=Paper=
{{Paper
|id=None
|storemode=property
|title=Towards Combining ThinkLets and Dialogue Games in Collaborative Modeling: an Explorative Case
|pdfUrl=https://ceur-ws.org/Vol-777/paper2.pdf
|volume=Vol-777
}}
==Towards Combining ThinkLets and Dialogue Games in Collaborative Modeling: an Explorative Case==
https://ceur-ws.org/Vol-777/paper2.pdf
In: Nolte, A.; Prilla, M.; Lukosch, S.; Kolfschoten, G. and Herrmann, T.: Proceedings of the 1st International Workshop
on Collaborative Usage and Development of Models and Visualizations at the ECSCW 2011 (CollabViz 2011)
Towards Combining ThinkLets and
Dialogue Games in Collaborative
Modeling: an Explorative Case
S.J.B.A. Hoppenbrouwers1 and W. van Stokkum2
1
Radboud University Nijmegen, the Netherlands
and CRP Henri Tudor, Luxemburg
stijnh@cs.ru.nl
2
Everest B.V., the Netherlands
w.van.stokkum@everest.nl
Abstract. We present a next step in our ongoing effort to conceive innovative support
approaches for collaborative modeling. We propose to blend the well-established
Collaboration Engineering approach (rooted in CSCW) with the Dialogue Game approach
(rooted in Conceptual Modeling), viewing the second as a specialized extension of the
first, and describing how they can complement each other. We hope to eventually link not
only the approaches, but also the two fields. We provide a small but realistic illustration of
our proposal at the hand of a real, industrially used elicitation pattern from knowledge
modeling, and briefly show how this pattern can be wrapped up as an ʻm-thinkLetʼ.
Introduction
In many uses of collaborative modeling, e.g. in business engineering (den
Hengst & de Vreede, 2004), knowledge engineering (Hoppenbrouwers, Schotten,
& Lucas, 2010), problem structuring (Vennix, 1996), and enterprise engineering
(Barjis, 2009), collaborative modeling with stakeholders untrained in modeling is
a required and common practice, but also a continuous challenge, referred to as
the ‘knowledge acquisition bottleneck’ (Hoppenbrouwers et al., 2010).
11
In: Nolte, A.; Prilla, M.; Lukosch, S.; Kolfschoten, G. and Herrmann, T.: Proceedings of the 1st International Workshop
on Collaborative Usage and Development of Models and Visualizations at the ECSCW 2011 (CollabViz 2011)
In the field of collaborative modeling (Renger, Kofschoten, & De Vreede,
2008), most work focuses on the collaborative creation and validation of model
diagrams, using some standard modeling language (for example, UML activity
diagrams: (Rittgen, 2007)). A different approach, which this paper is an exponent
of, concerns more focused, ‘smaller’ conceptualizations that help gather and
communicate highly to-the-point, well structured information that can be the basis
for derivation (manually or possibly automatically) of more abstract, ‘technical’
models (Hoppenbrouwers, 2008; Hoppenbrouwers et al., 2010).
Once we move away from the ‘collaborative diagram drawing’ approach and
into more limited and focused conceptualization (closer to the stakeholders’
familiar concepts and requiring less skill in dealing with abstract syntax and
complex visualizations and verbalizations), we can also move towards more
closely guided, wizard-like conceptualization support (Hoppenbrouwers,
Weigand, & Rouwette, 2009). We thus, in the long run, work towards the creation
of a coherent library of well focused ‘modeling games’: rule-based, goal-driven
interactive procedures that do not involve more than a few meta-concepts each
and should be relatively easy to ‘play’ for stakeholders untrained in formal
modeling (Wilmont, Brinkkemper, van de Weerd, & Hoppenbrouwers, 2010).
Such ‘conceptualization games’ bear considerable resemblance to the thinkLet
concept central in Collaboration Engineering or CE (de Vreede & Briggs, 2005;
Kolfschoten, Briggs, de Vreede, Jacobs, & Appelman, 2006), and can in fact be
seen as a specialized extension of that approach. However, as will be explained in
the next section, some additional properties are to be added to thinkLets as they
(also) become Dialogue Games (DGs). The DG approach originated in the field
of conceptual modeling, whereas CE concerns collaboration more in general, yet
in the specific context of collaborative interaction support (in particular, CSCW).
