Paper presented at JSAI on collaborative innovation tools

1. Collaborative Innovation Tools
John C. Thomas
IBM T. J. Watson Research
PO Box 704, Yorktown Heights New York 10598 USA
1. Importance of Collaboration: Practical and Scientific.
From practical and economic perspectives, we live in an increasingly interconnected
world. In reflection of this trend, the field of human-computer interaction has shifted
focus from supporting the productivity of individual workers to teams and large
organizations (Thomas, In Press). From a scientific perspective, we learn most about the
object of study during transitions and adaptations. Thus, a learning test is generally more
diagnostic of brain function than a test of stored knowledge; a glucose tolerance test tells
us more than a resting blood sugar level; a stress test reveals more about the heart than
does resting heart rate. Similarly, this century’s rapid transitions should allow us to
understand more about collective human behavior than ever before possible. At the same
time, we still face enormous planetary problems potentially including but not limited to
global fouling of the ecosphere, inequity in economic opportunity, increased chances for
catastrophic disease, and international terrorism. Such planetary problems arose with
current approaches and limitations to collaboration and probably will only be solved via
breakthroughs in collaboration.
From a much more prosaic and practical point of view, a similar set of challenges arises
for large, international organizations today. For instance, the world is changing more
and more quickly but the ability of people to creatively design has not increased in any
noticeable way. As a result, there is a widening gap between the degree of flexibility and
creativity that is needed to adapt and the capacity of individuals and organizations to do
so (Drucker, 1995). Yet, design problems are often extremely high leverage problems for
organizations. For instance, errors in design, whether in software, drugs, business
processes, or automobiles are extremely costly, compared, for instance, with coding
errors or manufacturing errors. Conversely, effective and innovative designs can be
extremely lucrative; are one of the hallmarks of long-lived companies (Collins and
Porras, 1994; DeGeus, 1997). Even a modest increase in the ability of organizations to
create more effective designs could greatly reduce costs in existing markets and create
whole new markets. Again, increasing the effectiveness of design will require
breakthroughs in collaboration.
Human beings evolved natural language as a method for collaboration among small
groups of people who generally shared context, goals, experience and culture. Under
those circumstances, sequential human speech served fairly well, e.g., the telling of
stories for sharing experiences (Thomas, 1999). However, unaided speech is not well-
suited to large-scale collaborations among people; particularly not when the people
involved may have vastly different sets of assumptions, cultural backgrounds, goals,
contexts, experiences and even different native languages. We have not yet invented an
entirely effective replacement of natural language for large, diverse groups though
storytelling can be useful in bridging some gaps among groups when incorporated into
the appropriate process (Van Der Heijden, 1996; Beyer & Holtzblatt, 1998; Bodker,
1999). Can we extend such techniques even further to facilitate communication among

2. 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 worldwide
or at the very least involves remote participants. In many conversations and papers, it
appears that an assumption, often implicit, is that remote collaboration is limited by
bandwidth alone and that the superiority of face to face collaboration over remote
collaboration will disappear once bandwidth becomes large enough for us to clearly see
the details and subtleties of other people’s faces and to clearly hear the subtleties of other
people’s voices; perhaps other senses could also be transmitted. But such an analysis
overlooks two additional and potentially quite important aspects of face to face
collaboration.
First, face to face collaboration typically means that people get to see and experience
some of the physical and social context of their collaborators. They see the building
perhaps where others work; try the same food; find out whether they are working in a
quiet 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 deeper
interaction and that possibility may well affect trust even if the possibility never
materializes. Consider two rather extreme examples. First, two people sharing a
physical space may be subject to a natural disaster such as an earthquake and one may
save the life of the other. Although this is obviously a very low probability event, the
mere possibility may well put people’s perceptual and emotional apparatus into a
heightened state of arousal. Second, if two people share a common physical space, one
could strike out and physically injure the other. Since A’s trust of B is enhanced by
situations wherein A could hurt B but in fact, does not, the typical face to face interaction
may enhance trust in just this way.
We should note however that it is not only the medium of communication and the
context that impact collaboration, but also the content. In particular, we argue that
expressive communications may offer an opportunity for collaborators to gain more
comprehensive models of each other than instrumental communication alone.
Instrumental communication would constitute communication that is required to
accomplish the current task. Expressive communication is communication that tells
more about the communicator than about the subject; it is communicated more because
the communicator wants to than because they need to.
Zheng, Bos, Olson, and Olson, (2001) showed that collaboration and trust can be, in
effect, “jump-started” with social chitchat. We have had some practical experience in
several business contexts (but no rigorous empirical results yet) to indicate that stories
can also help people develop more trust than the exchange of information per se. A story
is not simply an objective recounting of events; it always implies a number of revealing
choices. The storyteller chooses which events to talk about; they choose where to start
the 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 as
about the subject. The listener then has data from which to learn about the storyteller as
well as about the subject of the story.

