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Collaborative Innovation ToolsJohn C. ThomasIBM T. J. Watson ResearchPO Box 704, Yorktown Heights New York 10598 USA1. Importance of Collaboration: Practical and Scientific.From practical and economic perspectives, we live in an increasingly interconnectedworld. In reflection of this trend, the field of human-computer interaction has shiftedfocus from supporting the productivity of individual workers to teams and largeorganizations (Thomas, In Press). From a scientific perspective, we learn most about theobject of study during transitions and adaptations. Thus, a learning test is generally morediagnostic of brain function than a test of stored knowledge; a glucose tolerance test tellsus more than a resting blood sugar level; a stress test reveals more about the heart thandoes resting heart rate. Similarly, this century’s rapid transitions should allow us tounderstand more about collective human behavior than ever before possible. At the sametime, we still face enormous planetary problems potentially including but not limited toglobal fouling of the ecosphere, inequity in economic opportunity, increased chances forcatastrophic disease, and international terrorism. Such planetary problems arose withcurrent approaches and limitations to collaboration and probably will only be solved viabreakthroughs in collaboration.From a much more prosaic and practical point of view, a similar set of challenges arisesfor large, international organizations today. For instance, the world is changing moreand more quickly but the ability of people to creatively design has not increased in anynoticeable way. As a result, there is a widening gap between the degree of flexibility andcreativity that is needed to adapt and the capacity of individuals and organizations to doso (Drucker, 1995). Yet, design problems are often extremely high leverage problems fororganizations. For instance, errors in design, whether in software, drugs, businessprocesses, or automobiles are extremely costly, compared, for instance, with codingerrors or manufacturing errors. Conversely, effective and innovative designs can beextremely lucrative; are one of the hallmarks of long-lived companies (Collins andPorras, 1994; DeGeus, 1997). Even a modest increase in the ability of organizations tocreate more effective designs could greatly reduce costs in existing markets and createwhole new markets. Again, increasing the effectiveness of design will requirebreakthroughs in collaboration.Human beings evolved natural language as a method for collaboration among smallgroups of people who generally shared context, goals, experience and culture. Underthose circumstances, sequential human speech served fairly well, e.g., the telling ofstories for sharing experiences (Thomas, 1999). However, unaided speech is not well-suited to large-scale collaborations among people; particularly not when the peopleinvolved may have vastly different sets of assumptions, cultural backgrounds, goals,contexts, experiences and even different native languages. We have not yet invented anentirely effective replacement of natural language for large, diverse groups thoughstorytelling can be useful in bridging some gaps among groups when incorporated intothe appropriate process (Van Der Heijden, 1996; Beyer & Holtzblatt, 1998; Bodker,1999). Can we extend such techniques even further to facilitate communication among
larger, more diverse groups? Or, should we limit such interactions to “dry” interactions(Azechi, 2000)?One of the special challenges offered by collaboration today is that it is often worldwideor at the very least involves remote participants. In many conversations and papers, itappears that an assumption, often implicit, is that remote collaboration is limited bybandwidth alone and that the superiority of face to face collaboration over remotecollaboration will disappear once bandwidth becomes large enough for us to clearly seethe details and subtleties of other people’s faces and to clearly hear the subtleties of otherpeople’s voices; perhaps other senses could also be transmitted. But such an analysisoverlooks two additional and potentially quite important aspects of face to facecollaboration.First, face to face collaboration typically means that people get to see and experiencesome of the physical and social context of their collaborators. They see the buildingperhaps where others work; try the same food; find out whether they are working in aquiet or noisy environment; what the moods are of those that pass by in the hallways.Second, sharing an actual physical space allows the possibility of much deeperinteraction and that possibility may well affect trust even if the