We hope to link not only the approaches, but ultimately also the two fields.
ThinkLets and Dialogue Games
The Dialogue Game (DG) approach to collaborative modeling is rooted in a
theoretical view on modeling as a conversation (Veldhuijzen van Zanten,
Hoppenbrouwers, & Proper, 2004). Detailing this line of thinking led to a
framework in which the core concepts are Rules, Interactions, and Models (RIM):
Rules both drive and constrain conversational Interactions that include
propositions, but also argumentation about those propositions. A set of
propositions as accepted by the modelers at some point in time constitutes a
current Model. For an elaborate explanation of the RIM framework, see
(Ssebuggwawo, Hoppenbrouwers, & Proper, 2009). Interactions include
conversational moves like arguing for or against a proposition, agreeing,
disagreeing, and of course putting forward or withdrawing a proposition.
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In: Nolte, A.; Prilla, M.; Lukosch, S.; Kolfschoten, G. and Herrmann, T.: Proceedings of the 1st International Workshop
on Collaborative Usage and Development of Models and Visualizations at the ECSCW 2011 (CollabViz 2011)
From the rule-based RIM approach, it is a small step to viewing modeling
sessions as enacted games (instantiations of a game type). In addition, there is a
theoretical link between the RIM approach and ‘dialogue games’, a known
concept in Argumentation Theory (Eemeren et al., 1996).
Let us now consider the CE approach (involving thinkLets) and see how this
approach relates to the DG approach to collaborative modeling. Please note that
lack of space prevents us from providing a full scale, detailed comparison
between the CE and DG approaches here; we intend to do this elsewhere,
including identification of overlap between existing thinkLets and (parts of)
Dialogue Games. Indeed we know such overlap exists. However, our strategy is
to first focus on the creation of playable game implementations; analysis and (re)-
use of generic patterns (thinkLets) in these games will have to come later.
The CE concepts we refer to below are based on (Kolfschoten et al., 2006).
Symbolical of the overlap between the two approaches, we refer to ‘m-thinkLets’:
a (still mostly fictional) class of thinkLets for use in collaborative modeling and
compatible with the structure of DGs.
In (Kolfschoten et al., 2006), thinkLets are defined as “named, packaged
facilitation techniques that create predictable, repeatable patterns of collaboration
among people working towards a goal”. In (Hoppenbrouwers et al., 2009),
collaborative modeling is characterized as a “goal-driven interactive activity that
requires freedom of action and decision within clearly set boundaries.” Games are
typically also such activities. A similar direction is suggested in (Kolfschoten et
al., 2006)) by shifting from the use of complete and rather detailed, restrictive
‘scripts’ as part of specifying thinkLets, to defining rules. Though they do not
explicitly refer to ‘games’, from the DG/RIM perspective even classic thinkLets
are games, of a sort.
In dealing with the optimal trade-off between constraint and freedom in
guiding interaction, much can be learned from game dynamics. In addition, taking
the game metaphor seriously suggests some interesting possibilities: the use of
advanced interfacing from gaming to make collaborative interaction more
accessible and engaging; even the use of devices like score systems or local
competition embedded in over-all collaboration (Hoppenbrouwers et al., 2009).
The DG approach recognizes the long term goal (also highly prominent in CE)
of removing the facilitator as much as possible (disintermediation), yet it
currently focuses on simplifying and structuring the facilitator’s role rather than
removing it. A DG for modeling is typically viewed as two entwined games with
distinct sets of goals and rules: one (or more) for the stakeholder-participants, one
for the facilitator-participants. Again this merges the notion of ‘rules’ with the
notion of ‘script’, including the facilitator as a role in the game. Such a setup was
successfully executed in a pilot DG for Group Model Building, transforming a
script into a DG (Hoppenbrouwers & Rouwette, 2011).
13
In: Nolte, A.; Prilla, M.; Lukosch, S.; Kolfschoten, G. and Herrmann, T.: Proceedings of the 1st International Workshop
on Collaborative Usage and Development of Models and Visualizations at the ECSCW 2011 (CollabViz 2011)
In modeling (as opposed to generic collaboration), a key notion is that of a
meta model or modeling language. Though this aspect is in principle covered by
thinkLet design concepts, it could benefit from additional, further specialized
views from the DG approach. The pragmatic focus of a DG (the intended use of
the conceptualization it renders: its desired resulting contents) is driven by focus
questions; its semantic-syntactic focus (the modeling language or conceptual
format of the result) constrains the formulation of focused answers
(Hoppenbrouwers & Wilmont, 2010). Small sets of meta concepts used in
modeling can thus be deliberately introduced in m-thinkLets, aiding their
pragmatic and semantic-syntactic focus.
