The
Technical
Cooperation
Program
Australia - Canada - New Zealand - United Kingdom - United States of America
TTCP TECHNICAL REPORT
JSA Action Group 14 Complex Adaptive Systems for Defence
Progress Report
Causal & Influence Networks
in
Complex Systems
May 2010
DOC – AG14 - C&IN #4-2010
Correspondence: Anne-Marie.Grisogono@defence.gov.au
This report contains Information which is provided in confidence to the Governments of Australia, Canada,
New Zealand, the United Kingdom and the United States of America under the auspices of The Technical
Cooperation Program (TTCP). The Information contained herein may be used and disseminated for national
Defense Purposes within the recipient Governments and their national defense Contractors. The recipient
Governments will ensure that any other use or disclosure of the Information is made only with the prior
written consent of each of the above Governments.
CONTENTS
Introduction ................................................................................................................. 3
Causation in Complex Systems ................................................................................ 6
Causal and Influence Networks .............................................................................. 6
Challenges to Understanding C&INs ..................................................................... 8
Decision Support in Complex Situations ............................................................. 11
Existing C&IN Approaches – capabilities and limitations ................................ 15
Bayesian techniques ................................................................................... 15
System Dynamics ....................................................................................... 15
Network theory .......................................................................................... 16
Simulation ................................................................................................... 17
Non-Linear Dynamical Systems .............................................................. 17
Strategy ....................................................................................................................... 18
State-of-the-Art review ........................................................................................... 18
Novel CAS Approaches .......................................................................................... 20
Promising CAS Concepts ....................................................................................... 20
Multi-scale Intent ....................................................................................... 21
Attractors ..................................................................................................... 22
What is known about interacting adaptive processes? ........................ 24
Implementation: The C&IN Program ................................................................... 25
Scoping Paper and Open Questions ..................................................................... 25
Workshop Series ...................................................................................................... 26
Deliverables and Forward Workplan ................................................................... 28
References................................................................................................................... 31
Annex A: Essential and Desirable Requirements for a C&IN Support Tool
for Complex Operations .......................................................................................... 34
Annex B: International Collaborative Research Program ................................. 38
PAGE 2 OF 54
Introduction
Context - and why this is a progress report rather than a final report.
From its inception, AG14 recognised two fundamental research challenges at the
root of most complexity problems:

how structure arises and develops, and

how causation works in complex systems.
Research thrusts were established in both, as well as in several application areas,
as described in the Final Report [1] which this document accompanies. The
outcomes of the first thrust, a Conceptual Framework for Adaptation, presented
in an accompanying Synthesis Report [2], have been applied extensively in much
of AG14’s work.
The second thrust, addressed here, turned out to be much more challenging, not
surprisingly, since it is in a sense the problem of complexity, and subsumes the
issues surrounding how structure arises and develops. What we have learned
within the first thrust is therefore relevant to and helpful for addressing the
second, as will be discussed below.
In initial discussions with complex systems theorists and practitioners (eg at the
Santa Fe Institute, within CSIRO, and at various universities) about causation in
complex systems, a frequently encountered response was that the scope of what
we were seeking to address was far too sweeping and grandiose, and we were
encouraged to focus our attention on a well-defined problem that could be
worked on in more detail.
But AG14 was already doing this in its application thrusts, as well as individual
members doing so in the normal course of their defence work, and the challenges
of understanding complex causation were recurring themes which limited the
extent to which each selected problem could be satisfactorily addressed.
So we persisted, against well-intentioned and well-informed advice, but
recognising the magnitude of the task, and that AG14 would therefore need to
motivate support and cooperation from many quarters in order to make any
headway. As a logical step to doing that, a short discussion paper was produced
and circulated, but with little positive response anticipated, given earlier
experiences.
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However it clearly struck a chord in many places, because in fact, it generated
unexpectedly enthusiastic support, endorsement of objectives, offers of
cooperation and even funding.
In this way, an international cooperative effort to tackle causation in complex
systems was launched. A core organising group was developed, mechanisms for
handling the funding from several sources were created, and a program of
international engagement and work started to grow. Details, including the
original discussion paper, the organising group, and other materials are
provided in Annex B.
While this project was initiated by AG14, it has now transcended AG14’s bounds
both with respect to participation beyond the five nations of TTCP, and with
respect to scope beyond the domain of purely defence-related research.
However, AG14 strongly feels that this is a necessary path to take for the very
reasons of the magnitude and generality of the issues.
Although AG14 has now reached the end of its 3 year mandate, the program
described here is still gathering momentum and it is too early to attempt a
synthesis of what has been learned. Accordingly, the present document is better
described as a Progress Report.
We expect that the program will continue and produce its intended deliverables
well beyond the lifecycle of AG14. However there is a valuable opportunity here
for a successor TTCP entity to continue the strong shaping, motivating and
contributing role that AG14 has played, and to steer its course so as to ensure
defence-relevant outcomes.
Report Overview
The structure of this progress report falls into three sections.
The first section, after this context-setting introduction, discusses the challenge of
causation in complex systems, and introduces the notion of networked causality,
and the difficulties that poses. Given these problems, a discussion of the ways in
which limited models can be used for decision support in complex situations
follows. The capabilities and limitations of existing approaches are then briefly
reviewed, including reference to one particular approach based on dynamic
networks, which is discussed in more detail in a supplementary report.
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In summary, having identified problems and needs, this section suggests that
although high fidelity detailed predictive models of complex situations are
unlikely to be achievable, useful decision support is still possible in such
situations in the context of an appropriately adaptive decision methodology,
with more modest, and therefore more achievable, but not yet fully achieved,
goals for the representation and analysis of complex situations.
The second section proposes a two-pronged strategy for addressing those goals,
of generalising from existing domain-specific advances gleaned from a state-ofthe-art review, and generating new approaches from a CAS science perspective.
A way forward for the state-of-the-art approach is discussed, and the section
concludes with an exploratory discussion of some promising CAS ideas, and of
some consequent research questions.
The final section addresses implementation of this program. A brief account is
given of workshops held and insights generated. The section concludes with a
discussion of the final deliverables aimed for, and how they are to be achieved.
Detailed program documents including a list of open questions, and an invitation
to participate are provided in Annex B.
PAGE 5 OF 54
“The complexities of cause and effect defy analysis”
Douglas Adams: Dirk Gently’s Holistic Detective Agency
Causation in Complex Systems
Influencing complex systems towards desired outcomes and away from
undesired ones requires some understanding of how they develop in time, and
what the consequences of events may be, given the system’s properties, current
state and history. Traditionally, one looks for causal relationships between events
and their consequences, but how does causality operate in complex
interconnected systems?
In fact, the concept of causality has a long history in philosophy and science and
remains controversial to this day even in quite simple scenarios, let alone in
complex ones.
However the agenda here is to take a very pragmatic approach to causality as the
production and propagation of effects, with the ultimate aim of supporting
effective interventions in real world complex systems.
Therefore we have not sought to delve into the formal or epistemological nature
of causality for its own sake, but to nevertheless remain open to the possibility
that some insights from such deliberations may illuminate the difficult problem
of analysing possible future trajectories of a complex system and the
consequences of various actions or events in order to support better decisions
about possible interventions.
We begin by examining the sources of difficulties.
Causal and Influence Networks
What makes systems complex is the network of interdependencies between the
elements of the system. This means that consequences of any event or property
develop through many interacting pathways, and similarly, that if we want to
know how a particular property or event came about, we will find that there
were many cross-linked pathways that contributed to it. In such a system
therefore, we cannot expect simple causality (one cause – one effect), or linear
causal chains (each effect inexorably causing the next, like a Rube Goldberg
machine), to hold in general.
PAGE 6 OF 54
Yet models based on simple causality and linear causal chains are attractive
because they seem tractable, unlike the confusing complexity of the real system.
It is possible to make a complex system appear simpler by restricting the scope of
attention to a particular pathway, but if the scope is widened to include other
pathways, or if unexpected side-effects that have propagated through those
pathways are linked back and suddenly manifested within the restricted scope,
we are quickly reminded that the causal chain was just one of many pathways
through a network.
Such systems are therefore inherently characterised not by linear causal chains,
but by networks of causal relationships through which consequences propagate
and interact. Such networksi of interactions between contributing factors can
exhibit emergent behaviours which are not readily attributable or
comprehensible.
The simple observation that complex systems are characterised by networks of
causal relationships has some significant consequences for our ability to
understand and influence them:

Properties of complex systems are the product of many causal pathways,
which may themselves be cross-linked – thus influencing each other;

Consequences of events or properties of complex systems may also
develop through many causal pathways which may also be cross-linked
and influence each other;

Causal and influence relationships in a network can close into feedback
loops that will amplify or suppress certain types of outcomes, and which
can also interact with each other;

Multiple interacting pathways exist in general between an event or
property of a complex system at one time, and a property of interest of the
system at some later time.
i
For example, the firing of a particular neuron in a brain is influenced by the input signals received from
many other neurons, and its own firing in turn influences the probability of firing for many further
downstream neurons, including some of those that provide its own inputs. Looking at the entire network of
interconnected neurons we see firing patterns which are difficult to disentangle yet seem associated with
complex higher-level functions.
PAGE 7 OF 54
We are using the provisional term Causal and Influence Network (C&IN) to
describe such phenomena, but other terms have also been proposed: emergent
causality, complex causality, networked causality and decentralised causality.
Challenges to Understanding C&INs
It is not just the fact that causality is networked that makes C&INs a challenge to
understand. Another fundamental property of real complex systems is that they
exist at multiple scales, raising questions about how causal interactions at one
scale affect or are manifested in behaviour and properties at other scales.
This includes both emergence, the appearance of complex structure and
dynamics at larger scales as a result of smaller-scale phenomena, and the
converse, top-down causation, whereby events or properties at a larger scale can
alter what is happening at the smaller scales.
Traditional scientific reductionism partially addresses emergence by explanatory
decomposition of complex phenomena to smaller scales, but for many reasonsii
cannot predict what the emergent properties will be, given the known properties
at the smaller scales.
Top-down causation would seem essential to our ultimate objective of more
effective intervention and influence of complex systems, but is in fact
philosophically contentious, with some even denying its existence.
Furthermore, there is no unique perspective which provides a complete view of a
complex system, there is no unique way of determining its boundary, and
wherever the boundary is drawn, interactions between the system and its
environment are important. And in real complex systems, many properties and
events will be inaccessible to observation, so one has to expect to always be
working with uncertainty and incomplete information.
All these factors add to the difficulty of studying the C&INs operating in a given
complex system or situation.
ii
Reasons include the unpredictability of external and contingent factors that may influence the emergent
outcomes, non-linearities that result in extreme sensitivity to initial conditions (therefore creating large
uncertainties in computer predictions), incomplete knowledge of the C&IN, and in many cases, practical
uncomputability in reasonable time.
PAGE 8 OF 54
By contrast, much of our cultural conditioning is predicated on a naïve view of
linear causal chains. Examples include: finding ‘the cause’ of an effect, or ‘the
person’ to be held responsible for something, or ‘the cure’ for a problem etc. This
focus on singular or primary causes makes it more difficult to intervene
effectively in complex systems and produce desired outcomes without attendant
undesired ones (so-called ‘side-effects’ or unintended consequences).
Yet effective ways of interacting with particular complex systems have been
developed in many domains, such as medicine, personal relationships, politics,
business, genomics, agriculture, etc. While these domains are certainly not free of
unintended consequences, neither are people paralysed into inaction by the
bewildering difficulty of understanding the complex systems they are interacting
with.
On the other hand, our limited ability to understand and purposefully influence
situations and systems that have large scale complexity hampers our
effectiveness in dealing with them – witness for example the attempts to contain
the recent cascading financial crises, ongoing problems with terrorism and
insurgencies, difficulty in motivating concerted international action on climate
change, world poverty, and many other serious global challenges.
Similarly, in the defence sector, the problems of creating, transforming,
integrating, managing and employing our extensively networked forces also
stem from their underlying complex C&IN structure.
Our hypothesis is that there is a need not only to challenge inappropriately linear
causal thinking in complex systems, but importantly, to search for and develop
where necessary, better ways of thinking about causation and influence in
complex systems, in order to help achieve more effective action in these problem
situations.
The many issues that need to be confronted in attempting to rise to this challenge
can be broken down into four key areas:
1. Methodology for engagement with complex C&INs:

categorisation of causal and influence networks and of our relationships
with them – i.e. understanding what kind of problem we are dealing with,
and hence ...

