Eight to Late

Sensemaking and Analytics for Organizations

Archive for July 2009

IBIS, dialogue mapping, and the art of collaborative knowledge creation

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Introduction

In earlier posts  I’ve described a notation called IBIS (Issue-based information system), and demonstrated its utility in visualising reasoning and resolving complex issues through dialogue mapping.  The IBIS notation consists of just three elements (issues, ideas and arguments) that can be connected in a small number of ways. Yet, despite these limitations, IBIS has been found to enhance creativity when used in collaborative design discussions.  Given the simplicity of the notation and grammar, this claim is surprising,  even paradoxical.  The present post  resolves this paradox by viewing  collaborative knowledge creation as an art, and considers the aesthetic competencies required to facilitate this art.

Knowledge art

In a position paper entitled, The paradox of the “practice level” in collaborative design rationale, Al Selvin draws an analogy between design  discussions using Compendium (an open source IBIS-based argument mapping tool)  and art.  He uses the example of the artist Piet Mondrian, highlighting the difference in  style between Mondrian’s earlier and later work. To quote from the paper,

Whenever I think of surfacing design rationale as an intentional activity — something that people engaged in some effort decide to do (or have to do), I think of Piet Mondrian’s approach to painting in his later years. During this time, he departed from the naturalistic and impressionist (and more derivative, less original) work of his youth (view an image here) and produced the highly abstract geometric paintings (view an image here) most associated with his name…

Selvin points out that the difference between the first and the second paintings is essentially one of abstraction: the first one is almost instantly recognisable as a depiction of dunes on a beach whereas the second one, from Mondrian’s minimalist period, needs some effort to understand and appreciate, as it uses a very small number of elements to create a specific ambience. To quote from the paper again,

“One might think (as many in his day did) that he was betraying beauty, nature, and emotion by going in such an abstract direction. But for Mondrian it was the opposite. Each of his paintings in this vein was a fresh attempt to go as far as he could in the depiction of cosmic tensions and balances. Each mattered to him in a deeply personal way. Each was a unique foray into a depth of expression where nothing was given and everything had to be struggled for to bring into being without collapsing into imbalance and irrelevance. The depictions and the act of depicting were inseparable. We get to look at the seemingly effortless result, but there are storms behind the polished surfaces. Bringing about these perfected abstractions required emotion, expression, struggle, inspiration, failure and recovery — in short, creativity…”

In analogy, Selvin contends that a group of people who work through design issues using a minimalist notation such as IBIS can generate creative new ideas. In other words:  IBIS, when used in a group setting such as dialogue mapping,  can become a vehicle for collaborative creativity. The effectiveness of the tool, though, depends on those who wield it:

“…To my mind using tools and methods with groups is a matter of how effective, artistic, creative, etc. whoever is applying and organizing the approach can be with the situation, constraints, and people. Done effectively, even the force-fitting of rationale surfacing into a “free-flowing” design discussion can unleash creativity and imagination in the people engaged in the effort, getting people to “think different” and look at their situation through a different set of lenses. Done ineffectively, it can impede or smother creativity as so many normal methods, interventions, and attitudes do…”

Although Selvin’s discussion is framed in the context of design discussions using Compendium,  this is but dialogue mapping by another name.  So,  in essence, he  makes a case for viewing the collaborative generation of knowledge (through dialogue mapping or any other means) as an art.  In fact, in another article, Selvin uses the term knowledge art to describe both the process and the product of creating knowledge as discussed above.   Knowledge Art as he sees it, is a marriage of the two forms of discourse that make up the term. On the one hand, we have knowledge which, “… in an organizational setting, can be thought of as what is needed to perform work; the tacit and explicit concepts, relationships, and rules that allow us to know how to do what we do.” On the other, we have art which “… is concerned with heightened expression, metaphor, crafting, emotion, nuance, creativity, meaning, purpose, beauty, rhythm, timbre, tone, immediacy, and connection.”   