CE uses the concept of “parameters” of thinkLets: content-specific variables,
for example focus questions. One could view such parametrization as an
important aspect of the development of m-thinkLets. However, the creation of m-
thinkLets would involve the setting of parameters that would still be generic for a
certain flavor of modeling, e.g. ontological modeling, process modeling, and so
on. Indeed, m-thinkLets require a specific, focused approach to the use of
parameters extending into ‘syntax setting’ for m-thinkLet results.
CE covers ‘moves of the game’ that relate to the rendering of results of
thinkLets. Discussion is explicitly included as a possible ‘action’ in thinkLets, but
CE does not guide, constrain, or log its ‘mechanics’. Contrarily, the DG approach
considers the typical interactions of discussion and argumentation as discrete
‘moves of the game’ (Hoppenbrouwers & Rouwette, 2011). Logging all
“discussion moves” and making them accessible both during and after the game is
standard. Possibly, CE in general might benefit from such a mechanism.
Having explored key similarities and differences between CE and the DG
approach, let us consider a realistic example of a potential DG based m-thinkLet.
Example: The ‘Weighted Factor Elicitation Game’
An exemplary ‘m-thinkLet’ interaction pattern was created in context of a project
in which a radical new distributed model was conceived for scheduling Dutch
railway traffic (van Stokkum, 1999). The pattern involved was applied in a one-
and-a-half hour collaborative modeling session with three domain specialists of
Dutch Railways, and a facilitator. A role playing setup was used to elicit the
weighed factors that influence the creation of scheduling conflicts between trains.
The facilitator (a knowledge engineer) initiated the session by introducing a
limited set of scenarios that can lead to a conflict. These scenarios were presented
by schematic diagrams (Fig. 1).
The diamonds in Fig. 1 represent junctions. The other icons represent trains.
The goal of the game is for the players to set parameters such that, for a specific
scenario, there is a given p% chance (e.g. 75%) that the trains will raise a conflict
(i.e. arrive at the same time) at the junction. During the game, the facilitator
14
In: Nolte, A.; Prilla, M.; Lukosch, S.; Kolfschoten, G. and Herrmann, T.: Proceedings of the 1st International Workshop
on Collaborative Usage and Development of Models and Visualizations at the ECSCW 2011 (CollabViz 2011)
actively varies scenario details like the types of trains involved (e.g. length, load)
or events occurring (e.g. wind conditions, engine failure ).
Figure 1. Two of the scenarios in which two trains could be arriving too close together at one
infrastructural railtrack point
For example: “let domain expert 1 be the red train. This red train is a long
cargo train carrying a heavy load. Domain expert 2 is the blue train which is IC
train with high priority. Domain expert 3 is a junction that will assess
continuously the chance of collision. Assignment: for this situation,
collaboratively conceive and set factors so that there is a 75% chance the trains
collide”. The actual, utilitarian goal of the game is to collaboratively define a
stable set of factors influencing the chances of collisions taking place. Factor
types thus elicited included speed, maintenance record, weather influences,
weight, type of engine, priority of passengers and cargo; weights (high/low)
indicated the importance of the factors.
The domain experts involved had no experience in creating formal models.
The described session was one in a series of nine interrelated sessions, each of a
similar focused nature. In each session the focus (both pragmatic and semantic-
syntactic) was set differently to address a specific aspect: the train, the
infrastructural points, creating conflict, creating a plan to prevent a conflict,
determining a cost function to evaluate a plan, decision making on plans,
determining follow-up conflicts, define a stop criterion for evaluating uncertain
follow-up conflicts. By breaking up the problem into small, focused sessions, in
the end a very complex distributed scheduling system was collaboratively
modeled, without any ‘comprehensive diagram drawing’ (in fact, such a diagram
would have too complex to draw in the first place: it was represented as a set of
mathematical formulae).