possibility nevermaterializes. Consider two rather extreme examples. First, two people sharing aphysical space may be subject to a natural disaster such as an earthquake and one maysave the life of the other. Although this is obviously a very low probability event, themere possibility may well put people’s perceptual and emotional apparatus into aheightened state of arousal. Second, if two people share a common physical space, onecould strike out and physically injure the other. Since A’s trust of B is enhanced bysituations wherein A could hurt B but in fact, does not, the typical face to face interactionmay enhance trust in just this way.We should note however that it is not only the medium of communication and thecontext that impact collaboration, but also the content. In particular, we argue thatexpressive communications may offer an opportunity for collaborators to gain morecomprehensive models of each other than instrumental communication alone.Instrumental communication would constitute communication that is required toaccomplish the current task. Expressive communication is communication that tellsmore about the communicator than about the subject; it is communicated more becausethe communicator wants to than because they need to.Zheng, Bos, Olson, and Olson, (2001) showed that collaboration and trust can be, ineffect, “jump-started” with social chitchat. We have had some practical experience inseveral business contexts (but no rigorous empirical results yet) to indicate that storiescan also help people develop more trust than the exchange of information per se. A storyis not simply an objective recounting of events; it always implies a number of revealingchoices. The storyteller chooses which events to talk about; they choose where to startthe story; the tone; they choose the viewpoint; which details to describe and so on.Through a host of choices, the storyteller inevitably reveals as much about themselves asabout the subject. The listener then has data from which to learn about the storyteller aswell as about the subject of the story.
So long as collaboration proceeds along predictable lines; e.g., if two employees of acorporation are simply following a procedure, the models built from expressivecommunication may not be necessary or important. But, if the procedure breaks down orbecomes irrelevant, then collaborators who have developed more complex models ofeach other will be able to react more effectively and efficiently as a team. Of course,there is also a danger here. The potentially higher level of effectiveness and efficiencypresumes that the team will put group goals ahead of individual goals or even intentionalgrudges. As perhaps hinted at by Azechi (2000), stories might also reveal characteristicsof the storyteller that other collaborators might find quite negative while purelyinstrumental communications are unlikely to do so.People evolved a communication system adapted to small tribes, but on an even longerevolutionary scale, animals in general also evolved so as to be sensitive to suddenchanges in sound, illumination and other sensory input. In the “natural” environment of100,000,000 or even 10,000 years ago, such perceptual biases were conducive tosurvival. Today, these same perceptual and attentional predispositions guide our currentactions; e.g., observers are held rapt by high-speed chase scenes in movies and televisionthough such scenes have little if any actual survival value in the observers’ real lives. Bycontrast, many of today’s real problems such as overpopulation and global warming aretoo slow, too small, or too large to be perceptually salient (Ornstein & Ehrlich,1989.).For example, the exhaust of a car seemingly disappears a few feet beyond the tailpipe.How might we enhance the perceptual experiences of human beings to lead to moresystemic, collaborative and productive thinking? We know that changing representationscan make isomorphic problems easy or difficult (Ahlberg, Williamson, and Shneiderman,1993; Carroll, Thomas, and Malhotra, 1980; Tufte, 1997). Perhaps we can presentimportant but non-obvious problems in a way that helps utilize our natural perceptualcapabilities. For example, we could show people pictures of a coral reef taken fromthirty years ago and taken again today. In this “time lapse” technique, the devastatingand widespread effects of pollution can be made more visible and salient. We couldshow “extrapolative” movies illustrating what the impact on the world will be if all 8billion people on earth produced as much pollution as the typical American.Similar issues of having large groups of people understand a more global and moresystems view occur even in the context of a purely commercial organization. A