Facilitating collaborative knowledge creation

In the business world, there’s never enough time to deliberate or think through ideas (either individually or collectively): everything is done in a hurry and the result is never as good as it should or could be; the picture never quite complete.  However, as Selvin says,

“…each moment (spent discussing or thinking through ideas or designs) can yield a bit of the picture, if there is a way to capture the bits and relate them, piece them together over time. That capturing and piecing is the domain of Knowledge Art. Knowledge Art requires a spectrum of skills, regardless of how it’ practiced or what form it takes. It means listening and paying attention, determining the style and level of intervention, authenticity, engagement, providing conceptual frameworks and structures, improvisation, representational skill and fluidity, and skill in working with electronic information…”

So,  knowledge art requires a wide range of technical and non-technical skills.  In previous posts  I’ve discussed some of  technical skills required – fluency with IBIS, for example.  Let’s now look at  some of the non-technical competencies.

What are the competencies needed for collaborative knowledge creation?  Palus and Horth offer some suggestions in their paper entitled, Leading Complexity; The Art of Making Sense.  They define the concept of  creative leadership as making shared sense out of complexity and chaos and the crafting of meaningful action.  Creative leadership is akin to dialogue mapping, which Jeff Conklin describes as  a means to achieve a shared understanding of wicked problems  and a shared commitment to solving them.  The connection between creative leadership and dialogue mapping is apparent once one notices the similarity between their definitions.  So the  competencies  of creative leadership should apply to dialogue mapping (or collaborative knowledge creation)  as well.

Palus  and Horth describe  six basic competencies of creative leadership. I outline these below, mentioning  their relevance to dialogue mapping:

Paying Attention:  This refers to the ability to slow down discourse  with the aim of  achieving a deep understanding of the issues at hand. A skilled dialogue mapper has to be able to listen; to pay attention to what’s being said.

Personalizing:  This refers to the ability to draw upon personal experiences, interests and passions whilst engaged in work. Although the connection to dialogue mapping isn’t immediately evident, the point Palus and Horth make is that the ability to make connections between work and one’s interests and passions helps increase involvement, enthusiasm and motivation in tackling work challenges.

Imaging:  This refers to the ability to visualise problems so as  to understand them better,  using metaphors, pictures stories etc to stimulate imagination, intuition and understanding. The connection to dialogue mapping is clear and needs no elaboration.

Serious play: This refers to the ability to experiment with new ideas; to learn by trying and doing in a non-threatening environment. This is something that software developers do when learning new technologies. A group engaged in a dialogue mapping must have a sense of play; of trying out new ideas, even if they seem somewhat unusual.

Collaborative enquiry: This refers to the ability to  sustain productive dialogue in a diverse group of stakeholders. Again, the connection to dialogue mapping is evident.

Crafting: This refers to the ability to synthesise issues, ideas, arguments and actions into coherent, meaningful wholes. Yet again, the connection to dialogue mapping is clear – the end product is ideally a shared understanding of the problem and a shared commitment to a meaningful solution.

Palus and Horth suggest that these competencies have been ignored in the business world because:

  1. They are seen as threatening the status quo (creativity is to feared because it invariably leads to changes).
  2. These competencies are aesthetic, and the current emphasis on scientific management devalues competencies that are not rational or analytical.

The irony is that creative scientists have these aesthetic competencies (or qualities) in spades. At the most fundamental level science is an art – it is about constructing theories or designing experiments that make sense of the world. Where do the ideas for these new theories or experiments come from? Well, they certainly aren’t out there in the objective world; they come from the imagination of the scientist. Science, in the real sense of the word, is knowledge art. If these competencies are useful in science, they should be more than good enough for the business world.

Summing up

To sum up:  knowledge creation in an organisational context is best viewed as an art – a collaborative art.  Visual representations such as IBIS provide a medium to capture snippets of knowledge and relate them, or  piece them together over time. They provide the canvas, brush and paint to express knowledge as art  through the process of dialogue mapping.