The same patterns have later been reapplied in other projects in need of a real
time distributed workflow scheduling solution. For example, the patterns have
been used to develop a system for scheduling ground operations at Zaventem
airport, scheduling autonomous operating robots in Rotterdam’s largest container
handling terminal port, creating simulations to solve traffic jam problems in
Holland and for developing an order picking system for distribution centers of a
Dutch super market chain.
The technique presented above is an excellent example of a ‘Focused
Conceptualization’ or ‘FoCon’ as introduced in (Hoppenbrouwers & Wilmont,
2010). Specifications of FoCons are somewhat similar to conceptual designs for
thinkLets, but they were developed strictly in context of collaborative modeling.
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In: Nolte, A.; Prilla, M.; Lukosch, S.; Kolfschoten, G. and Herrmann, T.: Proceedings of the 1st International Workshop
on Collaborative Usage and Development of Models and Visualizations at the ECSCW 2011 (CollabViz 2011)
FoCon analysis as an instrument concerns questions like: “What goes into a
FoCon situation, in terms of existing information and people (including their
concerns, knowledge, and skills)”; “What is the intended output of a FoCon
situation, in terms of pragmatic goals, conceptual (semantic-syntactic) constraints
set, and the required level and sort of agreement between people”, “what focus
questions are used, and what explicit instructions are to be given by the facilitator,
in which situation”, and “what rules govern the required or limited interaction
between players, in view of a current focus question”. Clearly, a similar analysis
could be applicable in a thinkLet context. The main points of a FoCon analysis of
the m-thinklet described above are given in Table I below:
“IN” Info Various given scenarios and given chances of collision
Concepts Trains, junctions, situations (diagrams); properties of trains,
partly based on results of ongoing elicitation; given chance of
collision (P-value, e.g. 0.75)
People Train traffic management experts, not trained in formal
modeling, some system thinking ability, homogeneous
professional background
“OUT” Info List, generalized over all scenarios used, of weighted factors
(pragm. focus) influencing collision risk
Concepts Factor types, weight for each factor type (high/low impact)
(sem.-synt. focus)
Social req. Factors commonly understood and agreed upon
Argumentation Arguments raised and accepted/rejected in discussing the
factors and their weights
Substeps/ Facilitator: iteratively set scenario, then discuss factors, then
Strategy change details of scenario or set new scenario, thus
systematically exploring all factors and developing a generic
overview; Players: assume role of train or junction; for a series
of scenarios, provide weighted factors matching a given
chance of collision
Interaction • Focus on shared understanding of scenario
Modes in • Focus on identifying relevant factors
the game • Focus on determining the weight of a factor
Table I. Overview of the main points of a FoCon analysis and DG outline of the example
We hope the table sufficiently illustrates how a FoCon analysis can serve as a
basis for designing both Dialogue Games and m-thinkLets. Note that in the
example, ‘argumentation’ plays a role in the actual elicitation process (arguments
can be looked up during a running game and are a source of ideas about factors
for the players) but argumentation is also logged for future reference to details in
the discussion (otherwise lost). Structure is inherently provided by the DG setup.
We leave out considerations of mappings between m-thinklets, aptly called
“transitions” (Kolfschoten et al., 2006), except by stating that such transitions can
be direct mappings of resulting concepts to models or model views, but also
derivations (typically by means of logical reasoning) based on concepts found
and possibly leading to further abstraction thereof (Hoppenbrouwers et al., 2010).
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In: Nolte, A.; Prilla, M.; Lukosch, S.; Kolfschoten, G. and Herrmann, T.: Proceedings of the 1st International Workshop
on Collaborative Usage and Development of Models and Visualizations at the ECSCW 2011 (CollabViz 2011)
Acknowledgments
This paper results from the Agile Service Development project (http://www.novay.nl/okb/projects/agile-
service-development/7628), a collaborative research initiative focused on methods, techniques and tools for
the agile development of business services. The project consortium consists of BeInformed, BiZZdesign,
CRP Henri Tudor, Everest, HU University of Applied Sciences Utrecht, IBM, Novay, O&i, PGGM,
RuleManagement Group, Radboud University Nijmegen, Twente University, Utrecht University, and Voogd
& Voogd. The project is part of the program Service Innovation & ICT of the Dutch Ministry of Economic
Affairs.
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