standardand widespread challenge is to motivate people in a large organization to share theirknowledge with others. Taking the time to document lessons learned does not seemparticularly motivating to the individual employee whose individual rewards wouldprobably be maximized by moving on quickly to the next project. Yet, the organizationas a whole loses valuable knowledge by this type of shortsightedness.2. New technological possibilities.Recent advances in computing power, interface technologies, bandwidth, storage, andsocial engineering provide a broad field of possibilities from which novel solutions tolarge scale collaboration may be designed, tested, and improved. In the “real world”effective on-line collaboration systems both at a distance (e.g, Finholt & Olson, 1997)and face-to-face (Fischer, 1997), are already being facilitated by technology. We believefurther advances can be made by incorporating creativity aids, suggestions for processes
(Thomas, 1989), and by providing tools for alternative representations (Thomas &Carroll, 1979).Failure to innovate is not random, but can generally be ascribed to one of several maindifficulties: 1. Individuals or groups do not engage in effective and efficient processes ofinnovative design. 2. The necessary skills, talents, and knowledge sources are notbrought to bear on the problem. 3. Appropriate representations of the situation are notused. Laboratory (e.g., Thomas, 1974; Carroll, et. als, 1980; Farnham, 2000) as well asfield research (e.g., Carroll, Thomas, & Malhotra, 1979; Olson & Bly, 1991; Poltrock &Englebeck, 1999) over the last several decades has established that the major processdifficulties of individuals and groups are mainly due to a limited number of errors andthat these errors can be avoided or ameliorated by providing appropriate structure.The appropriate overall structure for innovation has several substeps and structure isnecessary both to help facilitate the progress through these steps and to help guide theseparate substeps; distinct guidelines are appropriate for each of these substeps (Stein,1974; Thomas, 1989). As an example of a common failure in the overall controlstructure, people typically fail to spend sufficient time in the early stages of design; viz.,problem finding and problem formulation (cf. Sobel, 1995) . As an example of acommon failure during a specific stage of innovative design, people often bring criticaljudgment into play too early in the idea generation phase of problem solving. As anotherexample, empirical evidence shows that, unlike Newell and Simon’s (1972) normativemodel of ideal problem solving, in fact, people’s behavior is path-dependent and they areoften unwilling to take what appears to be a step that undoes a previous action even ifthat step is actually necessary for a solution (Thomas, 1974).Regarding the second issue (bringing to bear necessary skills, talents and knowledgesources), while software tools cannot fully substitute for human experts, evidencesuggests that individuals have a large amount of relevant implicit knowledge which theyoften will not bring to bear on a problem and that giving appropriate strategies (Thomas,1974), or knowledge sources (Thomas, Lyon, and Miller, 1977) can help.Regarding the third issue of appropriate representation, controlled laboratoryexperiments, (e.g., Carroll, Thomas, and Malhotra 1980) have shown that subjects didsignificantly better in a temporal design task when they used a spatial representation; yet,very few subjects spontaneously adopted such a representation. The impact of felicitousrepresentations, however, is not confined to laboratory demonstrations. Speech researchadvancements accelerated greatly when waveforms were largely replaced with speechspectrograms and Feynman diagrams allowed similar breakthroughs in atomic physics.By providing people with a variety of potential representations and some processes toencourage the exploration of various alternative representations, as well as someguidelines linking problem characteristics with appropriate representations, we couldprobably improve performance significantly.Advances in speech recognition, combined with natural language processing and datamining raise the possibility of large-scale real time collaborations. Speech recognitioncan turn raw speech into text. Statistical techniques can automate the formation of“affinity groups” that share various interests, values, or goals (Nishida, 2000). Speech