3. So long as collaboration proceeds along predictable lines; e.g., if two employees of a
corporation are simply following a procedure, the models built from expressive
communication may not be necessary or important. But, if the procedure breaks down or
becomes irrelevant, then collaborators who have developed more complex models of
each 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 efficiency
presumes that the team will put group goals ahead of individual goals or even intentional
grudges. As perhaps hinted at by Azechi (2000), stories might also reveal characteristics
of the storyteller that other collaborators might find quite negative while purely
instrumental communications are unlikely to do so.
People evolved a communication system adapted to small tribes, but on an even longer
evolutionary scale, animals in general also evolved so as to be sensitive to sudden
changes in sound, illumination and other sensory input. In the “natural” environment of
100,000,000 or even 10,000 years ago, such perceptual biases were conducive to
survival. Today, these same perceptual and attentional predispositions guide our current
actions; e.g., observers are held rapt by high-speed chase scenes in movies and television
though such scenes have little if any actual survival value in the observers’ real lives. By
contrast, many of today’s real problems such as overpopulation and global warming are
too 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 more
systemic, collaborative and productive thinking? We know that changing representations
can make isomorphic problems easy or difficult (Ahlberg, Williamson, and Shneiderman,
1993; Carroll, Thomas, and Malhotra, 1980; Tufte, 1997). Perhaps we can present
important but non-obvious problems in a way that helps utilize our natural perceptual
capabilities. For example, we could show people pictures of a coral reef taken from
thirty years ago and taken again today. In this “time lapse” technique, the devastating
and widespread effects of pollution can be made more visible and salient. We could
show “extrapolative” movies illustrating what the impact on the world will be if all 8
billion people on earth produced as much pollution as the typical American.
Similar issues of having large groups of people understand a more global and more
systems view occur even in the context of a purely commercial organization. A standard
and widespread challenge is to motivate people in a large organization to share their
knowledge with others. Taking the time to document lessons learned does not seem
particularly motivating to the individual employee whose individual rewards would
probably be maximized by moving on quickly to the next project. Yet, the organization
as a whole loses valuable knowledge by this type of shortsightedness.
2. New technological possibilities.
Recent advances in computing power, interface technologies, bandwidth, storage, and
social engineering provide a broad field of possibilities from which novel solutions to
large 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 believe
further advances can be made by incorporating creativity aids, suggestions for processes

4. (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 main
difficulties: 1. Individuals or groups do not engage in effective and efficient processes of
innovative design. 2. The necessary skills, talents, and knowledge sources are not
brought to bear on the problem. 3. Appropriate representations of the situation are not
used. Laboratory (e.g., Thomas, 1974; Carroll, et. als, 1980; Farnham, 2000) as well as
field research (e.g., Carroll, Thomas, & Malhotra, 1979; Olson & Bly, 1991; Poltrock &
Englebeck, 1999) over the last several decades has established that the major process
difficulties of individuals and groups are mainly due to a limited number of errors and
that these errors can be avoided or ameliorated by providing appropriate structure.
The appropriate overall structure for innovation has several substeps and structure is
necessary both to help facilitate the progress through these steps and to help guide the
separate substeps; distinct guidelines are appropriate for each of these substeps (Stein,
1974; Thomas, 1989). As an example of a common failure in the overall control
structure, 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 a
common failure during a specific stage of innovative design, people often bring critical
judgment into play too early in the idea generation phase of problem solving. As another
example, empirical evidence shows that, unlike Newell and Simon’s (1972) normative
model of ideal problem solving, in fact, people’s behavior is path-dependent and they are
often unwilling to take what appears to be a step that undoes a previous action even if
that step is actually necessary for a solution (Thomas, 1974).
Regarding the second issue (bringing to bear necessary skills, talents and knowledge
sources), while software tools cannot fully substitute for human experts, evidence
suggests that individuals have a large amount of relevant implicit knowledge which they
often 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 laboratory
experiments, (e.g., Carroll, Thomas, and Malhotra 1980) have shown that subjects did
significantly better in a temporal design task when they used a spatial representation; yet,
very few subjects spontaneously adopted such a representation. The impact of felicitous
representations, however, is not confined to laboratory demonstrations. Speech research
advancements accelerated greatly when waveforms were largely replaced with speech
spectrograms and Feynman diagrams allowed similar breakthroughs in atomic physics.
By providing people with a variety of potential representations and some processes to
encourage the exploration of various alternative representations, as well as some
guidelines linking problem characteristics with appropriate representations, we could
probably improve performance significantly.
Advances in speech recognition, combined with natural language processing and data
mining raise the possibility of large-scale real time collaborations. Speech recognition
can turn raw speech into text. Statistical techniques can automate the formation of
“affinity groups” that share various interests, values, or goals (Nishida, 2000). Speech