Maintenance matters

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Corporate developers spend majority of their programming time doing maintenance work.   My basis for this claim is two years worth of statistics that I have been gathering at my workplace. According to these figures, my group spends about 65 percent of their programming time on maintenance  (with  some developers spending considerably more, depending on the applications they support).   I suspect these numbers are applicable to most corporate IT shops – and possibly, to a somewhat smaller extent, to  software houses as well.  Unfortunately, maintenance work is often looked upon as being “inferior to” development.  This being the case,  it is worth dispelling some myths  about maintenance programming.  As it happens, I’ve just finished reading Robert Glass‘ wonderful book,  Facts and Fallacies of Software Engineering, in which he presents some interesting facts about software maintenance (among lots of other interesting facts).  This post looks at these facts which, I think,  some readers may find surprising.

Let’s get right to it.  Fact 41 in the book reads:

Maintenance typically consumes 40 to 80 percent (average 60 percent) of software costs. Therefore, it is probably the most important life cycle phase of software.

 Surprised? Wait, there’s more: Fact 42 reads: 

Enhancement is responsible for roughly 60 percent of software maintenance costs. Error correction is roughly 17 percent. Therefore software maintenance is largely about adding new capability to old software, not fixing it.

 As a corollary to Fact 42, Glass unveils Fact 43, which simply states that:

 Maintenance is a solution, not a problem.

 Developers who haven’t done any maintenance work may be surprised by these facts. Most corporate IT developers have done considerable maintenance time; so no one in my mob was  surprised when I mentioned these during a coffee break conversation.  Based on the number   quoted in the first paragraph (65 percent maintenance) and Glass’s figure (60 percent of maintenance is modification work), my colleagues  spend close to 40 percent of their time of  enhancing existing applications. All of them reckon this number is about right, and their thinking is  supported by my data.

 A few weeks ago, I wrote a piece entitled the legacy of legacy software in which I pointed out that legacy code is a problem for historians and programmers alike. Both have to understand legacy code, albeit in different ways. The historian needs to understand how it developed over the years so that he can understand its history; why it is the way it is and what made it so. The programmer has a more pragmatic interest – she needs to understand how it works so that she can modify it.  Now, Glass’ Fact 42 tells us that much of maintenance work is adding new functionality. New functionality implies new code, or at least substantial modifications of existing code.  Software is therefore  a palimpsest – written once, and then overwritten again and again.

The maintenance programmer whose job it is to modify legacy code has to first understand it. Like a historian or archaeologist decoding a palimpsest, she has to sort through layers of modifications made by different people at different times for different reasons. The task is often made harder by the fact that modifications are often under-documented (if not undocumented).   In Fact 44 of the book,   Glass states that this effort of understanding code – an effort that he calls undesign – makes up about 30 percent of the total time spent in maintenance. It is therefore the most significant maintenance activity.

But that’s not all.  After completing “undesign” the maintenance programmer has to design the enhancement within the context of the existing code – design under constraints, so to speak.   There are at least a couple of reasons why this is hard.  First,  as Brooks tells us in No Silver Bullet — design itself is hard work; it is one of the essential difficulties of software engineering.  Second, the original design is created with a specific understanding of requirements.  By the time modifications come around, the requirements may have changed substantially. These new requirements may conflict with the original design.  If so, the maintenance task becomes that much harder.

 Ideally, existing design documentation should ease the burden on the maintenance programmer. However it rarely does because such documentation is typically created in the design phase – and rarely modified to reflect design changes as the product is built. As a consequence, most design documentation is hopelessly out of date by the time the original product is released into production. To quote from the book:

Common sense would tell you that the design documentation, produced as the product is being built, would be an important basis for those undesign tasks. But common sense, in this case, would be wrong. As the product is built, the as-built program veers more and more away from the original design specifications. Ongoing maintenance drives the specs and product even further apart. The fact of the matter is, design documentation is almost completely untrustworthy when it comes to maintaining a software product. The result is, almost all of that undesign work involves reading of code (which is invariably up to date) and ignoring the documentation (which commonly is not).