recognition, in this context, need not be perfect; the purpose is not to produce perfecttranscripts of what is said but to transcribe enough of the content to enable naturallanguage processing software to cluster segments of speech turned text and the peopleassociated with that speech and text.There are additional benefits that could accrue from such a speech to text to clusteringsystem. In the past, conversations were transient. There was no “objective” evidence oftheir content. It often happens, e.g., in a group meeting that the first person to raise anew idea is not recognized as having done so. Instead, the second or third person tomention the idea if often credited with it, quite possibly because the first mention isunassimilable by the current mental model of the listeners but causes a change in mentalmodels so that a subsequent mention is comprehensible. The more general point is thatcomputerized records of group meetings and larger scale collaborations allow thepossibility of feeding back to the participants various visualizations of behavior, makingthe computer an active participant in group communication (Thomas, 1980). Inconjunction with effectiveness metrics, such feedback mechanisms may allow groups toimprove effectiveness. Moreover, at a more general and global level, such informatedsystems may allow the wider community of investigators in the area of social computingto investigate and understand patterns of behavior. At IBM, we are currently engaged ina corporate-wide experiment called “WorldJam” wherein all IBMers worldwide will beinvited to a three day electronic meeting in which we will discuss various issues ofinterest to people in IBM worldwide.Each topic will have a moderator and facilitators. Each moderator, in turn, has beenasked to assemble a “Board of Advisors” -- other people knowledgeable about the topicto provide references, web-sites, and other relevant materials ahead of time as well asparticipation during the on-line conference. In addition, the set of moderators andfacilitators will be communicating with each other through a socially translucent systemcalled “Babble” which was designed, developed, and deployed at IBM Research. TheBabble system blends synchronous and asynchronous communication. Individuals in thesystem are represented as colored dots. The position of a dot within a simplevisualization called a “social proxy” allows each participant to quickly see who else ispresent and which topics are being discussed. When a user of the system types an entryor scrolls through recorded discussion, their dot moves to the center of the social proxyfor that topic. Several “Babbles” are now active within IBM including one for“Community Builders”; that is, people in various organizations throughout IBMinterested in the process, tools, and methods for community building; “KM Blue” whichincludes a similar cross-organizational group interested in knowledge management and“Designers” which brings together people whose primary professional identification is asa designer. In the case or WorldJam, we believe Babble will enable the moderators andfacilitators to trade best practices and engage in joint problem solving in a timelymanner. Additional information about the features, functions, design rationale for andempirical studies of Babble is available in Erickson, et. als. (1999) and Erickson andKellogg (2000). .In earlier work, we showed that the introduction of problem solving aids to break setincreased performance and creativity (Thomas, Lyon, and Miller, 1977) and thatinstructions to take on multiple viewpoints increased problems found in heuristic
evaluation of a software design (Desurvire and Thomas, 1993). Unknown at the time tothe authors, the use of multiple viewpoints has been quite consciously used by theIroquois (and other cultures) for thousands of years (Underwood, 1994). Other writerson creativity have suggested similar methods (See, e.g., Stein, 1977; DeBono, 1985). Asalluded to earlier, a considerable body of empirical research has accrued that documentsboth the strengths and the limitations of human perception, memory, decision making,thinking, and problem solving. Each of these sets of findings, in turn, suggeststechnological aids that may help individuals, teams, and organizations transcend theselimitations and more fully utilize these strengths.For example, Kahneman and Tversky (1973) document some of the typical non-optimalbehaviors we humans tend to engage in with respect to probabilities and prediction. Forinstance, people tend to exhibit a strong primacy effect. Thus, asked for predictions forthe next color ball or for overall frequencies in a population, people will tend to givevery different answers when presented with a supposedly random sample of (Black,Black, White, Black, White, White) than for (White, White, Black, White, Black, Black).An individual tool could be built to potentially help overcome this tendency by takingthis linear sequence and presenting it in various