5. recognition, in this context, need not be perfect; the purpose is not to produce perfect
transcripts of what is said but to transcribe enough of the content to enable natural
language processing software to cluster segments of speech turned text and the people
associated with that speech and text.
There are additional benefits that could accrue from such a speech to text to clustering
system. In the past, conversations were transient. There was no “objective” evidence of
their content. It often happens, e.g., in a group meeting that the first person to raise a
new idea is not recognized as having done so. Instead, the second or third person to
mention the idea if often credited with it, quite possibly because the first mention is
unassimilable by the current mental model of the listeners but causes a change in mental
models so that a subsequent mention is comprehensible. The more general point is that
computerized records of group meetings and larger scale collaborations allow the
possibility of feeding back to the participants various visualizations of behavior, making
the computer an active participant in group communication (Thomas, 1980). In
conjunction with effectiveness metrics, such feedback mechanisms may allow groups to
improve effectiveness. Moreover, at a more general and global level, such informated
systems may allow the wider community of investigators in the area of social computing
to investigate and understand patterns of behavior. At IBM, we are currently engaged in
a corporate-wide experiment called “WorldJam” wherein all IBMers worldwide will be
invited to a three day electronic meeting in which we will discuss various issues of
interest to people in IBM worldwide.
Each topic will have a moderator and facilitators. Each moderator, in turn, has been
asked to assemble a “Board of Advisors” -- other people knowledgeable about the topic
to provide references, web-sites, and other relevant materials ahead of time as well as
participation during the on-line conference. In addition, the set of moderators and
facilitators will be communicating with each other through a socially translucent system
called “Babble” which was designed, developed, and deployed at IBM Research. The
Babble system blends synchronous and asynchronous communication. Individuals in the
system are represented as colored dots. The position of a dot within a simple
visualization called a “social proxy” allows each participant to quickly see who else is
present and which topics are being discussed. When a user of the system types an entry
or scrolls through recorded discussion, their dot moves to the center of the social proxy
for that topic. Several “Babbles” are now active within IBM including one for
“Community Builders”; that is, people in various organizations throughout IBM
interested in the process, tools, and methods for community building; “KM Blue” which
includes a similar cross-organizational group interested in knowledge management and
“Designers” which brings together people whose primary professional identification is as
a designer. In the case or WorldJam, we believe Babble will enable the moderators and
facilitators to trade best practices and engage in joint problem solving in a timely
manner. Additional information about the features, functions, design rationale for and
empirical studies of Babble is available in Erickson, et. als. (1999) and Erickson and
Kellogg (2000). .
In earlier work, we showed that the introduction of problem solving aids to break set
increased performance and creativity (Thomas, Lyon, and Miller, 1977) and that
instructions to take on multiple viewpoints increased problems found in heuristic

6. evaluation of a software design (Desurvire and Thomas, 1993). Unknown at the time to
the authors, the use of multiple viewpoints has been quite consciously used by the
Iroquois (and other cultures) for thousands of years (Underwood, 1994). Other writers
on creativity have suggested similar methods (See, e.g., Stein, 1977; DeBono, 1985). As
alluded to earlier, a considerable body of empirical research has accrued that documents
both the strengths and the limitations of human perception, memory, decision making,
thinking, and problem solving. Each of these sets of findings, in turn, suggests
technological aids that may help individuals, teams, and organizations transcend these
limitations and more fully utilize these strengths.
For example, Kahneman and Tversky (1973) document some of the typical non-optimal
behaviors we humans tend to engage in with respect to probabilities and prediction. For
instance, people tend to exhibit a strong primacy effect. Thus, asked for predictions for
the next color ball or for overall frequencies in a population, people will tend to give
very 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 taking
this linear sequence and presenting it in various spatial arrangements. A team tool could
be built that might help teams by presenting various permutations to various team
members 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 outlined
above. Our work comprises several interlaced threads. In one thread, we are
conceptualizing, designing, and building tools to support the creation, capture,
organization, understanding, and utilization of stories as a method for groups to build and
share knowledge. In the “Value Miner”, e.g., natural language processing methods are
used 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 words
and phrases and tries to categorize these. A related, “Point Of View” tool shows the
value similarities and differences of participants. We are also working on story
visualizations aimed at helping individuals and groups create, understand, and find
stories relevant to a situation at hand. For example, in one line of development, we are
showing timelines of plot points and character development. In another line of
representation research, we show a top level view of the kinds of attributes that are used
to describe characters. By clicking on a top level view, the user may zoom onto the
value associated with that attribute and ultimately to the underlying text. In addition to
visualizations, there are guidelines and measures based on known heuristics of story
writing that can be incorporated into groupware (McKee, 1997; Frey, 1994).
In order to provide a common underpinning for the various story related tools that we
have developed, we have proposed a first pass at a “StoryML”; that is, a markup
language specifically geared toward stories. In this representation, there are three
different 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). In
turn, 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 to
purpose. For some purposes, the user (e.g., a student studying mystery plots) may be