 So, one of the main reasons maintenance work is hard is that the programmer has to expend considerable effort in decoding someone else’s code (some might argue that this is the most time consuming part of undesign). Programmers know that it is hard to infer what a program does by reading it, so the word “code” in the previous sentence could well be used in the sense of code as an obfuscated or encrypted message. As Charles Simonyi said in response to an Edge question:

 Programmers using today’s paradigm start from a problem statement, for example that a Boeing 767 requires a pilot, a copilot, and seven cabin crew with various certification requirements for each—and combine this with their knowledge of computer science and software engineering—that is how this rule can be encoded in computer language and turned into an algorithm. This act of combining is the programming process, the result of which is called the source code. Now, programming is well known to be a difficult-to-invert function, perhaps not to cryptography’s standards, but one can joke about the possibility of the airline being able to keep their proprietary scheduling rules secret by publishing the source code for the implementation since no one could figure out what the rules were—or really whether the code had to do with scheduling or spare parts inventory—by studying the source code, it can be that obscure.

  Glass offers up one final maintenance-related fact in his book (Fact 45):

 Better software engineering leads to more maintenance, not less.

 Huh? How’s that possible.

 The answer is actually implicit in the previous facts and Simonyi’s observation: in the absence of documentation, the ease with which modifications can be made is directly related to the ease with which the code can be understood. Well designed systems are easier to understand, and hence can be modified more quickly. So, in a given time interval, a well designed system will have more modifications done to it than one that is not so well designed. Glass mentions that this is an interesting manifestation of Fact 43: Maintenance as a solution, rather than a problem.

Towards the end of the book, Glass presents the following fallacy regarding maintenance:

The way to predict future maintenance costs and to make product replacement decisions is to look at past cost data.

The reason that prediction based on past data  doesn’t work is that a plot of maintenance costs vs. time plot has a bathtub shape. Initially, when a product is just released,   there is considerable maintenance work (error fixing and enhancements)  done on it. This decreases in time, until it plateaus out. This is the “stable” region corresponding to the period when the product is being used with relatively few modifications or error fixes.  Finally, towards the end of the product’s useful life, enhancements and error fixes become more expensive as technology moves on and/or the product begins to push the limits of its design. At this point costs increase again, often quite steeply.  The point Glass makes is that, in general, one does not know where the product is  on this bathtub curve. Hence, using past data to make predictions is fraught with risk – especially if one is near an inflection point, where the shape of the curve is changing.So what’s the solution? Glass suggests asking customer about their expectations regarding the future of the  product, rather than trying to extrapolate from past data.

Finally, Glass has this to say about replacing software:

Most companies find that retiring an existing software product is nearly impossible. To build  a replacement requires a source of the requirements that match the current version of the product, and those requirements probably don’t exist anywhere. They’re not in the documentation because it wasn’t kept up to date. They’re not to be found from the original customers or users or developers because those folks are long gone…They may be discernable form reverse engineering the existing product, but that’s an error-prone and undesirable task that hardly anyone wants to tackle. To paraphrase an old saying, “Old software never dies, it just tends to fade away.”

And it’s the maintenance programmer who extends its life, often way beyond original design and intent. So, maintenance matters because it adds complexity to the  legacy of legacy software. But above all it matters because it is a solution, not a problem.

Written by K

July 16, 2009 at 10:17 pm

The what and whence of issue-based information systems

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Over the last few months I’ve written a number of posts on IBIS (short for Issue Based Information System), an argument visualisation technique invented in the early 1970s by Horst Rittel and Werner Kunz.  IBIS is best known for its use in dialogue mapping – a collaborative approach to tackling wicked problems – but it has a range of other applications as well (capturing project knowledge is a good example).    All my prior posts on IBIS focused on its use in specific applications.   Hence the present piece,  in which I discuss the “what” and “whence”  of IBIS:  its practical aspects – notation, grammar etc. –   along with  its origins, advantages and limitations

I’ll begin with a brief introduction to the technique (in its present form) and then move on to its origins and other aspects.

A brief introduction to IBIS

IBIS  consists of three main elements:

  1. Issues (or questions): these are issues that need to be addressed.
  2. Positions (or ideas): these are responses to questions. Typically the set of ideas that respond to an issue represents the spectrum of perspectives on the issue.
  3. Arguments: these can be Pros (arguments supporting) or Cons (arguments against) an issue. The complete  set of arguments that respond to an idea represents the multiplicity of viewpoints on it.

