1. Mobile Systems for Computational Social Science:A Perfect StormJohn Charles Thomas!!Problem Solving International!UbiComp, Seattle WA!13 September, 20141

2. Interacting Factors for Perfect Storm Smaller, Cheaper, Faster, Lower Power Computing! Smaller, Cheaper Sensors and Effectors! Smart Phone Growth! Globalization! Shorter Cycles and Productivity Press >Continuous Measurement ! Classical Statistics > Big Data Analytics;Imputation; Monte Carlo Simulations; RandomForest; AI Techniques. ! Laboratory Studies > Field Observations andMeasures! Simple Theories > Complex Theories! These interact in positive feedback loops; e.g.,complex theories + faster computing + more data> theories can be more quickly refined.2 3. What are the limiting factors in mobile computing for socialscience? Not compute speed! Not sensors! Not effectors! Not cost! Not power requirements! Only Imagination.3 4. Normative Model of Development: All these arts can beinstrumented, studied, and improved. Problem Finding! Problem Formulation! Component Solution! Integration! Reality Check Reframing.! Design! Development! Deployment! Post-Mortem on Process and Product4 5. Google Hits Problem Solving - 63M! Problem Solving Techniques - 470K! Problem Finding - 3M! Problem Finding Techniques - 14K! Problem Formulation - 730K! Problem Formulation Techniques - 39K! Problem Reframing - 8K! Problem Reframing Techniques - 65 6. Early Studies of Query Languages (1974) Query By Example showed great improvement over IQF in a users ability totranslate questions from English into formal query language: ! IQF 4-24 hours training; QBE < 3 hours training! Ave. T/Query in IQF 5-12 min; QBE 1.6 min.! % correct IQF: 35%; QBE 67%! BUT: When given a series of problems and a DB description and asked to writetheir own relevant query and translate into QBE, users could not do it.! Answered question (without being able to look at any actual data!).! Wrote (and translated) irrelevant queries.! Wrote HAL+ queries.6 7. Some Methods of Community Knowledge Generation and Sharing(besides math models at one extreme and opinion at the other extreme) From General to Particular:Story! From Particular to General:Patterns and Pattern Language! Reframing: Context Generation7 8. Problem Finding by Observation of Patterns of Behavior thatViolate your Expectations (often non-linearities) Random Drops Become a Lake! Table Tennis Club Destruction8 9. Abstraction: Random Drops Become a Lake Main tendency but with variation ! Extreme outliers have qualitatively differentbehavior! The behavior of the extreme outliers changesthe field; in particular, makes the probabilityof other extreme outliers increase! Another example from The Power of PositiveDeviance: How Unlikely Innovators Solve theWorlds Toughest Problems. Childhood hungerin Vietnam.! In this case, the positive feedback loop didnot exist without intervention. ! Ubiquity could be used to help find suchpositive deviance9 10. Two Tables (Blue) supports stable community of 20-30 people every noonWhat happens with One Table? H1: Community will stay at about 20-30 people (theinteresting in table tennis trumps facility).! H2: Community will diminish to about 15-20 people(the facility will not support so many people). 11. 3022.5157.50Two Tables (Blue) supports stable communityOne Table (Green) does not support community (feedback disruption)Monday Tuesday Wednesday Thursday Friday Monday Tuesday11 12. Problem Finding from Story Stories deal with the edges of humanexperience! Stories thrive on conflict ! Stories thrive on emotion! Follow the Anger back to source offrustration: A problem to be solved.! In stories, typically it is the determination,cleverness, or bravery of the hero thatsaves the day.! However, they often have a special poweror gift: Make that a reality. ! Or, re-write the story so that theproblem(s) can still be solved, but byordinary people.12 13. Stories tend to focus on the edgesof human experience(Note similarity to Patterns ofBehavior that Violate Expectations)13 14. Stories can be viewed as three-dimensional:14 15. 