7. satisfied with minimal detail concerning Function and Trace but need to expand certain
aspects of the Story Form in great detail. In another context, a different user (e.g., a
historian comparing certain themes across time and cultures) might have a very high
level view of Story Form and Story Function but want to provide a detailed description
of Story Trace. At this point, the meta-data in StoryML must be supplied by a
knowledgeable 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 stories
with particular objects, people, or places in them, “keyword in context” searches are
generally sufficient. But, if one is looking for stories about activities, a more subtle
approach is required. In response to this challenge, we have developed a script-based
story browser. The “script” is a default set of parameters about an activity; it may
specify roles, goals, objects, and a sequence of events. In the story browser, a user may
choose an activity and find stories related to that activity or related activities through a
combination of searching and browsing. Although this activity-based search works at a
higher level of semantics than typical searches, in many cases, a person is searching for a
story that illustrates a particular kind of very abstract point and even the particular
activity is not that important. For instance, the story of Odysseus hiding his warriors in a
Trojan horse may be applicable in a wide variety of domains such as disease control or
computer security. In such cases, to find stories that are potentially applicable, we really
need a system based on abstract planning and problem solving strategies. In our lab,
Andrew Gordon has developed such an ontology for abstract planning and problem
solving by interviewing experts and reading strategy books in a wide variety of domains
and then formulating these strategies in abstract terms. In the next step, these terms can
be used to categorize stories according to the strategies that are utilized. This will enable
individual problem solvers, educators, and teams to find stories that are potentially
applicable 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 lattice
of interrelated patterns. Each pattern has a Title, a description of a context in which a
problem is likely to occur, a description of opposing forces, and the basic outline of a
solution. A pattern also often contains a diagram illustrating the basic solution, and may
contain references or other evidence about its efficacy. Each pattern also includes links
to higher level and lower level patterns. The notions of patterns and A Pattern Language
have been applied to a variety of fields besides architecture including object-oriented
programming (Gamma, et. als, 1995), project structure (Coplien, 2001) and human-
computer interaction (Borchers, 2001). Typically, a Pattern Language is developed by a
community 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 bear
appropriate strategies. Our first design is for a system that maps text (e.g., on-line
discussion group statements) onto strategies consists of a two-step algorithm. The first
step is to recognize the presence of particular feature sets in input text. Several
techniques may be appropriate; e.g., to hand-author a finite-state graph for text analysis
for each of the features, and normalize their comparative effectiveness based on
minimizing error-rate. The second step is to select a set of strategies potentially relevant
to the case at hand from a large collection. Our previous representation work has

8. identified over a thousand abstract features that could be used to make this decision using
a straightforward voting algorithm.
Providing people engaged in design problem solving with a wider array of potential
strategies is just one avenue that we are exploring; additional experimental software aids
will similarly deal with aiding process control. We are also building tools to incorporate
process guidelines to facilitate various kinds of meetings including synectics (a structured
kind of brainstorming), and Bohm Dialogue (Bohm, 1996). We will be applying these
techniques in the context of helping people do “out of the box” thinking in a large scale
effort to improve IBM’s Worldwide fulfillment process.
Our attempts to provide additional knowledge sources are focused mainly on teaching
stories (Thomas, 1999), particularly during specific stages of problem solving. For
example, the story “Who Speaks for Wolf” by Paula Underwood (1994) is a story
especially well-suited to either problem formulation or to a last minute check that all
stakeholders’ concerns are covered before significant resources are committed to a
particular plan. In other cases, the individual, team, or organization will need to use a
story browser whose expanding capabilities are outlines above.
In this paper, we have attempted to do three things. 1. Convince the reader that
improving and understanding the ability of individuals, teams, and organizations to
innovate more effectively is key to our collective survival. 2. Outline how recent
advances in science and technology offer a promise to enhance collaborative innovation.
3. Describe in outline the small contributions the specific research along these lines of
the IBM Research Knowledge Socialization Group.
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