The best IBIS mapping tool is Compendium – it can be downloaded here.  In Compendium, the IBIS elements described above are represented as nodes as shown in Figure 1: issues are represented by green question nodes; positions by yellow light bulbs; pros by green + signs and cons by red – signs.  Compendium supports a few other node types,  but these are not part of the core IBIS notation. Nodes can be linked only in ways specified by the IBIS grammar as I discuss next.

IBIS Elements

Figure 1: IBIS Elements

The IBIS grammar can can be summarized in a few simple rules:

  1. Issues can be raised anew or can arise from other issues, positions or arguments. In other words, any IBIS element can be questioned.  In Compendium notation:  a question node can connect to any other IBIS node.
  2. Ideas can only respond to questions – i.e.  in Compendium “light bulb” nodes  can only link to question nodes. The arrow pointing from the idea to the question depicts the “responds to” relationship.
  3. Arguments  can only be associated with ideas –  i.e in Compendium + and –  nodes can only link to “light bulb” nodes (with arrows pointing to the latter)

The legal links are summarized in Figure 2 below.

Figure 2: Legal Links in IBIS

Figure 2: Legal Links in IBIS

The rules are best illustrated by example-   follow the links below to see some illustrations of IBIS in action:

  1. See this post for a simple example of dialogue mapping.
  2. See this post or this one for examples of argument visualisation .
  3. See this post for the use IBIS  in capturing project knowledge.

Now that we know how IBIS works and have seen a few examples of it in action, it’s time to trace its history from its origins to the present day.

Wicked origins

A good place to start is where it all started. IBIS was first described in a paper entitled, Issues as elements of Information Systems; written by Horst Rittel (who coined the term “wicked problem”) and Werner Kunz in July 1970. They state the intent behind IBIS in the very first line of the abstract of their paper:

Issue-Based Information Systems (IBIS) are meant to support coordination and planning of political decision processes. IBIS guides the identification, structuring, and settling of issues raised by problem-solving groups, and provides information pertinent to the discourse.

Rittel’s preoccupation was the area of public policy and planning – which is also the context in which he defined wicked problems originally.  He defined the term in his landmark paper of 1973 entitled, Dilemmas in  a General Theory of Planning. A footnote to the paper states that it  is based on an article that he   presented at an AAAS meeting in 1969. So it is clear that he had already formulated his ideas on wickedness when he wrote his paper on IBIS in 1970.

Given the above background it is no surprise that Rittel and Kunz foresaw IBIS to be the:

…type of information system meant to support the work of cooperatives like governmental or administrative agencies or committees, planning groups, etc., that are confronted with a problem complex in order to arrive at a plan for decision…

The problems tackled by such  cooperatives are paradigm-defining examples of wicked problems. From the start, then, IBIS was intended as a tool to facilitate a collaborative approach to solving such problems.

Operation of early systems

When Rittel and Kunz wrote their paper, there were three IBIS-type systems in operation: two in governmental agencies (in the US, one presumes) and one in a university environment (possibly, Berkeley, where Rittel worked). Although it seems quaint and old-fashioned now, it is no surprise that they were all manual, paper-based systems- the effort and expense involved in computerizing such systems in the early 70s would have been prohibitive, and the pay-off questionable.

The paper also offers a short description of how these early IBIS systems operated:

An initially unstructured problem area or topic denotes the task named by a “trigger phrase” (“Urban Renewal in Baltimore,” “The War,” “Tax Reform”). About this topic and its subtopics a discourse develops. Issues are brought up and disputed because different positions (Rittel’s word for ideas or responses) are assumed. Arguments are constructed in defense of or against the different positions until the issue is settled by convincing the opponents or decided by a formal decision procedure. Frequently questions of fact are directed to experts or fed into a documentation system. Answers obtained can be questioned and turned into issues. Through this counterplay of questioning and arguing, the participants form and exert their judgments incessantly, developing more structured pictures of the problem and its solutions. It is not possible to separate “understanding the problem” as a phase from “information” or “solution” since every formulation of the problem is also a statement about a potential solution.