15 16. Problem Finding Examples: Pets do not always do what they should. Reinforcement works, but ownersare busy and away. S: Remote monitoring and delivery of reinforcement. ! Home objects have instructions that are illegible. S: Mobile phone couldread what the device is and display legible instructions. ! Plant signs are ambiguous. S: Photo sent to service which returns foursimilar pictures with names and links. ! New inventions promise wonders but lack convincing experientialevidence. S: ! Waiting turn for haircut is a pain. Plus, hard to describe how short youwant your hair to be cut. S: While waiting, iteratively choose haircut viewon based on your photo.16 17. Pets do not always do what they should. Reinforcement works, but ownersare busy and away. S: Remote monitoring and delivery of reinforcement. Planning the next !Catastrophe17 18. Home objects have instructions that are illegible. S: Mobile phonecould read what the device is and display legible instructions. Top view: Bose DVD player! Bottom view: Home thermostat! The real objects are just this!difficult to read.!Mobile device also allows a UX!intervention point for updates,!different languages, large print, etc18 19. Example: Computerizing a Chair by Story-izing Components of a Chair: Back, Seat,Legs! Material of a Chair: Fabric, Wood,Metal, Rubber! Purpose to Which Chairs are Put:Relaxation, Socialization, Work.! History of the Chair: Desires,Acquiring information, Designing,Raw Materials, ComponentConstruction, Assembly,Transportation, Preparation ofMaterials, Packaging, Sales, Wear(what fails? under what conditions?),Disposal?19 20. Playing with the Character Dimension of Story Person > Group, Team, Friends, Family,Clan, State, Nation, World, All Life! Person > Role, Mood, Age, Job, Hobby,As Relation, Time of Day, Time of Year! Special Needs > Sight, Hearing, Touch,Coordination, Germ Free.! Sight > Lack of glare, slow changes inillumination, large type, slow change offocus! Situations > Going on a family trip;attending a sporting event; shopping for ahouse; choosing a restaurant.! Values > Theoretical, Religious,Practical, Experiential, Social20 21. New inventions promise wonders but lack convincingexperiential evidence. S: ?? Grill cleaner, new skates,!mosquito hood and jacket,!rain barrel ! What do these feel like?! How long do they last?! What are maintenance issues?! Will this still seem cool when !I am not at 40,000 feet and have!just had 3 martinis?! What if all these inventions wereinstrumented BOTH for continuousimprovement AND so potential buyers couldsee how they actually performed?21 22. Plantar Fasciitis Which one of these productsdo I buy to fix my plantarfasciitis? ! Cheapest? ! Most expensive?! Most stars?! Doctor prescribed highpowered anti-inflammatoryand stop exercising! Solution?22 23. Remove pebble from shoe23 24. According to Judy Mod, founder of Social Executive Council Companies who produce andsell focus most of their energyon beating the competitionon price, performance,features, etc.! For IT system decisions,10-20% of lost sales prospectsare to competition.! 80-90% are lost to nodecision24 25. Companies do Market Research but Largely constrain the nature of the presumed problem upfront.! Study with ecologically invalid methods (e.g., New Coke).! Focus on beating the competition. ! Focus on selling the productbut cannot see what it lookslike from the customers viewpoint.! Its a clown. It is smiling. It has big eyes. It has all thefeatures that our research shows are correlated withcuteness. It has to be cute!25 26. 26 27. Ubiquitous Computing Allows: Studying in situ both physically (in thesmall and in the large) and socially ! Caveat: Still subject to interpretation! Pattern: Reality Check! Which one is the real desk?27 28. Pattern: Reality Check Often something easy tomeasure is highly correlatedwith what you really want tomeasure.! You measure this ersatzmeasure.! But, the correlation may changeover time. (e.g., programmingskill and speed).! Therefore, you need toperiodically do a reality check.28 29. Solving a Problem; Reframing a Problem TRIZ! Subtracting a Constraint! Solving SuccessiveSubproblems! Work from and TranscendApparent Contradiction! Adding a Constraint! Reframing by Adding !Context (story technique)! Iroquois