understanding what is and is not possible to do, and hence …

developing appropriate meta-strategies for engagement.
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2. Learning about the C&IN:

choosing system boundaries,

finding sources of information, choosing what to observe, and the
resolution of observations,

use of probing actions,

developing and testing conjectures about the C&IN,

determining what can be inferred and predicted,

identifying critical uncertainties,

identifying and learning about important interactions through the system
boundaries .
3. Modelling the C&IN:

representing information and conjectures about the C&IN,

dealing with
information,

dealing with multiple perspectives at multiple scales, and in particular,
the fact that the overall C&IN in a complex situation often consists of
many coupled C&INs which are also multi-scale and diverse (eg economic
aspects, social aspects, infrastructure aspects etc)

finding useful levels of abstraction,

working with heterogeneous approaches,

representing the C&IN’s environment (which is also a C&IN in its own
right),

exploiting and interrogating the model, and identifying what further
information is needed,

developing and testing techniques for reasoning about and exploring the
possible behaviour and properties of a C&IN,
negative,
uncertain,
contradictory
and
incomplete
4. Interacting with and Influencing the C&IN:

developing and evolving measures of success and failure for intervening
in the system,

developing and assessing options for stratagems to influence the system,

developing proxies for success and failure associated with the stratagems,
PAGE 10 OF 54

implementing and evolving a stratagem,

determining what needs to be monitored to support continuing evolution
of the stratagem and of the underpinning conjectures.
In each of these main areas and their subsidiary issues, advances are needed at
the theoretical and conceptual levels, as well as practical tools and techniques to
aid practitioners.
The goal of this project is to make some progress towards assembling a
generalised set of approaches, concepts and tools, and to stimulate further
research and development to continue improving the toolset available for
supporting complex system practitioners.
Decision Support in Complex Situations
To discuss the practical feasibility and potential utility of tools for supporting
decisions in complex situations, it is necessary to first make a distinction between
two classes of models, which we might in this context call system models and
situation models.
System models (SMs) represent well-defined systemsiii, with clear input and
output parameters, based on physical models of the processes that transform
inputs to outputs. A user can therefore run the model with certain input
parameters and 'discover' the resulting output parameters, which could in
principle be compared to real world outcomes and hence are falsifiable, provided
the input parameters used were an adequate representation of the real world
system and relevant aspects of its environment.
On the other hand, a complex situation requiring decisions to be made, will
comprise many interacting systems and be characterised by C&INs. A situation
model to provide a complex decision support environment (CxDSE) must
therefore contain many SMs and the interactions between them, as well as their
interactions through the boundaries of the C&IN, since we know that such
situations can never be treated as closed. In other words, a CxDSE is itself a
virtual complex system.
iii
If the system in question is also a complex system, and if it is further characterised by a C&IN in its own
right, then the boundaries become blurred and the complexities compounded, so let us reserve the term
system model in this context for those cases where the description given here is adequate
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Designing and implementing a CxDSE is therefore partly an exercise in learning
about and modelling a C&IN as discussed above, and a far more difficult
challengeiv than developing the component SMs.
The issues involved include all those raised in the preceding section methodology (deciding how to create the CxDS/E), framing (what SMs should be
included? How should they be represented? What interactions should be
included, both between SMs and with entities outside of the frame?), and so on,
plus the overall integration issue – how to put the CxDSE together in a way that
achieves fitness for purpose.
Since the complexity of this task mirrors to some extent the complexity of the
decision situation, and since system-of-systems integration challenges have
repeatedly defeated us at much lesser levels of complexity, the difficulty of the
integration issue should not be underestimated.
However, study of the evolution of biological complexity imply that such
integration challenges can be handled by iterative adaptive processes where the
'fitness' of the emerging complex system is continually tested and the feedback
used to guide incremental integration and build.
So if this is an effective methodology for developing the CxDSE, then it also
informs what is required of the component SMs to achieve overall fitness for
purpose.
It is in the context of such an approach that the uses and utility of models can
now be discussed. A falsifiable SM that is developed to closely match the real
world system it represents can be used with high confidence in both the
qualitative and quantitative aspects of its outputs. However the more SMs are
interconnected to represent complex situations, the more rapidly uncertainties
accumulate and multiply.
Moreover in reality, many of the component systems of the overall situation will
themselves be complex systems which need to be treated as C&INs in their own
right. So a recursive approach also needs to be applied of using SMs to develop
models of C&INs and then treating them as component systems to be integrated
into situation models of the bigger context in which they are embedded, and so
iv
There is a deep-seated parallel here between the difficulty of complex systems engineering (building an
integrated or federated system-of-systems from many diverse component systems), and that of building an
integrated or federated CxDSE of C&INs from many diverse SMs. Similar approaches are called for and
similar problems should be expected.
PAGE 12 OF 54
on. The complexities and uncertainties are compounded at each step, so a
CxDSEv for a real situation will almost inevitably provide low (on the
quantitative aspects) to mid (on the qualitative aspects) confidence at best, even
with high confidence in all component SMs, and even lower confidence if
components systems are complex rather than SMs.
So there are two related issues here: how to develop CxDSEs to be as good as
they can be, and how to make the best possible use of the limited CxDSEs that
can be built. Answers to the second question have consequences for what counts
as fitness for CxDSEs, which in turn has consequences for the fitness required of
the component systems and of their integration.
Figure 1 illustrates how a range of models may be used. A high confidence
model can be used with low risk to support big costly (to implement and to
reverse) decisions, provided one had high confidence that the model was
correctly framed for the decision.
Revisit model for option
generation  better options
Invest in reducing sources
of critical uncertainty
low
Confidence
entertainment
mid
Types of
outcomes
high
Identify possible +/‘side-effects’
& long-term effects
Invest in
ability to look
out for them
Exploit /nurture or
Suppress /mitigate
identifying
where further
investigation
is needed
Narrow range
by getting
feedback as
you go
Many
incremental
reversible &
modifiable
Low risk
support to big
costly
decisions
Ranges of
outcomes
Decisions
All subject to C/R/B
big costly
(to implement & to
reverse)
Figure 1 Uses of Models in Decision Support. See text for details.
v
Such an environment could in fact support many different roles or objectives: educating users about the
possibilities in the complex situation, exploring the intrinsic dynamics of the modelled complex situation,
providing a simulated environment to support experimentation with, learning about, and practice in,
metacognitive skills, methodology, strategy or tactics for dealing with real world situations with broadly
similar characteristics, event reconstruction eg for accidents, or most challengingly, in fact supporting real
decisions in the real world.
PAGE 13 OF 54
At the other end of the scale, a low confidence model cannot be used for decision
support, but could be used for other purposes such as entertainment, or to learn
about the inner dynamics of the model itself (without extrapolating to
conclusions about the real world).
Many models however could fall in the middle range of confidence where one
would be taking too much risk to base big costly decisions on their quantitative
outputs. However they might offer sufficient confidence in their qualitative
outputs i.e. their indication of the kinds of outcomes that could result from a
decision, and the possible ranges of values of output measures, to still have some
utility in decision support.
Specifically, if a big costly decision is unavoidable, then such results at least
highlight where further investigation may be required to reduce risk.
If however a more adaptive stance can be taken by shifting the decision strategy
away from big costly decisions to many incremental and modifiable decisions
(subject of course to a rigorous Risk/Cost/Benefit analysis) then the model results
can be used iteratively to narrow the range of uncertainty and to identify the
possible side-effects and longer-term effects that need to be monitored for and
that can be adaptively managed if and when they are observed.
This is important because as we have argued, high confidence models of complex
situations and C&INs are unlikely to be feasible.
Furthermore, to the extent that the best that can be done really fails to meet
minimal requirements for decision support, this can also stimulate further
research to reduce some of the sources of uncertainty in the CxDSE. Another
option, is to go back to the CxDSE and ask whether it can help generate alternate
decision options that have lower associated risk and adequate benefits.
Finally, and not shown in the diagram, an incremental and adaptive approach to
influencing the complex situation can (indeed must) also be used to adaptively
refine the CxDSE itself through comparison of actual outcomes with those
predicted by the CxDSE on every incremental iteration.
These different uses of models imply very different ways in which their fitness
for purpose should be evaluated, and therefore different objectives one would
strive for in their development.
In summary, it is possible to conceive of CxDSEs which include a variety of SMs
interacting in C&INs, and which can provide at best very limited confidence in
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the real world accuracy of the consequences they produce for various decision
scenarios, yet which can be of great utility in supporting an adaptive methodology for
understanding and influencing the complex situation.
Existing C&IN Approaches – capabilities and limitations
A number of well-known approaches are obvious candidates for dealing with
C&INs. While they all offer some useful capabilities, they also have significant
limitations with respect to the challenges discussed above.
Bayesian techniques
Bayesian [3] approaches can represent the combination of many feed-forward
causal pathways, and can be quantified, provided subjective assessments of all
the relevant priors can be made. In principle at least, such an approach can be
placed in a real-time learning loop and the observed versus predicted posteriors
can inform adaptive updating of the priors. However the requirement for an
acyclic graph structure is restrictive since this means that feedback and more
complex closed loop structures cannot be incorporated within the Bayesian
model itself. This reflects the fact that the technique is really aimed at supporting
inferences from uncertain data, rather than making forward projections about
complex temporal dynamics.
A detailed review [4] of commercial and government-off-the-shelf Bayesian and
similar decision support tools was performed against a set of essential and
desirable performance measures (listed in Annex A) for dealing with complex
military operations. None was found that met all the essential criteria.
Nevertheless this class of tools does offer valuable capability for interpreting
what is known about a situation and highlighting inconsistencies between beliefs
and the data. Applications could be developed that facilitate its real-time
adaptation as the situation evolves. However it is not suitable for exploring
complex temporal dynamics or for doing what-if analyses in C&INs containing
adaptive agents.
System Dynamics
System dynamics [5], originally developed in the 1950s, is a widely used
approach, amenable to quantification and simulation, and able to provide good
visualistion of the dynamics of flows, their accumulation into stocks, with
feedbacks and time delays, in C&INs. It can be used to explore different regimes
of of system behaviour, provided one has accurate enough information about the
PAGE 15 OF 54
relationships in the system, but cannot incorporate innovative adaptive processes
which change the structure of the C&IN.
Another drawback is that despite the ability to execute System Dynamics models
to discover the outcomes for a given set of initial conditions, all this really does is
trace one possible short path out of a vast possibility space. There are no
systematic tools that enable analysis of the possibility space.
Some modified versions of System Dynamics have been used to aid
comprehension of complex operations with some success, such as TNO’s Marvel
[6]
Network theory
A note of disambiguation is needed first. Although the word ‘network’ appears
both in network theory and in AG14’s concept of C&INs, there is an important
difference.
In a visualisation of a C&IN the ‘nodes’ do not represent objects but properties or
events which generate consequences on other properties or events, represented
by the ‘links’ to other nodes. There is therefore an explicit temporal dimension
along each path in a C&IN. What is networked is not entities, but the flow of
causation and influence over time.
Network theory [7] on the other hand, is the study of graphs as the
representation of asymmetric relations between discrete objects. Many network
analysis tools operate on time-integrated rather than temporally resolved
network data (apart from looking at how different network growth rules result in
different topologies) and they do not address emergent non-topological
properties, such as innovations by adaptive agents leading to changes of
structure and behaviours.
Network tools have become very popular over the last decade since the
discovery of small world and scale free networks, but they generally focus
primarily on system properties arising from the topology of the network, rather
than the functionality of nodes. Network theory is therefore well suited to
addressing such aspects as social networks, communication patterns, traffic
flows, propagation effects and network optimisation problems, but has little to
say about how information is processed and used to drive purposeful change.
One approach that does offer a richer and more dynamical view of networks is
Dynamic Network Analysis [8], and a supplementary AG14 report [9] on
application of this approach to defence problems accompanies this report.
PAGE 16 OF 54
Simulation
In practice, because of the lack of good analytical models of real complex
systems, practitioners have long resorted to simulation, in which the complexity
of the system or situation of interest is re-created dynamically, albeit in limited
and approximate ways, rather than attempting to build it into a model upfront.
Some of the approaches mentioned above lend themselves to simulation (such as
Systems Dynamics and some network tools), however one pre-eminently CASrelevant simulation approach is the use of Multi Agent Simulation, or Agent
Based Simulation, [10] applying the principles of autonomy, local views and
interactions, and decentralisation.
These approaches offer the benefit of resolving spatio-temporal patterns, but at
the expense of stripping agents down to very basic features. For example, there is
generally no multi-scale structure in an Agent Based Simulation, and their range
of behaviours are very constrained. Nevertheless complex emergent behaviours
such as self-organisation, which do not depend on detailed properties of the
agents, can be produced.
Some very large scale Agent Based Simulations are currently being developed to
study the dynamics of real world C&INs, and they may well produce valuable
insights if a very careful methodological approach is taken. However one
drawback suffered by all simulations is that they essentially represent single
trajectories through the phase space of the system rather than any systemic
analysis of the phase space. A derivative of this approach, Agent Based
Distillations [11] in which supercomputers are used to generate
multidimensional data arrays about many parallel trajectories, were pioneered
by the US Marine Corps Combat Development Command in the late 90s and
early 2000’s, but currently still fall far short in many respects of addressing the
C&IN challenges identified here.
Some of the best examples of effective use of simulations to deal with real world
C&INs are those developed for the real time adaptive management of epidemics,
such as that developed at Argonne National Laboratory [12].
Non-Linear Dynamical Systems
The theory of NLDS, [13] based on the notion of time evolution of a dynamical
system through its phase space via a deterministic or stochastic rule, generates
very powerful mathematical tools permitting analysis of the system’s trajectories,
and recognition of attractors, bifurcations and the onset of chaos. However it
PAGE 17 OF 54
cannot really be used for systems such as most real world C&INs lacking a
mathematical formulation.
In spite of not being able to exploit the rich mathematics of NLDS many of the
concepts such as state space, phase space, attractors, trajectories and chaos, can
be usefully applied in a loosely analogous way to real complex systems that
cannot be mathematically represented. Some of these will be discussed in the
section exploring CAS concepts.
Discussion
It is evident that no one of these approaches is able to address all the challenges
discussed above, and some of the challenges are not addressed by any of them
[4]. Admittedly, and as discussed above, there are nevertheless useful roles for
most of them within the context of an appropriate CAS based methodology.
However a critical gap remains in relation to innovative adaptation, complex
temporal dynamics, and complex multi-scale emergent phenomena, that is
unlikely to be filled by any combination of these approaches.
New approaches are needed to understanding and working with C&INs, that are
grounded in the science of complex systems and that can bridge the gap to access
the many strengths of established approaches and augment them with these
essential features of complex adaptive systems.
Strategy
There are two potential avenues for discovering better ways to understand and
work with C&INs:
I. State-of-the-Art review to leverage domain-specific advances already
made in particular disciplines, and
II. Novel CAS approaches – i.e. push the boundaries of the state of the art by
exploring relevant ideas in CAS science that could lead to innovative
advances.
State-of-the-Art review
This involves selecting a number of disciplines for review, and documenting how
their practitioners deal with the challenges of networked causality in their
systems of interest. To render their insights more broadly applicable they need to
be documented in a generalised domain-independent way.
PAGE 18 OF 54
Furthermore, it is also necessary to understand what it is about their systems of
interest that make their specific approaches and techniques feasible and useful,
so that the transferability of their approaches to other systems can be assessed.
The implies a four-step methodology:
A. Explore and review the state of the art of theoretical concepts and approaches,
and practical tools and techniques (referred to as methods for brevity in what
follows) in use across a number of disciplines that deal with complex systems.
In particular, document how they:

discover and model the C&INs operating in their systems of interest;

explain how observed properties and behaviour have come about;

think and reason about the possible consequences of observed or
hypothetical states or processes;

estimate the probablilities of various outcomes, and

explore how hypothetical properties or events might come about.
B. Generalise the identified methods, determine under what conditions they are
valid, and hence the characteristics required of a C&IN for those methods to
be applicable to them.
C. Hence develop a classification of both C&INs and the generalised methods,
according to those characteristics, so that a mapping can be made between
them, to identify which methods are suitable for which systems.
D. Discuss how to instantiate the generalised methods into applicable domains,
and discuss how they enable the user to:

define or recognise systemic (i.e. networked) causality in contrast to
traditional notions of direct causality;

assess the kinds of causal and influence relationships that can be
identified; and

capture, represent and investigate the C&INs operating in it.
In order to help focus effort on areas of either or both greatest need and greatest
potential for immediate benefits, we add the following two steps to the strategy:
E. Develop a prioritised list of open questions in relation to C&INs in the
disciplines consulted, and hence develop lists of both fundamental C&IN
research questions, and domain-specific high pay-off C&IN questions.
PAGE 19 OF 54
F. Look for cross-disciplinary opportunities to address any of the specific
questions with what has been learned from steps A-D.
Since the number of disciplines that could potentially be reviewed is very largevi,
some selection criteria are needed to determine which ones in particular will be
explored. The criteria identified and agreed are:

Availability of SME’s in the discipline that can be consulted for the review,
including both established practitioners and recent graduates;

An assesment of how much there may be to learn from the field

Whether the field offers coverage of generic problems, application issues
or spatio-temporal scales not addressed by the other fields selected.
Novel CAS Approaches
This avenue of the overall strategy needs to be informed by the following
considerations:

Greatest needs: Appreciation of the priority fundamental C&IN research
questions and high pay-off problems to be addressed;

Current limitations of existing methods; and

Promising CAS concepts that might be developed into useful innovations.
The results of Step E above will provide the first of these, the areas of greatest
need.
The second consideration was discussed in the preceding section, and the third is
now developed in more detail in the following.
Promising CAS Concepts
There are many complex situations where experts have developed effective ways
of perceiving the essential aspects of those situations so that they can rapidly
make good decisions about them. So an important question to ask is in what
ways are experts who successfully deal with particular types of C&INs different
from novices who struggle with them?
vi
Examples include Neuroscience, Medicine, Genomics, Defence, National Security, Government Policy,
Ecosystems, Economy, Finance, Diplomacy, International Relations, Parenting, Education, Psychology,
Business, Infrastructure management, Development and Aid Relief, etc
PAGE 20 OF 54
The evidence suggests that experts make sense of complex phenomena in their
domains by developing higher-level conceptual structures that reduce the hyperdimensionality of searches for solution or strategy elements.
For example a chess grand master does not see a chess board in play as just the
sum of the positions of the individual pieces, but as coherent larger scale patterns
which can be reasoned about, whereas the novice who can only see the
individual pieces is overwhelmed by the combinatorial explosions of
possibilities. Experienced doctors similarly can quickly diagnose complex
conditions from patterns created by a few observations whereas the recent
graduate has to exhaustively investigate all the possibilities with tests and
questions.
By extrapolation then, a reasonable goal might be to discover a set of CAS-based
mental constructs which aid in rapid appreciation of the structure of a large
amount of interconnected information about a C&IN and clarify its essential
dynamics and possibility space. So if this is a successful way of making
complexity more manageable, what are the relevant higher-level concepts for
C&INs?
CAS science suggests that interactions and dynamics in C&INs are more
important than composition and static configurations. But the main sources of
dynamically organised complexity are adaptive, self-organising and regulatory
processes, so could these be the building blocks? and if so, how might they be
used to develop better conceptual structures to deal with networked causality?
To explore this idea further it is necessary to understand how such processes
interact, and what recurring and/or persistent patterns and structures they can
create. Can we develop a “physics of interacting adaptive and S-O processes”?
There are two promising options that have started to be explored. However this
section is still purely speculative and needs development and experimentation.
Multi-scale Intent
This concept is discussed in AG14’s companion report on Complex Operations
[14]. Its relevance here is that it may make a number of simplifications possible.
For example, sets of agents with similar multi-scale intent structures at a given
scale of description may be aggregated and treated as a distributed super-agent,
reducing the number of entities to be considered.
PAGE 21 OF 54
The combination of an agent A who at one scale can effectively control the finer
scale intents of other agents a1, a2, … (whether by coercion, reward or
inspiration) can be replaced with a single agent with the intent of A and the
enlarged capabilities of the set.
When combined with the adaptive processes whereby agents’ intents are
changed, the dynamics of a complex situation with a large number of agents
might be replaced with a much smaller number of variable composite interacting
agents at a several scales.
Attractors
Attractors are regions of the possibility space of the situation which are more
likely to be occupied than other regions, and in the extreme, once entered, are
very difficult to escape from.
In Non Linear Dynamical Systems theory, attractors are well-defined
mathematical constructs which characterise the system’s asymptotic phase space
trajectories as the net result of all the forces acting. Such attractors are therefore
manifestations of equilibrium seeking physical processes. Examples include
point attractors, limit cycles and the Lorenz strange attractors.
However in complex adaptive systems, agent behaviours are driven by intents
and opportunities not simply by physical forces (although they are of course
bound by the laws of physics, their behaviours are not comprehensible from that
perspective alone).
We claim that adaptive processes of agents (whether individual or collective)
create “attractors” by virtue of the fact that they exert a bias for particular
outcomes and against other outcomes, so distorting what might otherwise have
been an equi-probable range of outcomes. The quotation marks are to
acknowledge that this is a qualitatively different phenomenon than the
mathematical attractors mentioned above, although there is an obvious analogy.
In the formal mathematical sense, what creates the attractor are the restoring
equilibrium seeking physical forces that bring the system back when a
perturbation causes an excursion. In complex adaptive systems, what creates the
attractor are the dynamic adaptive processes of agents acting in their own
interests. This creates a dynamic stability as opposed to an intrinsic lowestenergy stability.
PAGE 22 OF 54
The attractor created by a single agent might be quite weak, but if many agents’
adaptive processes are coherently aligned then the collective result may be quite
a deep attractor.
However such a coherent alignment would be vulnerable to sudden collapse if a
changed condition affected them all in exactly the same way.
A more robust attractor might result from the interactions of a number of
different but interlocked adaptive processes whereby a perturbation that
threatened to weaken any one element would stimulate adaptive responses from
those others most immediately affected, which in turn would stimulate others, so
that successive synergistic waves of adaptive responses would overcome the
perturbation and restore, perhaps even deepen the attractor.
Such attractors would be manifested as very persistent features of the C&IN
which one would describe either as resilient if they were perceived as desirable,
or intractable, if they were seen as problems.
Many examples spring to mind, e.g. the intractability of corruption, drug use,
poverty and crime problems. Each of these can be thought of as an attractor and
its persistence understood as resulting from the interacting adaptive processes
that are stimulated by any attempt to modify the dynamics. Interestingly, many
of the individual agents whose adaptive behaviours contribute to the dynamic
stability of the attractor may in fact be trapped by it themselves.
It is also evident that such attractors can interact at a larger scale and create more
systemic attractors (e.g. corruption protecting and profiting from crime which
protects and profits from drug use, which contributes to and exploits poverty
which feeds corruption etc)
This is just one example, but the principle worth exploring here is that there may
be a number of robust features of C&INs which result from the myriad ways in
which adaptive and self-organising processes could interact in mutually
stabilising ways.
Discovering these might be a key to developing those higher level constructs
which could replace the overwhelming and confusing diversity and multiplicity
of individual entities and events with a much richer but simpler picture of the
essential dynamic interactions of fewer significant elements overlaid on a
background of static that can be filtered out.
PAGE 23 OF 54
The underlying fundamental research challenge here is to develop a general
theoretical framework for understanding the interactions between adaptive and
self-organising processes.
What is known about interacting adaptive processes?
Specific examples of interacting adaptive processes, and adaptive processes
interacting with self-organising processes, have been examined in a number of
fields. For example:

Population dynamics study the co-evolution of species, in other words
how two (or more) evolutionary processes interact. Arms races and the
Red Queen effectvii are both examples of competitive evolution between
two species, and can also be seen in competitive adaptations in two agents
(e.g. a literal arms race)

Group selection in evolutionary theory (the interaction between adaptive
selection processes occurring at the individual level within a species, and
at the group level within a group-competitive environment) has recently
been rehabilitated by DS Wilson, Sober [15, 16], and others after years of
mainstream rejection. New analyses of these interacting adaptive
processes at different scales offer instructive examples. In fact multi-level
selection theories [17] are now a hot topic in theoretical biology so there is
a lot of new thinking that can be exploited.

Niche Construction is the term given to the relatively new theory that
explicitly addresses interaction between genetic evolution and the
adaptive processes that shape the environment inherited by progeny,
thereby modifying the selection processes operating on them. Extensive
mathematical analyses of these very different interacting adaptive
processes result in counterintuitive and very significant effects on
evolutionary outcomes [18].

Even more ambitious studies of interactions between four different
inheritance systems (Genetics, Epigenetics, Behavioural, Symbolic) are
now being looked at [19].
The Red Queen effect is named for the character in Lewis Carroll’s Through the Looking Glass, who
proclaims that “it takes all the running you can do, to keep in the same place”. Clearly the evolutionary
consequernce of an arms race.
vii
PAGE 24 OF 54

The AG14 Conceptual Framework for Adaptation [2] recognises five
nested levels of adaptation of which 4 involve applying adaptation to
adaptive processes. These represent a subset of the ways in which
adaptive processes can interact - a carefully selected subset that can
operate simultaneously and synergistically.

So-called “evo-devo” studies address interaction between evolutionarily
developed self-organising processes and clade selection processes in
development [20].
There is a rich and rapidly developing literature here that could and should be
mined for important insights into how to formulate a more general theory of
interacting adaptive processes. Such a theory could yield a wealth of novel and
valuable ideas that could be applied to the problem of understanding and
influencing complex systems.
This is probably the single most important theoretical research challenge that has
come out of this thrust, and advances could have immediate applicability in
better ways to disable undesirable attractors in complex operations including use
of IEDs, government corruption, cultivation of narcotics and so on, and helping
to chart better strategies to foster the emergence of more acceptable attractors in
counterinsurgency and interventions in failed states.
It could also have applicability to many other domains, including organisational
change, policy development, and complex systems engineering. Particular issues
range from understanding the consequences (especially unintended) of
interventions designed to address climate change, to improving the effectiveness
of aid agencies, to name but a few.
Implementation: The C&IN Program
This ambitious program is clearly beyond the capacity of AG14 to implement
alone. However, AG14 has been able to seed interest in a much larger community
which is now providing funding, support and active participative contributions.
Scoping Paper and Open Questions
A short scoping paper circulated in early 2009 stimulated positive responses
from many quarters, resulting in over $100K of funding being raised from the US
Air Force Research Lab’s Asian Office of Aerospace R&D (AFRL AOARD), the
US Office of Naval Research – Global (ONR-G), the Australian Commonwealth
PAGE 25 OF 54
Scientific and Industrial Research Organisation (CSIRO), the University of South
Australia (UniSA), the Australian Research Council (ARC, via the Complex Open
Systems Network (COSNet) which they fund) and the Defence Science and
Technology Organisation (DSTO).
To promote discussion, a prioritised list of open questions relating to C&IN in
relevant disciplines was developed. This was useful in identifying the very broad
range of issues which people felt were relevant, and in stimulating interest in
attempting to answer them.
The scoping paper and the open questions are listed as part of the Invitation to
Participate in Annex B.
Workshop Series
The funds raised were primarily intended to support a workshop series whereby
leading international scientists can be engaged to participate and contribute.
An Organising Committee was set up, co-chaired by the AG14 Chair, a senior
CSIRO scientist and two US professors. The OC has arranged four workshops so
far, listed below. The workshops have been reported in AG14 reports as AG14
activities.
Table 1: C&IN Workshops held up till May 2010
Workshop
1.
2.
3.
4.
23-25 February 2009
First, inaugural, workshop on causal and
influence networks, held under the auspices of
Complex 2009 in Shanghai, China
Mt Tamborine, Qld,
Australia July 2009
Focused 5 day workshop with 17 participants
from AS, US, CA and France
Arlington, VA, USA
Held in association with the AAAI November
Symposium - Complex Adaptive Systems and the
Threshold Effect. See [21]
Shanghai, China
5-7 November 2009
Paris, France
November 2009
Held at the Institut des Systèmes Complexes Paris
in association with the International Conference
on Emergence in Geographic Space: Concepts,
Methods and Models
PAGE 26 OF 54
There will be at least two more workshops in the second half of 2010.
The purposes of the workshops are to build and develop a broad community of
scientists and thinkers to engage in an extended dialogue about the questions
raised here and in the Open Questions, growing collective understanding, and
sharpening the research questions.
Accordingly the workshops have been structured to foster critical debate with
participants presenting brief summaries of relevant papers and short
thoughtpieces around new ideas or questions to stimulate longer discussion
sessions.
For more information see: http://cs.calstatela.edu/wiki/index.php
Insights from Workshops so far
A lot of ground has been covered and intriguing ideas put forward. To give a
sense of the breadth, a smattering of dot points highlights follow, but there is no
attempt yet to form a coherent view.

Prof Cliff Hooker (Uni of Newcastle, NSW, Australia) – bio-cognition,
adaptive management of CS.

Prof Sandra Mitchell (Uni of Pittsburgh, US) – causation and explanation
in biological systems.

Prof George Ellis (Uni of Capetown, South Africa) – 5 kinds of top-down
causation in CS, theory of intervention.

Aaron Bramson (Uni of Michigan) – mathematical formulation & analysis
of tipping points.

Dr Beth Fulton (CSIRO, Australia) – adaptive modelling and
participative/adaptive management of socio-economic-ecological systems.

Dr Anil Seth (Uni of Sussex, UK) – Granger causality in neuroscience.

Nicholas Brodu (Uni of Rennes, France) – conceptual frameworks for
causation.

Dr Valeriy Vyatkin (Uni of Auckland, NZ) – Causality Analysis Using
Model-Checking.
PAGE 27 OF 54

Dr René Doursat (CNRS, Ecole Polytechnique and Director, Complex
Systemes Institute (ISCIF), Paris, France) – Causing & influencing patterns
by designing the agents.

Prof Jacques Dubucs (Uni of Paris I – Sorbonne, Head Institute of History
and Philosophy of Science and Technology) – Unfolding Complexity
(knowing simple mechanisms in CS is not sufficient).