spatial arrangements. A team tool couldbe built that might help teams by presenting various permutations to various teammembers so that the biases of different members would tend to cancel each other out.3. Work of the knowledge socialization group.The work of our own group obviously relates to a tiny area of the vast space outlinedabove. Our work comprises several interlaced threads. In one thread, we areconceptualizing, designing, and building tools to support the creation, capture,organization, understanding, and utilization of stories as a method for groups to build andshare knowledge. In the “Value Miner”, e.g., natural language processing methods areused to find values as expressed in text. This could be applied to conversations,documents, and web-sites as well as stories. The Value Miner finds value-related wordsand phrases and tries to categorize these. A related, “Point Of View” tool shows thevalue similarities and differences of participants. We are also working on storyvisualizations aimed at helping individuals and groups create, understand, and findstories relevant to a situation at hand. For example, in one line of development, we areshowing timelines of plot points and character development. In another line ofrepresentation research, we show a top level view of the kinds of attributes that are usedto describe characters. By clicking on a top level view, the user may zoom onto thevalue associated with that attribute and ultimately to the underlying text. In addition tovisualizations, there are guidelines and measures based on known heuristics of storywriting that can be incorporated into groupware (McKee, 1997; Frey, 1994).In order to provide a common underpinning for the various story related tools that wehave developed, we have proposed a first pass at a “StoryML”; that is, a markuplanguage specifically geared toward stories. In this representation, there are threedifferent but related “views” of story: Story Form (what is in the story); Story Function(what are the purposes of the story); and Story Trace (what is the history of the story). Inturn, the Story Form can be broken down into dimensions of Environment, Character,Plot, and Narrative. The idea of the StoryML is that it is expandable according topurpose. For some purposes, the user (e.g., a student studying mystery plots) may be
satisfied with minimal detail concerning Function and Trace but need to expand certainaspects of the Story Form in great detail. In another context, a different user (e.g., ahistorian comparing certain themes across time and cultures) might have a very highlevel view of Story Form and Story Function but want to provide a detailed descriptionof Story Trace. At this point, the meta-data in StoryML must be supplied by aknowledgeable human being.Once a base of potentially useful stories becomes large in any one collection or domain,it can become a challenge to find the “right” story or stories. If one is looking for storieswith particular objects, people, or places in them, “keyword in context” searches aregenerally sufficient. But, if one is looking for stories about activities, a more subtleapproach is required. In response to this challenge, we have developed a script-basedstory browser. The “script” is a default set of parameters about an activity; it mayspecify roles, goals, objects, and a sequence of events. In the story browser, a user maychoose an activity and find stories related to that activity or related activities through acombination of searching and browsing. Although this activity-based search works at ahigher level of semantics than typical searches, in many cases, a person is searching for astory that illustrates a particular kind of very abstract point and even the particularactivity is not that important. For instance, the story of Odysseus hiding his warriors in aTrojan horse may be applicable in a wide variety of domains such as disease control orcomputer security. In such cases, to find stories that are potentially applicable, we reallyneed a system based on abstract planning and problem solving strategies. In our lab,Andrew Gordon has developed such an ontology for abstract planning and problemsolving by interviewing experts and reading strategy books in a wide variety of domainsand then formulating these strategies in abstract terms. In the next step, these terms canbe used to categorize stories according to the strategies that are utilized. This will enableindividual problem solvers, educators, and teams to find stories that are potentiallyapplicable to improving specific situations or solving particular problems.We are also engaged in attempting to extend the architect Christopher Alexander’s(1977) concept of a Pattern Language to