Rule of Six29 30. Wheres Jonathan?! Supposed to be here at 8:00; now 8:15! He doesnt care about the project!! OR.Your appointment bookhas the wrong time.! ORYour watch is wrong.! ORJonathan comes from aculture where 8:15 is not late.! ORJonathan was waylaid inthe hall by the CEO to talk aboutthe project. ! ORYou are in the wrong room.30 31. Generalizing the Solution Social Pattern: Who Speaksfor Wolf?! Spatial Pattern: Context-SettingEntrance! Information Pattern:Clarification Graffiti! Temporal Pattern: SmallSuccesses Early31 32. Social Pattern: Who Speaks for Wolf?!A lot of effort and thought goes intodecision making and design.Nonetheless, it is often the case that baddecisions are made and bad designsconceived and implemented primarilybecause some critical and relevantperspective has not been brought to bear. This is especially often true if therelevant perspective is that of astakeholder in the outcome. Therefore, make sure that every relevantstakeholders perspective is brought tobear early.32 33. Spatial Pattern: Context-Setting Entrance! Because people function in many different contextsand come from many different backgrounds andcultures, there are a wide variety of behaviors thatare considered appropriate in variouscircumstances. Sometimes, we are expected to compete with eachother vigorously. Other times, we are expected to behighly cooperative. When our own expectations are violated, we mayfeel resentful, angry, or afraid. When we violate whatwe later find to be the expectations of others, we mayfeel embarrassed or resentful. We dont want to be the only person at a party toshow up in a tux while everyone else is in blue jeans--- or vice versa. Therefore, provide a context-setting entrance so thatpeople know what is appropriate.33 34. Information Pattern: Clarification Graffiti Often people design formalinformation systems without anadequate understanding of what theworld is like to the end user.! When a user comes upon a puzzlingsituation, they sometimes find asolution. ! Often, when this happens, the userwants to share what they learnedwith others.! When possible, this leads to informalannotations that help clarify what isreally meant for other users.34 35. Temporal Pattern: Small Successes Early Some problems require large teams of relativestrangers to work together cooperatively in orderto solve the overall problem. Yet, people generally take time to learn to trustone another as well as to learn another'sstrengths and weaknesses and preferred styles. Plunging a large group of strangers immediatelyinto a complex task often results in non-productivejockeying for position, failure,blaming, finger-pointing, etc. Therefore, insure that the team or communityfirst undertakes a task that is likely to bring somesmall success before engaging in a complexeffort.35 36. Major Challenges: Scientific and Ethical Technology keeps changing; peoplekeep learning; tasks and goals andcontexts keep changing andexpanding > How can we cumulatescience?! Query Study! www.ibm.com! We may be able to accurately(statistically) predict bad behaviorbefore it occurs.! Who decides when, how, andwhether to intervene?! Minority Report; The Circle36 37. Mastering the Opportunity Offered by The Perfect Storm Find Problems! In Daily Life! In Stories! Note and Store Patterns! Use Ubiquity to Find Problems! Formulate Problems (Rule of Six)! Generate many Possible Solutions ! The Real Competition may be NODECISION = NO SALE! Test in situ! Reality Check! Learn to Improve Over Time37 38. Three different Disciplines are Converging:ScienceInventionOperationsHypothesis: The perfect storm allowson-going measurement, refinement,improvement, reframing, reinvention,and scientific discovery all at the sametime from using the same data and usingvarious combinations of the same methods.38 39. Science Triple Blind experiments:people do not even know theyare in a study. Ethical? ! Contingent Experiments:Rather than pre-plan theentire experiment, conditionsevolve and multiply asevidence accumulates. ! In Situ experiments: As more ofthe real world conditions canbe monitored and dealt with,less need to perform in lab.39 40. Invention More scientific