Prof Hernan Makse (City College of New York, US) – Modularity map of
C&IN (human embryo development).
One strong theme did coalesce in the 4th workshop – the central role of the
human mind and the multiple ways in which it was relevant to the program
objectives:
1. Human Mind as a C&IN i.e. as an object of study, probably the most
complex C&IN we know of, the consequence being that neuroscience,
psychology and cognitive science are all fertile domains to consult
2. Human Mind as Decision-Maker i.e. as the target of the study, since it is
human minds that we wish to aid in doing the understanding and
decision-making to exert influence, the consequence being that is
necessary to understand their strengths and limitations in order to deliver
more useful understanding and decision aids – this links in directly with
the AG14 thrust on Complex Cognition and Decision-Making [23]
3. Human Minds within a C&IN i.e. as elements of C&INs of interest, it is
generally human behaviour in C&INs that add greatly to their
intransigence, the consequence being that it is likely to be important and
valuable to develop complex adaptive and multi-scalar conceptual models
of human agents in C&INs (as discussed in [24]), and
4. Human Mind as Researcher i.e. as the subject of the study, via the minds of
the study participants.
This will be used as an organising and integrating theme in the development of
the final products.
Deliverables and Forward Workplan
This project began with a very practical goal – improving the ability to
understand and influence complex situations.
PAGE 28 OF 54
The journey of seeking insights and novel ideas that might contribute to that goal
has meandered through a very wide landscape of human knowledge, experience
and reflection.
At present (May 2010) we stand somewhere close to the widest perspective,
which will be progressively focused as the key ideas and questions emerge.
The closing phase of the project over the next 1-2 years, will be a process of
abstraction, generalisation and synthesis.
In the closing session of the 4th workshop in Paris, the group revisited its
objectives and planned deliverables and outlined a forward workplan. A
summary is presented here.
The main outcome, apart from the individual learnings of all the participants,
will be a publication of one or two volumes of peer reviewed and edited invited
and contributed papers that provide a useful guide to best practice, priority
challenges, and stimulate further research and exploration of novel approaches.
The draft structure of this publication is in five sections:
1. Introduction:
a.
Statement of the Problem,
b.
Scope and Issues,
c.
History (incl of this project)
2. The State of the Art
a.
A number of compact subsections, one per selected knowledge
domain, in which the following are discussed:
i. Types of C&INs
ii. Methodologies, tools and techniques
iii. Why they work for these C&INs (and what the limits are,
to what extent do they not work?)
iv. What the high pay-off problems are
v. Optional: illustrative examples and case studies provided
by domain experts could be included in sidebars in some
cases
PAGE 29 OF 54
b.
Synthesis:
i. Approaches, tools and techniques identified in the review
will be summarised and generalised, together with
discussion of the conditions under which they are valid;
ii. These results will inform a mapping between:
o The generalised tools and approaches etc, and
o Classification of types of C&INs
iii. Summary discussion of domain-specific and domain
independent (common) high pay-off issues
3. Human Factors in Interacting with Complex systems
a. Discussion of how this is an integrating / organising theme
for the whole project
b. How the strengths and limitations of human decisionmaking should impact the way in which the results of this
project are presented and developed
4. Novel Approaches
It is hoped that a number of novel approaches will be proposed in
addition to the speculative ideas put forward in this progress report. This
section will consist of a number of invited and contributed papers and
thoughtpieces.
5. Synthesis and Future Directions
PAGE 30 OF 54
References
1. TTCP JSA AG14 Final Report, May 2010 JSA-AG14-AR-2010
2. TTCP JSA AG14 Synthesis Report: Conceptual Framework for Adaptation,
May 2010 DOC – AG14 - CFA #3-2010
3. Nicholson A and Korb K Bayesian Artificial Intelligence Chapman & Hall 2004
4. Gossink, D et al Assessment of Causal and Influence Network Modelling tools
relevant to OPSTSR 69 DSTO Internal Report [CLASSIFIED] 2007
5. Sterman J System Dynamics modelling: Tools for learning in a complex world
California Management Review, 43 (4) pp8-25 2001
6. Van Zijderveld, EJA Marvel – principles of a method for semi-qualitative system
behaviour and policy analysis, TNO, NL
www.tno.nl/downloads/def_alg_Paper_MARVEL_SDS_20071.pdf
7. Newman M, Barabasi A-L, Watts DJ The Structure and dynamics of Networks
Princeton University Press 2006
8. Carley, KM Dynamic Network Analysis, NRC Workshop on Social Network
Modelling and Analysis (R. Breiger and KM Carley Eds)
9. TTCP JSA AG14 Supplementary Report, Pestov, I. CAS in Defence and Security:
a Network-Enabled Approach, May 2010. DOC-AG14-C&IN:NEA #5-2010
10. Epstein, JM. Agent Based Computational Models and Generative Social Science.
Complexity, Vol 4, # 5, pp 41-60, 1999
11. Horne, G Beyond Point Estimates: Operational Synthesis and Data Farming
Maneuver Warfare Science 2001 USMC Project Albert, Quantico, VA
12. ANL Episim ref
13. Katok, AB and Hasselblatt, B Introduction to the Modern Theory of Dynamical
Systems, Cambridge University Press, 1999
14. TTCP JSA AG14 Synthesis Report: Complex Adaptive Systems Concepts for
Complex Operations, May 2010 DOC – AG14 - CxOps #2-2010
PAGE 31 OF 54
15. Sober, E. and Wilson, D.S.: 1998, Unto Others – the Evolution and Psychology of
Unselfish Behavior, Harvard University Press, Cambridge.
16. Wilson, D.S.: 1990, Weak Altruism, Strong Group Selection, Oikos 59, 135–140.
17. Okasha, S Multi-Level Selection, Co-variance and Contextual Analysis Brit. J. Phil.
Sci. 55 (2004) 481-504
18. Odling-Smee, FJ, Laland, K and Feldman, MW Niche Construction: The
Neglected Process in Evolution Monographs in Population Biology, Simon A.
Levin and Henry S. Horn (Eds), Princeton.
19. Jablonska, E and Lamb, M. Evolution in four Dimensions: Genetic, Epigenetic,
Behavioral and Symbolic variation in the History of Life Life and Mind:
Philosophical Issue sin Biology and Psychology, MIT Press, 2006
20. Kirschner, M.W. and Gerhart, J.C., 2005. The Plausibility of Life – Resolving
Darwin’s Dilemma. Yale University Press, New Haven and London.
21. AAAI (2009). Complex Adaptive Systems and the Threshold Effect: Views
from the Natural and Social Sciences. In Proceedings of the AAAI Fall
Symposium: Arlington 2009, Mirsad Hadzikadic and Ted Carmichael Eds,
AAAI/MIT press.
22. Mitleton-Kelly, E. The Emergence of Final Cause in Aaltonen, M., (Ed) The Third
Lens. Multi-Ontology Sense-Making and Strategic Decision-making, Ashgate
Publishing Limited, UK 2007
23. TTCP JSA AG14 Progress Report: Complex Cognition and Decision-Making,
May 2010 DOC – AG14 - CxDM #6-2010
24. TTCP JSA AG14 Synthesis Report: Complex Adaptive System Concepts for
Complex Operations, May 2010 DOC – AG14 - CxOps #2-2010
Additional Reading
Dowe, Phil. Causal Processes, The Stanford Encyclopedia of Philosophy (Fall 2008
Edition), Edward N. Zalta (ed.).
Buchanan, B.G. and Shortliffe, E.H, Rule-based Expert Systems: The MYCIN
Experiments of the Stanford Heuristic Programming Project, Addison-Wesley,
reading, MA, 1984.
PAGE 32 OF 54
de Kleer, J. & Williams, B.C., Diagnosing Multiple Faults, Artif. Intel. 32, 97 1987.
Hitchcock, Christopher. Probabilistic Causation, The Stanford Encyclopedia of
Philosophy (Fall 2008 Edition), Edward N. Zalta (ed.).
Kaminska-Labbe, R., On the Co-Evolution of Causality: A Study or Aristotelian and
other Entangled Influences, Acad. Management, Annual Meeting, Atlanta, 2006
Malinas, Gary and John Bigelow, Simpson's Paradox, The Stanford Encyclopedia
of Philosophy (Winter 2008 Edition), Edward N. Zalta (ed.).
Mancosu, Paolo, Explanation in Mathematics, The Stanford Encyclopedia of
Philosophy (Fall 2008 Edition), Edward N. Zalta (ed.).
Mayes, G. Randolph, Theories of Explanation, The Internet Encyclopedia of
Philosophy, J. Fieser (ed.).
Mitchell, S., Exporting Causal Knowledge in Evolutionary and Developmental Biology,
Philosophy of Science, 75 (December 2008) pp. 697–706.
Nordin, Ingemar Complex Causation and the Virtue of Pluralism Medicine, Health
Care and Philosophy, 9:321-323, 2006
Pearl, Judea, Causality, see http://bayes.cs.ucla.edu/BOOK-2K
Reiter, R., A Theory of Diagnosis from First Principles, Artif. Intel. 32, 57-95, 1987.
Rittel, Horst and Melvin Webber, Dilemmas in a General Theory of Planning, Policy
Sciences 4 (1973), 155-169, Elsevier Scientific Publishing Company, Amsterdam.
Schaffer, Jonathan, The Metaphysics of Causation, The Stanford Encyclopedia of
Philosophy (Fall 2008 Edition), Edward N. Zalta (ed.).
Shalizi, Cosma R. Bayes < Darwin-Wallace, Blog entry.
Wagner, Andreas, Causality in Complex Systems,
http://www.bioc.uzh.ch/wagner/papers/BiologyPhilosophy1999.pdf
Woodward, James, Causation and Manipulability, The Stanford Encyclopedia of
Philosophy (Winter 2008 Edition), Edward N. Zalta (ed.).
Woodward, James, Scientific Explanation, The Stanford Encyclopedia of
Philosophy (Spring 2009 Edition), Edward N. Zalta (ed.).
PAGE 33 OF 54
Annex A: Essential and Desirable Requirements for a C&IN Support
Tool for Complex Operations
Feature
How this feature
supports the
Adaptivity Battle
Urgency
What tool should do
Desired
output
What users
can expect to
able to do
Show causal network
on standard laptop
screen.
Causal graph
that visually
depicts the
relationships
between nodes
Some
customisation.
Drag node
locations, hide
or show
nodes.
Users can read
the text from
within the
software
Users can edit
and add text.
Present data
visually on a
single screen
and print.
Visual
representation aids
comprehension.
Single screen
summary view aids
easy briefing for CO
and others.
High
Include
narrative text
Allows someone
who was not
involved in creating
the model to
understand it. Tells
a story that is
consistent with the
causal graph.
High
Implement
version control
of models,
including
metadata.
Adaptation requires
change. An audit
trail on the history
of the causal
network allows the
user to understand
changes and the
progress of the
model.
High
Allow users to access
previous or
alternative versions
of the model. This
could be
sophisticated (eg
CVS) or rely on users
to develop naming
conventions and use
Save As to start new
branches and
significant updates.
A set of model
files that can
be loaded one
at a time. Each
model has
metadata so it
can be
uniquely
identified
User decides
when
significant
changes merit
a new version,
and enters
metadata.
Show
difference
between two
versions.
Better helps
understanding in
previous item on
version control.
Very
Low
Allows a user to see
what has changed
between two
versions.
List of
differences
between two
models.
Two versions
to compare.
View temporal
dynamics of
variables
Time scales and
time lags are critical
in understanding
feedback.
High
Graph variables over
time.
A temporal
view of
individual
variables.
Select variable
to view
graphed over
time, and set
time window
i.e. add
annotations
Export to a printable
format.
Allow entry of free
text.
Text can be
associated with the
whole model, or
with components of
the model.
PAGE 34 OF 54
Identify and
display
feedback loops
Feedbacks create
nonlinearity. They
require careful
attention so that
points of leverage
and stability can be
identified to focus
adaptive action.
Medium
Feedback loops are
shown in a list or
highlighted in the
visual display. The
kind of feeback (+/-)
would be also
shown.
List of
classified
feedback
loops, with
attributes . Or
drawn as a
layer on graph.
Run
algorithm to
ID loops.
Choose
whether to
hide or show
loops in
visualisation
Identify and
display
internal
inconsistencies;
If the model is
inconsistent, it may
lead to invalid
inferences about
what action to take.
Low
If the user enters a
set of hypotheses
that are logically
impossible, the tool
should flag this, and
require re-entry.
Error message
describes
inconsistent
model
components
User decides
if error or if
alternate
models are
needed.
Display
strength and
nature of
relationships
Aids
comprehension.
Low
Provide visual
representation of
strength of
relationships, so that
stronger
relationships are
bolder and +/- are
different colours
Variable width
arcs in causal
graph
Enter strength
Represent
confidence;
User knows relative
confidence to place
in different models,
based on evidence
Low
Provide a confidence
level for the whole
model, and for
components of the
model.
Confidence in
[0,1]
Enter
evidence
Show causal
networks at
various scales,
allowing
aggregation,
hierarchical
views and
zooming
Aids
comprehension.
Structure may be
obscured by
hundreds of
variables. Give a
sense of structure
and importance
Low
Like Google Earth,
some links and
nodes can only be
seen as the user
zooms in. They are
hidden to give a
synoptic view. Each
node has a scale
attribute, and is
hidden at coarser
scales.
Partial
visualisation
Either choose
a scale, which
determines
automatically
what is
displayed; or
manually
show/hide
nodes
Facilitate
search, query,
and ability to
select a
variable and
highlight every
node within x
Increases efficiency
of user who wants
to understand or
change the model.
Low
User inputs a query
which changes their
view of the data. Can
be used to hide
information or focus
on a region of the
causal network.
Partial
visualisation
Query that
determines
the focus and
scale of
observation
PAGE 35 OF 54
hops
Link visual
components to
narrative
components
Switching between
visuals and text aids
user synthesise information for deeper
comprehension.
Very
Low
Double clicking on a
node or relationship
pops up explanatory
text, documenting
the source and
reason for the
node/relationship
Text boxes that
can be
shown/hidden
within the
visualisation.
User enters
free text.
Support
multiple
concurrent
users with
different views
of the same
causal network
Enables adaptation
to occur at multiple
levels and in
parallel.
Low
Manage
simultaneous
read/write requests
from multiple users.
Messages such
as: File
currently
locked by user
X. Open for
read only?
Whether they
require read
or write
access on
Open
Represent
logical
relations
between
hypotheses, eg
AND, OR,
NAND, NOR
and XOR
Enables more
sophisticated
relationships to be
expressed.
Low
Allow input of
coupling between
relationships.
Visualise
couplings
between
relationships
User specifies
the couplings
Display spatial
information in
GIS, and
animate
temporal data.
Aids
comprehension.
Shows spatial and
temporal patterns
that may otherwise
be missed
Very
Low
Include a GIS
mapping layer, or
export data in a GIS
format. Run
temporal dynamics