stories. A Pattern Language consists of a latticeof interrelated patterns. Each pattern has a Title, a description of a context in which aproblem is likely to occur, a description of opposing forces, and the basic outline of asolution. A pattern also often contains a diagram illustrating the basic solution, and maycontain references or other evidence about its efficacy. Each pattern also includes linksto higher level and lower level patterns. The notions of patterns and A Pattern Languagehave been applied to a variety of fields besides architecture including object-orientedprogramming (Gamma, et. als, 1995), project structure (Coplien, 2001) and human-computer interaction (Borchers, 2001). Typically, a Pattern Language is developed by acommunity of practice as a way to create, organize and reuse knowledge.Other recent work in our group (Gordon, 1999) focuses on helping people bring to bearappropriate strategies. Our first design is for a system that maps text (e.g., on-linediscussion group statements) onto strategies consists of a two-step algorithm. The firststep is to recognize the presence of particular feature sets in input text. Severaltechniques may be appropriate; e.g., to hand-author a finite-state graph for text analysisfor each of the features, and normalize their comparative effectiveness based onminimizing error-rate. The second step is to select a set of strategies potentially relevantto the case at hand from a large collection. Our previous representation work has
identified over a thousand abstract features that could be used to make this decision usinga straightforward voting algorithm.Providing people engaged in design problem solving with a wider array of potentialstrategies is just one avenue that we are exploring; additional experimental software aidswill similarly deal with aiding process control. We are also building tools to incorporateprocess guidelines to facilitate various kinds of meetings including synectics (a structuredkind of brainstorming), and Bohm Dialogue (Bohm, 1996). We will be applying thesetechniques in the context of helping people do “out of the box” thinking in a large scaleeffort to improve IBM’s Worldwide fulfillment process.Our attempts to provide additional knowledge sources are focused mainly on teachingstories (Thomas, 1999), particularly during specific stages of problem solving. Forexample, the story “Who Speaks for Wolf” by Paula Underwood (1994) is a storyespecially well-suited to either problem formulation or to a last minute check that allstakeholders’ concerns are covered before significant resources are committed to aparticular plan. In other cases, the individual, team, or organization will need to use astory browser whose expanding capabilities are outlines above.In this paper, we have attempted to do three things. 1. Convince the reader thatimproving and understanding the ability of individuals, teams, and organizations toinnovate more effectively is key to our collective survival. 2. Outline how recentadvances in science and technology offer a promise to enhance collaborative innovation.3. Describe in outline the small contributions the specific research along these lines ofthe IBM Research Knowledge Socialization Group.4. References.* Ahlberg, C., Williamson, C. and Shneiderman, B. Dynamic Queries for informationexploration: an implementation and evaluation. In B. Shneiderman (Ed.) Sparks ofinnovation in human computer interaction. Norwood, NJ: Ablex, 1993.* Alexander, C., Ishikawa, S., Silverstein, M. Jacobson, M., Fiksdahl-King, I. and Angel,S. (1977). A pattern language. New York: Oxford Univeristy Press.* Azechi, S. Social psychological approach to knowledge-creating communities. In T.Nishida (Ed.), Dynamic knowledge interaction. Boca Raton: CRC Press, 2000.* Beyer, H. and Holtzblatt, K. Contextual design: defining customer-centered systems.San Francisco: Morgan Kaufman, 1998.* Bodker, SA. Scenarios in user-centered design: setting the stage for reflection andaction. Presented at the 32nd annual Hawaii International Conference on SystemScience, January, 1999, Maui, Hawaii* Bohm, D. On dialogue. London: Routledge, 1996.* Borchers, J. A patterns approach to interaction design. New York: Wiley, 2001.* Carroll, J., Thomas, J.C. and Malhotra, A. Presentation and representation in designproblem solving. British Journal of Psychology, 71 (1), pp. 143-155, 1980.* Collins, J. and Porras, J. 1994. Built to last. New York: Harper.* Coplien, James. http://www1.bell-labs.com/user/cope/Patterns/Process/index.html* De Bono, E. Six thinking hats. Boston: Little, Brown, 1985.* DeGeus, A. 1997. The living company: habits for survival in a turbulent businessenvironment. Boston: Harvard Business School Press,.
