy & Engineering (WPE) http://www- illigal.ge.uiuc.edu/wpe
      • Illinois Genetic Algorithms Lab http://www-illigal.ge.uiuc.edu/
    57. Optimal Teams: Deciding & Doing
      • Dichotomy, in Simon’s Administrative Behavior.
      • Non-dimensional form:

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