with movie style
functionality (play,
pause, step)
Map based
animated
visualisation
Each variable
and
relationship
must have
associated
space and
time
attributes
input
Allows
multiple
concurrent
users with
competing
theories that
may involve
inconsistent
views of the
same causal
network
Can evaluate the
performance of
multiple models
from the same
evidence.
Inconsistencies
between models
informs important
information
requirements
Very
Low
Allows users to
create a new variant
of the model with
some different nodes
and relationships.
New evidence can
update multiple
concurrent models.
Multiple
models.
Comparison
between
models
produces a
table of
inconsistencies.
User creates
more than one
model.
PAGE 36 OF 54
DESIRABLE CHARACTERISTICS
Feature
Intent
Importance
User Documentation
Is documentation available? If so is it in an accessible format and
what degree of specialisation is assumed?
High
Training
Documentation
Is there a training program for the tool that is well established and
known to provide users sufficient knowledge and expertise to use
the tools effectively.
High
Need for Technical
Understanding
Does the tool require the user to have a substantial understanding
of the mathematical techniques being employed and the
implications of these Techniques.
Medium
Intuitive Interaction
Are the menus, tool selectors, dialogue boxes etc, in places that a
user may expect them
High
Convenience
Can a user import extant models, are there clipboard like features
eg. Cut, Paste, Undo and Redo.
Medium
Extensibility / API’s
Is the tool under consideration easily extended or supplied with a
set of API’s so that it can be modified to suit end application. (Who
owns the tool and how accessible is it.)
Medium
Models instantly
recognisable and
meaningful.
Are the models understandable without substantial interpretation?
High
Platformdependence
Is the tool only available on certain platforms and does it function
identically across platforms.
Low
Objective
Correctness
Has the tool been shown to be objectively correct? i.e. under a
controlled environment provides confirmed correct results. To
what degree are scientifically validated techniques utilised.
High
Evidence Inclusion
Does the tool explicitly allow a user to introduce empirical evidence
back into the model
High
Distributed
Can the tool support multiple users simultaneously
Low
Part of a Tool Suite
Is the tool self contained or part of a series of tools with extensions
(eg. BPE and Matlab).
Low
Scalability
Is the tool capable of handling large models?
Medium
Computability
Can the tool compute on large models, does it scale linearly, or nonlinearly.
Medium
PAGE 37 OF 54
Annex B: International Collaborative Research Program
Membership of Organising Committee
Co-Chairs
Anne-Marie Grisogono
David Batten
Prof Russ Abbott
Prof Paul Davies
DSTO Australia
CSIRO Australia
California State University, LA, USA
Beyond Center, ASU, Phoenix, AZ, USA
Members
Axel Bender
Berryman Mathew
Bob Bolia
Bohdan Durnota
David Sonntag
Fabio Boschetti
Jimmie McEver
Hussein Abbass
Patrick Beautement
Scott Wheeler
Vanja Radenovic
DSTO Australia
UniSA Australia
ONR USA
Tjurunga Research Pty Ltd, China
AFRL USA
CSIRO Australia
EBR, USA
UNSW Australia
Abaci Partners UK
DSTO Australia
DSTO Australia
Invitation to Participate
On the following pages is the letter and information that has been sent to the
scientists so far invited to participate.
PAGE 38 OF 54
International Collaborative Research Program: Understanding
and Intervening in Systems characterised by Complex Causal and Influence Networks
8 March 2016
Dear colleague
I am writing to invite your participation in an international collaborative research program on Causality in
Complex Systems over the next 12 months.
As you will be well aware, we currently face many serious global problems whose underlying dynamics are
better described as Complex Causal and Influence Networks than as linear causal chains.
Addressing such problems calls for better ways of learning about, understanding and influencing situations
where causal pathways are so interconnected that traditional planning and management approaches fail.
With the generous support of the research agencies shown below, and under the auspices of The Technical
Cooperation Program Joint Systems Analysis Action Group on Complex Adaptive Systems for Defence, we aim
to involve a number of leading researchers in relevant fields and to stimulate lively and deep exchange of ideas
between them through a series of workshops and through online interaction.
The overall intention is to review and advance the state of the art in understanding and influencing complex
systems, culminating in an edited collection of invited and contributed papers.
Your understanding of complex system dynamics would be very valuable, and we would really appreciate your
perspectives on our research questions.
Realising that there are many demands on your time, we are very flexible about how you might engage further
with us, and are happy to discuss ways to facilitate your interactions with us.
More information is provided on the next page and in appended documents, including scope, details about the
planned workshops and other ways of participating.
As a first step we would appreciate an indication of whether you wish to accept this invitation, and if so, what
form that might take.
Looking forward to hearing from you.
Respectfully
Anne-Marie Grisogono
On behalf of the Causality in Complex Systems Organising Group
Prof Hussein Abbass, UNSW
Prof Russ Abbott, US CalStateLA
David Batten, CSIRO (co-chair)
Patrick Beautement, UK, Abaci
Matthew Berryman, UniSA
Axel Bender, DSTO
Bob Bolia, US ONR Global
Fabio Boschetti , CSIRO
Marie Grisogono, DSTO (co-chair)
David Sonntag, US AFRL AOARD
DSTO
Alex Ryan, SAMS, US Army
DSTO
AnneVanja Radenovic,
Scott
Wheeler,
International Collaborative Research Program: Understanding
and Intervening in Systems characterised by Complex Causal and Influence Networks
Program Scope
To initiate the program, we have developed a short scoping paper and a set of Open Questions (both attached).
These documents, together with other information, are also available on the program wiki:
http://cs.calstatela.edu/wiki/index.php/Causality_in_complex_sytems
We invite you to share your thoughts in response to whichever questions attract your interest by email to the
Co-chairs below. We expect the questions to evolve through the course of the program, so we also welcome
your suggested revisions and new questions.
Workshop Series
4 workshops have already been held, in Shanghai (as a special session of Complex’09, Feb 09), in Mt
Tamborine, Qld, Australia 6 -10 July 2009, in Arlington, VA (as a session within AAAI Fall Symposium on CAS) 5
- 7 Nov 2009, and in Paris, France (as a session within Emergence in Geographic Space) 23-25 Nov 2009. At
least two more workshops are planned, in Australia and the US, to progress and bring the program to a closing
synthesis.
Intended Outcome
An edited 1 or 2 volume collection of invited papers addressing a coherent research agenda, evolved from our
Open Questions, to be published by a major scientific publisher, in late 2011 or early 2012.
We anticipate that participants will both learn from each other and generate new insights through the course
of this program. The papers are therefore expected to be written after the workshops are completed, so that
they include the new understandings that are reached.
Ways of Participating
Any combination of these is possible. Please feel free to discuss your preferences with us.
A. Written response to some of the Open Questions in the attached document.
B. Suggest papers to read and other contributors to invite.
C. Participate in one or more of the workshops that are planned.
D. Contribute a thought-piece to stimulate discussion during the program.
E. Participate in online discussion through the wiki.
F. Write a paper for the final collection of papers to be published. Papers may be contributed or
invited. Invited papers will be sought from funded participants.
G. Join the Organising Group, and / or become an Editor or Reviewer.
Dr David Batten (Co-Chair)
Dr Anne-Marie Grisogono (Co-Chair)
Senior Research Fellow,
Commonwealth Scientific and Industrial Research
Organisation
and The Temaplan Group
T +613 92394420 M +61431708361
Research Leader Complex Adaptive Systems
DSTO Long Range Research Fellow
Chair TTCP JSA AG14 Complex Adaptive Systems for Defence
Defence Science and Technology Organisation
T +618 82596532 M +614 09076684
David.Batten@csiro.au
Anne-Marie.Grisogono@dsto.defence.gov.au
International Collaborative Research Program:
Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks
SCOPING WHITE PAPER
Research Theme on Networked Causality:
Understanding and Influencing Systems
with Complex Causal and Influence Networks
Background:
The concept of causality has a long history in philosophy and science and remains controversial to this day.
This project does not seek to delve into the formal or epistemological nature of causality for its own sake, but
rather to take a very pragmatic approach to causality as the production and propagation of effects, with the aim
of supporting the understanding of real world complex systems.
Of particular interest is the problem of analysing possible future trajectories of a complex system and the
consequences of various actions or events in order to support decisions about possible interventions.
What makes systems complex is the network of interdependencies between the elements of the system.
This means that consequences of any event or property develop through many interacting pathways, and
similarly, that if we want to know how a particular property or event came about, we will find that there were
many cross-linked pathways that contributed to it. In such a system therefore, we cannot expect simple
causality (one cause – one effect), or linear causal chains (each effect inexorably causing the next, like a Rube
Goldberg machine), to hold in general.
Yet models based on simple causality and linear causal chains are attractive because they seem tractable,
unlike the confusing complexity of the real system.
It is possible to make a complex system appear simpler by restricting the scope of attention to a particular
pathway, but if the scope is widened to include other pathways, or if unexpected side-effects that have
propagated through those pathways are linked back and suddenly manifested within the restricted scope, we
are quickly reminded that the causal chain was just one of many pathways through a network.
Such systems are therefore inherently characterised not by linear causal chains, but by networks 8 of causal
realtionships through which consequences propagate and interact. Such networks of interactions between
contributing factors can exhibit emergent behaviours which are not readily attributable or comprehensible. This
simple observation has some significant consequences for our ability to understand and influence complex
systems:
8
-
Properties of complex systems are the product of many causal pathways, which may themselves be
cross-linked – thus influencing each other;
-
Consequences of events or properties of complex systems may also develop through many causal
pathways which may also be cross-linked and influence each other;
-
Causal and influence relationships in a network can close into feedback loops that will amplify or
suppress certain types of outcomes, and which can also interact with each other;
For example, the firing of a particular neuron in a brain is influenced by the input signals received from many other neurons, and its own
firing in turn influences the probability of firing for many further downstream neurons. Looking at the entire network of interconnected
neurons we see firing patterns which are associated with complex higher-level functions.
International Collaborative Research Program:
Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks
-
Multiple interacting pathways exist in general between an event or property of a complex system at
one time, and a property of interest of the system at some later time.
We are using the provisional term Causal and Influence Network (C&IN) to describe such phenomena, but
other terms have also been proposed: emergent causality, complex causality, networked causality and
decentralised causality.
A fundamental property of real complex systems is that they exist at multiple scales, raising the question of
how causality at the lower levels is manifested in behaviour and properties at the higher levels.
In addition, there is no unique perspective which provides a complete view of a complex system, there is no
unique way of determining its boundary, and wherever the boundary is drawn, interactions between the system
and its environment are important.
In real complex systems, many properties and events will be inaccessible to observation, so one has to expect
to always be working with uncertainty and incomplete information.
All these factors add to the difficulty of studying the C&IN operating in a given complex system.
By contrast, much of our cultural conditioning is predicated on a naïve view of linear causal chains – examples
include: finding ‘the cause’ of an effect, or ‘the person’ to be held responsible for something, or ‘the cure’ for a
problem etc. This focus on singular or primary causes makes it more difficult to intervene effectively in complex
systems and produce desired outcomes without attendant undesired ones (so-called ‘side-effects’ or
unintended consequences).
Yet effective ways of interacting with particular complex systems have been developed in many domains, such
as medicine, personal relationships, politics, business, genomics, agriculture, etc. While these domains are
certainly not free of unintended consequences, neither are people paralysed into inaction by the bewildering
difficulty of understanding the complex systems they are interacting with.
On the other hand, our limited ability to understand and purposefully influence situations and systems that have
large scale complexity hampers our effectiveness in dealing with them – witness for example the attempts to
contain the recent cascading economic crises, ongoing problems with terrorism and insurgencies, difficulty in
motivating concerted international action on climate change, world poverty, and many other serious global
challenges.
On the defence sector, the problems of creating, transforming, integrating, managing and employing our
extensively networked forces also stem from their underlying complex C&IN structure.
In all these arenas, better ways of understanding and working with C&INs might improve effective action.
Approach
G. Explore and review the state of the art of theoretical concepts and approaches, and practical tools and
techniques in use across a number of disciplines that deal with complex systems. In particular, we will pay
attention to how they:

discover and model the C&INs operating in their systems of interest;

Explain how observed properties and behaviour have come about;

Think and reason about the possible consequences of observed or hypothetical states or processes;

Estimate the probablilities of various outcomes, and

Explore how hypothetical properties or events might come about.
H. Generalise the identified theoretical concepts and approaches, and tools and techniques, determine under
what conditions they are valid, and discuss their implications for how to:
International Collaborative Research Program:
Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks

define systemic (i.e. networked) causality in contrast to traditional notions of direct causality;

assess whether cause-effect relationships can be identified and analysed in systems of agents that are
changing interactively over time;

Identify, capture and model (where possible) the C&INs operating in several systems of interest;
I.
Hence develop a set of classifications for C&INs that can be used to assess which theoretical concepts
and approaches, and tools and techniques are applicable.
J.
Develop a prioritised list of open questions in relation to C&INs in the disciplines consulted, and hence lists
of both fundamental C&IN research questions, and domain-specific high pay-off C&IN questions.
K. Look for cross-disciplinary opportunities to address any of the specific questions with concepts or
techniques generalised from other domains.
L. develop and explore innovative CAS approaches to tackling selected fundamental questions.
More Detailed Problem Statement
There are four main areas in relation to C&INs where the project seeks to leverage existing knowledge and
expertise in relevant disciplines, and to make progress in assembling a generalised set of approaches,
concepts and tools:
5. Methodology for engagement with complex C&INs:

categorisation of causal and influence networks and of our relationships with them,

understanding what is and is not possible to do in different cases,

developing appropriate meta-strategies for engagement.
6. Learning about the C&IN:

choosing system boundaries,

finding sources of information, choosing what to observe, and the resolution of observations,

use of probing actions,

developing and testing conjectures about the C&IN,

determining what can be inferred and predicted,

identifying critical uncertainties,

identifying and learning about important interactions through the system boundaries .
7. Modelling the C&IN:

how to represent the information and the conjectures,

dealing with negative, uncertain, contradictory and incomplete information,

dealing with multiple perspectives at multiple scales,

finding useful levels of abstraction,

working with heterogeneous approaches,

representing the C&IN’s environment (which is also a C&IN in its own right),
International Collaborative Research Program:
Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks

exploiting and interrogating the model,

developing and testing techniques for reasoning about and exploring the possible behaviour and
properties of a C&IN,

requirements for support tools.
8. Interacting with and Influencing the C&IN:

developing and evolving measures of success and failure for intervening in the system,

developing and assessing options for stratagems to influence the system,

developing proxies for success and failure associated with the stratagems,

implementing and evolving a stratagem,

determining what needs to be monitored to support continuing evolution of the stratagem and of the
underpinning conjectures.
Examples of Well-known Existing Approaches – strengths and weaknesses

Bayesian techniques – can represent the combination of many causal pathways, can be quantified, but
cannot incorporate feedback loops

System Dynamics – provides good visualisation of causal and inflience networks, can be quantified to
explore different regimes of behaviour, but cannot incorporate innovative adaptive processes

Network theory – many practical analysis tools have been developed, but focuses primarily on
properties arising from topology rather than functionality of nodes

Nonlinear dynamical systems – very powerful mathematical tools permitting analysis of the system’s
phase space, but not applicable to C&INs lacking a mathematical formulation
Interesting Questions to consider

clarifying the relationships between predictabilty, control, influence and determinism, contingency,
chaos, noise and uncertainty

explore such notions of causation as:

the deliberate initiation or stimulation of a causal network to produce an outcome (i.e.
attributing intent and causal power to the human element);

the lynchpin in a causal network, i.e. an element whose removal or modification results in a
significantly different outcome;

the ‘pawn’ who plays a role in a causal network but is entirely dispensable or replaceable i.e.
removing the ‘pawn’ does not significantly change the outcome.

How should we select the subset of C&IN dimensions that provide useful phase space views?

can we identify those aspects of a particular C&IN which have greater and lesser degrees of
predictability?

Is there a way of analysing the topology of a phase space of a C&IN to identify regions of greater and
lesser stability? And regions of greater and lesser influencability?

In the absence of a mathematical (nonlinear dynamical systems) formulation of a C&IN, what utility do
the concepts of attractors, tipping points and thresholds have?
International Collaborative Research Program:
Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks

Can we map adaptive and self-organising processes in a C&IN and associate them with attractors’ or
regions of greater stability / predictability?

Can we develop a “physics” of interacting adaptive processes?
Outcomes and Deliverables

Publication of a book (1-2 volumes) of selected papers by Springer, Elsevier or Wiley, addressing the
above themes.

Provisional assessment of whether cause-and-effect relationships can be identified in nonlinear systems of
agents that are changing interactively.

Produce a list of fundamental C&IN research questions, and high pay-off specific C&IN questions in the
disciplines consulted