* Desurvire, H. and Thomas, J. Enhancing the performance of interface evaluators usingnon-empirical usability methods. Proceedings of the 37th Annual Human FactorsSociety Meeting, 1132-1136, Santa Monica, CA: Human Factors Society, 1993.* Drucker, P. 1995. Managing in a time of great change. Truman Talley Books: New York.* Erickson, T., Smith, D. Kellogg, W., Laff, M., Richards, J. and Bradner, E. (1999).Socially translucent systems: Social proxies, persistent conversation and the design of“Babble.” In Human Factors and Computing Systems: The proceedings of CHI‘99. NewYork: ACM Press.* Erickson, T. & Kellogg, W. "Social Translucence: An Approach to Designing Systemsthat Mesh with Social Processes." Transactions on Computer-Human Interaction, 7(1),59-83, 2000.* Farnham, S. et. als. 2000. Structured online interactions: Improving the decision-making of smalldiscussion groups. Proceedings of CSCW 2000. pp. 299-308. New York: ACM.* Finholt, T.A., and Olson, G.M., "From Laboratories to Collaboratories: A NewOrganizational Form for Scientific Collaboration," University of Michigan, Ann Arbor,January 1997.* Fischer, G. Domain-Oriented Design Environments: Supporting Individual and SocialCreativity", in J. Gero and M.L. Maher (eds): "Computational Models of Creative DesignIV", Key Centre of Design Computing and Cognition, Syndney, Australia, 1999, pp83-111.* Frey, J. How to write a damned good novel II. New York: St. Martin’s Press, 1994.*Gamma, E., Helm, R., Johnson, R., and Vlissides, J. Design Patterns: Elements ofReusable Object Oriented Software. Reading, MA: Addison-Wesley, 1995.* Gordon, A. 2001. Playing chess with Machiavelli: Improving interactive entertainment with explicitstrategies. AAAI Spring Symposium, Stanford.* Kahneman, D. & Tversky, A. (1973). On the psychology of prediction. PsychologicalReview, 80, 237-251.* McKee, R. Story: Substance, structure, style and the principles of screenwriting. NewYork: Harper, 1997.* Newell, A. and Simon, H. Human problem solving. Upper Saddle River, NJ: Prentice-Hall.* Nishida, T. Dynamic knowledge interaction. Boca Raton: CRC Press, 2000.* Olson, M. and Bly, S. 1991. The Portland experience: A report on a distributed research group.International Journal of Man-Machine Studies, 34, 211-228.* Ornstein, R. and Ehrlich, P. New world new mind. NY: Simon and Schuster, 1989.* Poltrock, S. and Englebeck, G. 1999. Requirements for a virtual collocation environment, Informationand Software Technology, 41(6), 331-339.* Sobel, D. 1995. Longitude: The true story of a lone genius who solved the greatest scientific problemof his time. New York: Penguin.* Stein, M. Stimulating creativity. New York: Academic Press, 1974.*Thomas, J. 1974. An analysis of behavior in the hobbits-orcs problem. Cognitive Psychology, 6,257-269.* Thomas, J. , Lyon, D., and Miller, L. Aids for problem solving. IBM T. J. WatsonResearch Report, RC-6468. New York: IBM, 1977.* Thomas, J. A design-interpretation analysis of natural English. International Journalof Man-Machine Studies, 10,651-668, 1978.* Thomas, J. and Carroll, J. The psychological study of design. Design Studies, 1(1),5-11, 1979.
* Thomas, J. C. The computer as an active communications medium. Proceedings of the18th Annual Meeting of the Association for Computational Linguistics, 83-86, NewYork: ACL, 1980.* Thomas, J. Studies in office systems: The effect of communication medium on personperception. Office Systems Research Journal 1(2), 75-88, 1983.* Thomas, J. Problem solving by human-machine interaction. In Gilhooly K.J., (Ed).Human and machine problem solving. London: Plenum Publishing, 1989.* Thomas, J. Narrative technology and the new millennium. Knowledge ManagementJournal, 2(9), 14-17, 1999.* Thomas, J. An HCI agenda for the next millennium: Emergent global intelligence. InR. Earnshaw, R. Guedy, A. van Dam and J. Vince (Eds.), Frontiers of Human-CentredComputing, Online Communities and Virtual Environments. In Press.* Tufte, E. Visual explanations: images and quantiles, evidence and narrative. Cheshire,CT: Graphics Press, 1997.* Underwood, P. Three Native American learning stories. Georgetown, Texas: Tribe ofTwo Press, 1994.* Van Der Heijden, K. Scenarios: The art of strategic conversation. New York: Wiley,1996.*Zheng, J., Bos, N. Olson, J. and Olson, G. Trust Without Touch: Jump-Start Trust WithSocial Chat. Proceedings of CHI 01 (Conference Companion), New York: ACM, 2001.