Even today, forty years later, this is an excellent description of how IBIS is used to facilitate a common understanding of complex (or wicked) problems. The paper contains an overview of the structure and operation of manual IBIS-type systems. However, I’ll omit these because they are of little relevance in the present-day world.

As an aside, there’s a  term that’s conspicuous by its absence in the Rittel-Kunz paper: design rationale. Rittel must have been aware of the utility of IBIS in capturing design rationale: he was a professor of design science at Berkley and design reasoning was one of his main interests. So it is somewhat odd that  he does not mention this term  even once  in his IBIS  paper.

Fast forward a couple decades (and more!)

In a paper published in 1988 entitled, gIBIS: A hypertext tool for exploratory policy discussion, Conklin and Begeman describe a prototype of a graphical, hypertext-based  IBIS-type system (called gIBIS) and its use in capturing design rationale (yes, despite the title of the paper, it is more about capturing design rationale than policy discussions). The development of  gIBIS represents a key step between the original Rittel-Kunz version of IBIS and its  present-day version as implemented  in Compendium.  Amongst other things, IBIS was finally off paper and on to disk, opening up a new world of possibilities.

gIBIS aimed to offer users:

  1. The ability to capture design rationale – the options discussed (including the ones rejected) and the discussion around the pros and cons of each.
  2. A platform for promoting computer-mediated collaborative design work  – ideally in situations where participants were located at sites remote from each other.
  3. The ability to store a large amount of information and to be able to navigate through it in an intuitive way.

Before moving on, one point needs to be emphasized: gIBIS was intended to be used in collaborative settings; to help groups achieve a shared understanding of central issues, by mapping out dialogues in real time. In present-day terms – one could say that it was intended as a tool for sense making.

The gIBIS prototype proved successful enough to catalyse the development of Questmap, a commercially available software tool that supported IBIS. However, although there were some notable early successes in the real-time use of IBIS in industry environments (see this paper, for example), these were not accompanied by widespread adoption of the technique. Other graphical, IBIS-like methods to capture design rationale were proposed (an example is Questions, Options and Criteria (QOC) proposed by MacLean et. al. in 1991), but these too met with a general reluctance in adoption.

Making sense through IBIS

The reasons for the lack of traction of IBIS-type techniques in industry are discussed in an excellent paper by Shum et. al. entitled, Hypermedia Support for Argumentation-Based Rationale: 15 Years on from gIBIS and QOC.  The reasons they give are:

  1. For acceptance, any system must offer immediate value to the person who is using it. Quoting from the paper, “No designer can be expected to altruistically enter quality design rationale solely for the possible benefit of a possibly unknown person at an unknown point in the future for an unknown task. There must be immediate value.” Such immediate value is not obvious to novice users of IBIS-type systems.
  2. There is some effort involved in gaining fluency in the use of IBIS-based software tools. It is only after this that users can gain an appreciation of the value of such tools in overcoming the limitations of mapping design arguments on paper, whiteboards etc.

The intellectual effort – or cognitive overhead, as it is called in academese – in using IBIS in real time involves:

  1. Teasing out issues, ideas and arguments from the dialogue.
  2. Classifying points raised into issues, ideas and arguments.
  3. Naming (or describing) the point succinctly.
  4. Relating (or linking) the point to an existing node.

This is a fair bit of work, so it is no surprise that beginners might find it hard to use IBIS to map dialogues. However, once learnt, a skilled practitioner can add value to design (and more generally, sense making) discussions in several ways including:

  1. Keeping the map (and discussion) coherent and focused on pertinent issues.
  2. Ensuring that all participants are engaged in contributing to the map (and hence the discussion).
  3. Facilitating useful maps (and dialogues) – usefulness being measured by the extent to which the objectives of the session are achieved.