studies of theinvention processes willsnowball number and breadthof inventions. ! Brute Force exploration willhappen more quickly. (e.g.,light bulb, lead storage battery,scrabble). ! The instrumentation of realitywill lead to finding a greatnumber of problems to besolved.40 41. Operations Manufacturing is already heavilyinstrumented; that trend willcontinue.! Now, the entire value chain will beinstrumented: problemidentification, design,development, deployment, sales,maintenance, disposal. ! Feedback from later stages canalter decisions earlier in theprocess changing problem asdefined, design, manufacturingprocess, transportation, etc.41 42. Key to Making this All Happen is You and Your Approach Using your knowledge, skill,and a variety of sophisticatedtechniques while inside! Still being the inquisitive child.! To boldly go .42 43. References:! Alexander, C. Ishikawa S., Silverstein, M. Jacobson, M. , Fikshdahl-King, I., Angel, S. (1977), A Pattern Language. New York: OxfordUniversity Press. Srivastava, S., Rajput, N, Dhanesha, K., Basson, S., and Thomas, J. (2013) Community-oriented spoken web browser for low literate users.Accepted for CSCW Paper, San Antonio, TX, 2013. Pan, Y., Roedl, D., Blevis, E. and Thomas, J. (2012), Re-conceptualizing Fashion in Sustainable HCI. Designing Interactive Systemsconference. New Castle, UK, June 2012. Thomas, J. C. (2012). Patterns for emergent global intelligence. In Creativity and Rationale: Enhancing Human Experience By Design J.Carroll (Ed.), New York: Springer. Thomas, J. C. & Richards, J. T. (2012). Achieving psychological simplicity: Measures and methods to reduce cognitive complexity. InThe Human-Computer Interaction Handbook. J. Jacko (Ed.) Boca Raton, FL: CRC Press. Trewin, S., Richards, J., Hanson, V., Sloan, D., John, B., Swart, C., Thomas, J. (2012). Understanding the role of age and fluid intelligencein information search. Presented at the ASSETS Conference, Boulder CO. Thomas, J., Diament,J., Martino, J. and Bellamy, R., (2012) Using Physics of Notations to Analyze a Visual Representation of BusinessDecision Modeling. Presented at VL/HCC 2012 conference in Salsburg, Austria. Srivastava, S., Dhanesh, K., Basson, S., Rajput, N., Thomas, J., Srivastava, K. (2012) Voice user interface and growth markets. India HCIconference. Trewin, S., John, B.E., Richards, J., Swart, C., Brezin, J. and Thomas, J. C. (2010). Towards a Tool for Keystroke Level Modeling ofSkilled Screen Reading, ASSETS 2010. Thomas, J. C. and Gould, J. D. (1974). A psychological study of Query By Example. IBM Research Report, RC 5124. Armonk NY: IBM. Thomas, J. C. (1983), Psychological issues in the design of database query languages. In Designing for Human-Computer Communication.M.E. Sime and M. J. Coombs (Eds.), London: Academic Press. Thomas, J.C. (1983). Studies in office systems I: The effect of communication medium on person perception. Office Systems ResearchJournal, 1 (2), pp. 75-88.!43 44. References Sternin, J. and Sternin, J. (2010). The Power of Positive Deviance: How Unlikely Innovators Solve the Worlds ToughestProblems. Harvard Business Review Press. Green, S., Jones, L. Matchen, P. & Thomas, J. (2003). Iterative development in the field. IBM Sysems Journal, 42 (2). Thomas, J. C., Kellogg, W.A., and Erickson, T. (2001) The Knowledge Management puzzle: Human and social factors inknowledge management. IBM Systems Journal, 40(4), 863-884. Thomas, J. C. (2001). An HCI Agenda for the Next Millennium: Emergent Global Intelligence. In R. Earnshaw, R. Guedj, A.van Dam, and J. Vince (Eds.), Frontiers of human-centered computing, online communities, and virtual environments. London:Springer-Verlag. Thomas, J. C. (1999) Narrative technology and the new millennium. Knowledge Management Journal, 2(9), 14-17. Desurvire, H. & Thomas, J.C. (1993). Enhancing performance of interface evaluators using non-empirical usability methods.In Proceedings of the Human Factors 37th Annual Meeting, 2, 1132-1136. Seattle, WA: October 11-15. Santa Monica, CA:Human Factors and Ergonomics Society. ! Thomas, J.C. and Kellogg, W.A. (1989). Minimizing ecological gaps in interface design, IEEE Software, January 1989.