See this paper by Selvin and Shum for more on these criteria. Incidentally, these criteria are a qualitative measure of how well a group achieves a shared understanding of the problem under discussion.  Clearly, there is a good deal of effort involved in learning and becoming proficient at using IBIS-type systems, but the payoff is an ability to facilitate  a shared understanding of wicked problems – whether in public planning or in technical design.

Why IBIS is better than conventional modes of documentation

IBIS has several advantages over conventional documentation systems. Rittel and Kunz’s 1970  paper contains a nice summary of the advantages, which I paraphrase below:

  1. IBIS can bridge the gap between discussions and records of discussions (minutes, audio/video transcriptions etc,). IBIS sits between the two, acting as a short term memory. The paper thus foreshadows the use of issue-based systems as an aid to organizational or project memory.
  2. Many elements (issue, ideas or arguments) that come up in a discussion have contextual meanings that are different from any pre-existing definitions. In discussions, contextual meaning is more than formal meaning. IBIS  captures the former in a very clear way – for example a response to a question “What do we mean by X? elicits the meaning of X in the context of the discussion, which is then subsequently captured as an idea (position)”.
  3. Related to the above, the commonality of an issue with other, similar issues might be more important than its precise meaning. To quote from the paper, “…the description of the subject matter in terms of librarians or documentalists (sic) may be less significant than the similarity of an issue with issues dealt with previously and the information used in their treatment…”  With search technologies available, this is less of an issue now. However, search technologies are still limited in terms of finding matches between “similar” items (How is “similar” defined? Ans: it depends on context). A properly structured, context-searchable IBIS-based project archive may still be more useful than a conventional document archive based on a document management system.
  4. The reasoning used in discussions is made transparent, as is the supporting (or opposing) evidence. (see my post on visualizing argumentation for example)
  5. The state of the argument (discussion) at any time can be inferred at a glance (unlike the case in written records). See this post for more on the advantages of visual documentation over prose.

Issues with issue-based information systems

Lest I leave readers with the impression that IBIS is a panacea, I should emphasise that it isn’t. According to Conklin, IBIS maps have the following limitations:

  1. They atomize streams of thought into unnaturally small chunks of information thereby breaking up any smooth rhetorical flow that creates larger, more meaningful chunks of narrative.
  2. They disperse rhetorically connected chunks throughout a large structure.
  3. They are not is not chronological in structure (the chronological sequence is normally factored out);
  4. Contributions are not attributed (who said what is normally factored out).
  5. They do not convey the maturity of the map – one cannot distinguish, from the map alone, whether one map is more “sound” than another.
  6. They do not offer a systematic way to decide if two questions are the same, or how the maps of two related questions relate.

Some of these issues (points 3, 4) can be addressed by annotating nodes;  others are not so easy to solve.

Concluding remarks

My aim in this post has been to introduce readers to the IBIS notation, and also discuss its origins, development and limitations.  On one hand, a knowledge of the origins and development  is valuable because it  gives  insight into the rationale behind the technique, which leads to a better understanding of the different ways in which it can be used. On the other, it is also important to know a technique’s limitations,  if for no other reason than to be aware of these so that one can work around them.

Before signing off, I’d like to mention an observation from my experience with IBIS. The real surprise for me has been that the technique can capture most written arguments and discussions,  despite having only three distinct elements and a very simple grammar. Yes, it does require some thought to do this, particularly when mapping discussions in real time. However,  this cognitive “overhead”  is good because  it forces the mapper to think  about what’s being said  instead of just writing it down blind. Thoughtful transcription is the aim of the game. When done right, this results in a map that truly reflects a  shared understanding of the complex  (and possibly wicked) problem under discussion.

There’s no better coda to this post on IBIS than the following quote from  this paper by Conklin:

…Despite concerns over the years that IBIS is too simple and limited on the one hand or too hard to use on the other, there is a growing international community who are fluent enough in IBIS to facilitate and capture highly contentious debates using dialogue mapping, primarily in corporate and educational environments…

For me that’s reason enough to improve my understanding of IBIS and its applications,  and to look for opportunities to use it in ever more challenging situations.

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