Cooperative development of some spin-off benefits to selected specific C&IN questions in the disciplines
consulted
International Collaborative Research
Program: Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks
Some Open Questionsix about Understanding and Influencing
Systems with Complex Causal and Influence Networks (C&INs)
These questions illustrate the tortuous journey that the organising group has been on in the last
few months in formulating the issues that have brought us together.
Fortunately, we are neither seeking consensus nor a definitive articulation of the problem space.
We are however interested in stimulating some lively discussion and evolving a better set of
questions, and in engaging wider and more diverse participation.
We expect to learn many things as a result. They will all be interesting. We hope that some of
them will also be useful to people who have to make decisions in complex situations, in spite of
all the inherent pitfalls and uncertainties!
Please respond to whichever questions motivate you. There is no
expectation that anyone should respond to all of them. Answering even
one is appreciated.
If you work with real complex systems we are particularly interested in your
responses to Part 2 (on the last page) as well.
We will use your responses to evolve and focus our objectives and to plan the workshops
throughout 2009/2010. We expect to have some funding to support invited participation in the
workshops.
Please support your responses where possible with references, and suggestions of other people
who could further contribute.
PART 1 - How does causality work in complex systems??
These questions are grouped into the following domains:
Phenomenology – types of causation, heuristics
1. Are there different types of causation?
Preamble: Different examples of causation that we easily recognise include: a deliberate
intervention into a causal network to produce an outcome (eg a doctor prescribing a
treatment for an illness); the lynchpin in a causal network, (i.e. an element whose removal or
modification results in a significantly different outcome); the pawn who plays a role in a causal
network but is entirely dispensable or replaceable (i.e. removing the ‘pawn’ does not
significantly change the outcome.) These examples suggest two aspects of causation that
may be relevant to our puposes: intentionality in the first instance, and causal power (high for
the lynchpin and low fro the pawn).
We would like to acknowledge that the format of this document is inspired by the ‘Open Questions on the
Origin of Life’ document circulated by Pier Luigi Luisi and Kepa Ruiz-Mirazo to solicit participation in their
workshop of the same title in San Sebastian in May 2009.
ix
International Collaborative Research
Program: Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks
Question: Do you think it is important or valuable to make such distinctions? Why? Are there
other distinctions that also need to be made? Do you know of ways to facilitate recognition of
different instances, for example in a graphical map of a complex causal and influence
network?
Philosophy – framing, relation to explanation, ontology, computability, contingency and determinism
2. What do we mean by causation?
Preamble: There are conceptual links between the notions of causation, predictability,
control, influence, determinism, contingency, explanation and emergence, but the use of
these terms and how they relate to each other is not always clear or consistent in the
literature. This lack of clarity can be an obstacle to resolving important questions and to
making the necessary conceptual distinctions.
Question: What do you think the fundamental concepts are in relation to causation in
complex systems? How do you see their relationships? Where do you think points of
confusion lie and how do you think they should be resolved?
3. Computer Modelling of Causation
Preamble: It has been argued that processes with genuine causal power can not emerge
within a system of formal logic, such as all traditional computer programs are. A similar view
in the field of causality is shared by Rosen x and Hausmanxi, among others, and is also
somehow implicit in Hume’s classic discussion xii. This has crucial implications for complex
causality; first because much research in Complex System Science is carried out via
computer simulation, and second because a closed, formal logic approach is implicit in the
abstraction of complex systems as existing in pre-defined state spaces.
Questions: To what extent can causation be studied via computer modelling? What kind of
causation can be studied via computer modelling? Has any computer modelling approach
had success in examining causation?
4. Contingency versus determinism in the development of a complex system’s trajectory.
Preamble. complex systems are often seen in terms of two basic, opposite schemes,
determinism or necessity, and contingency. Generally, the two principles work hand in the
hand, as each “choice” made by contingency must then comply to natural laws and, in turn,
further contingencies arise through a developing history. However, if we ask whether the
development of a complex system follows an obligatory deterministic pathway (absolute
determinism), or whether it is due to the vagaries of contingency, the two views clash.
The question. How do you see the interplay of these two principles in the temporal
development of a complex system? How should we combine them in an approach to
understanding and influencing complex systems?
x
Rosen, R. Life Itself, a Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life; Columbia
University Press, 2001
xi Daniel M. Hausman, Causal Asymmetries (Cambridge Studies in Probability, Induction and Decision
Theory), Cambridge University Press, 1998
xii David Owens, Causes and Coincidences, Cambridge University Press, 2002
International Collaborative Research
Program: Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks
5. Framing Causation
Preamble: The laws of physics (famously) don't speak in terms of causes. The equations
(famously) work either forwards or backwards. And certainly one wouldn't want to attribute a
teleological "cause" to events that produced known results. Perhaps we shouldn't use a
causal-based framework for discussing the world.
Questions: Do you agree that causal-based framework is not the best way of discussing the
world? Is there an alternative? What about the way causation is treated in computer science,
where one speaks of states instead of causes?
Scientific
– relation to physical principles and laws, roles of equilibrium, noise, energy, chaos, entropy
etc
6. Equilibrium, causation, end energy flows
Preamble: Is a perspective that starts from a preference for stability and equilibrium in
fundamental conflict with the way the world we live in works? If there were no energy flows,
there would be no causes and no effects (because nothing would happen)
Questions: How would one relate energy flows to causality? If one presumes that all
systems of interest involve energy being provided to them from an external source, does it
make sense to attempt to understand such systems in terms of equilibrium-based modes?
How should causation be related to energy flows?
7. Role of chaos, noise and uncertainty in causation
Preamble: The notion of causation suggests reduction in uncertainty about the outcome
whereas chaos and noise suggest the opposite. In real complex systems chaotic, causal and
influence relationships coexist with sources of noise and uncertainty.
Question: Do you think these antithetical aspects of the dynamics of complex systems
should be treated independently (e.g., as simply placing computable limits on predictability?)
or can they be integrated in some way? Are there fundamental research issues to be
addressed here?
8. To change or not to change our minds
Preamble: Several leading psychologists, neurologists and cognitive scientists have
suggested that an individual agent (or a collective) can select what types of behaviour it
wishes to promote or what types of behaviour it wishes to forbid. The particular parenting
model we experience as children has a strong influence on what we regard as good or bad.
There is evidence that those of us with conservative leanings tend to adopt the strict father
model and shape their reasoning using use direct causation, whereas those with more
progressive ideas tend to adopt the nurturant parent model and think quite differently about
causation (systemic causality). Ultimately, conservative and progressive impulses may be
rooted in our evolutionary programming.
The tendency to create these kinds of
classifications, and to act at least partially on them, seems to be universal and hard-wired in
our brains. If this is true, the brain’s neural censors govern its decisions about which classes
of behaviour to enhance or suppress.
Questions: Why do some people reason using direct causation while others invoke systemic
causation? When we change our mind about something, does it change our brains? Does the
mental process of “changing one’s mind” correspond to the physical process of switching
International Collaborative Research
Program: Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks
between attractors in our brain? Would a complex systems perspective, centred on selforganizing, attractor neural networks, be helpful in ordering our thinking about how internal
psychic and neurodynamic processes lead us to reason and behave in one way or another?
9. Connectionist Models for Decision-Making
Preamble: Decision-making can be defined as an outcome of mental (cognitive) processes
leading to the selection of a course of action amongst several alternatives. Those processes
have historically been modelled in many different ways including: as following from individual
aspirations, needs and goals; as a continuous process shaped from context of environment;
or as a logical rational selection mechanism. More recently, connectionist models have
sought metaphors for the functions of the brain (for example models of neural networks).
Questions: To what extent do you hold that individual actions are shaped by their decisionmaking processes? What are the beliefs that make smart people dumb. Is any decisionmaking framework useful in identifying, diagnosing, classifying or otherwise assisting in
coming to grips with causality in complex systems?
Complexity
– emergence, phase spaces, attractors, tipping points, thresholds, multiscalarity, ‘black
swans’
10. Emergence and emergent properties.
Preamble. Emergence is a notion with many complex sides. A popular view is that emergent
properties are those novel properties that arise when parts or components assemble together
(in space and or time) into a higher hierarchic order – novel in the sense that they are not
present in the parts or components. Ryanxiii has argued that emergence is coupled to scope
rather than scale. Shalizixiv defines emergence in terms of predictive efficiency. Abbottxv
defines emergence in terms of levels of abstraction. All these approaches address the
presence of emergent properties of a complex system, rather than the process by which they
may have arisen (emerged). In other words, it seems more natural to use the concept of
emergence to refer to a class of properties rather than to refer to a class of process. Why
would that be the case? Are the various processes that produce emergent properties more
fundamental with emergence being no more than a short-hand for ‘here be dragons’? On the
other hand, some argue that emergence is a fundamental defining aspect of complex
systems. Maxent approaches for example claim that the underlying driver is the creation of
more efficient ways of dissipating free energy and that emergence is the inevitable result.
The question. How important is the role of emergence in driving the dynamics of a
developing complex system? What is the relationship between emergence and the
processes that produce the emergent properties? how should emergence be
xiii
Ryan, A.J. ‘Emergence is coupled to scope, not level’, Complexity 13(2):67-77, 2007.
Crutchfield, J.P.; Shalizi, C.R. ‘Thermodynamic depth of causal states: Objective complexity via
minimal representations’, Phys Rev E 1999, 59, 275–283.
xiv
xv
Abott, R. The Reductionist Blind Spot, Complexity (forthcoming)
http://www3.interscience.wiley.com/journal/121685801/
International Collaborative Research
Program: Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks
integrated into a causal view of a complex system? What evidence can you provide
for your views?
11. Complex Systems as integrated or loosely coupled
Preamble. Complex systems can be treated as entities or as collections of coupled but still
distinguishable subsystems, each with its own recognizable dynamics and requirements for
stability. Both views are useful but neither is sufficient. To the extent that the entity view
applies, the developmental trajectory of the system can be discussed in terms of entity level
properties, and to the extent that the coupled subsystem view works, it may be amenable to
analysing subsystem behaviours in somewhat independent terms and treating their
interactions explicitly. Wilsonxvi notes that most systems of interest—such as biological and
social systems—are simultaneously both entities and groups and that a key aspect of
complex systems is to find structures and mechanisms that enable entities to form persistent
groups.
The question. Do you see these as complementary, alternative or inadequate views
of complex systems? What do you think is the proper way to apply the notions of
interdependency versus subsystem independence, in the understanding of both the
historical and future development of a complex system? Can a different
understanding of the organization and stability of a given complex system lead to
better predictability of its future development?
12. Ecology and individuality
Preamble: A (human) ecosystem can be understood as a collection of interactions and
interdependencies which arise from the nature of the individual agents that participate in it.
Alternatively, that same ecosystem could be understood as a collective, cooperative entity,
whose structure and dynamics obey principles of their own; thereby determining the kinds of
agents that can arise and survive within the system (in the sense assumed by Darwinian
competition and selection). Certainly, ecosystems, their agents and the set of interactions
between agents co-evolve over time. Does ecology come from interacting individuality, or
does the converse apply? The direction in which we should infer causation is unclear.
Questions: What are—and how should we discover—the organizational principles that
govern collective ecology and individuality, and the directions in which causal and influence
factors or constraints flow between these two scales? In what sense is individuality a
subordinate concept to ecology, and in what sense is ecological order the subordinate
concept? {UPWARD versus DOWNWARD causation}
13. Attractors and Phase Spaces
Preamble: A nonlinear dynamical system that can be formulated in terms of differential
equations permits application of very powerful mathematical techniques such as Melnikov
theory to analyse its dynamics in terms of trajectories and attractors in its phase space. Most
real complex systems of interest are not able to be expressed in precise mathematical form.
Nevertheless it is often possible to recognise analogous properties such as thresholds,
xvi
Sober, E., & Wilson, D. S. (1998). Unto Others: The Evolution and Psychology of Unselfish Behavior.
Cambridge, MA: Harvard University Press.,
International Collaborative Research
Program: Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks
tipping points, and regions of greater likelihood which we might call ‘attractors’. In particular,
complex adaptive systems have by definition some inbuilt biases for or against particular
types of outcomes – i.e. those which their adaptive processes select for or against – which
we could therefore identify as ‘attractors’ in some sense. A similar argument could be made
for complex systems with self-organising properties, to associate their self-organised
structures with ‘attractors’ or regions of greater stability/predictability.
Questions: In the absence of a mathematical formulation of a C&IN, what utility do the
concepts of attractors, tipping points and thresholds have? To the extent that you do see
some utility to these notions, how do you think one should select the subset of C&IN
dimensions that provide useful phase space views? How might we identify those aspects of a
particular C&IN which have greater and lesser degrees of predictability? Are there ways of
analysing the topology of a phase space of a C&IN to identify regions of greater and lesser
stability? And regions of greater and lesser influencability? Do you know of any examples of
such ideas being applied in particular complex systems? When there are more than one
adaptive or self-organising processes active in a complex system, how should the effects of
the multiple simultaneous processes be combined? Do you think it may be possible to
develop a “physics” of interacting adaptive processes?
14. What Role does Interconnection Topology Play?
Preamble: There has been a great deal of work in the complex networks community (see
Boccaletti attached for an excellent review) regarding the role of the interconnection topology
in determining the synchronisation properties of complex networks. Extended work by Siljak
in large scale systems theory in the 80's and 90's also presented strong evidence of the
relationships between many areas of interconnected systems theory and their graphical
representation. There is a strong argument to be made that many more properties of complex
adaptive systems can be understood purely in terms of the graphical topology of these
systems.
Question: How much information about the causality, operation, interaction and function of a
complex adaptive system can be obtained from understanding and analysing the graphical
topological structure of a complex system? Are there limits to what the topology can tell us
about such a system? i.e. are there properties of such systems not revealed or only partially
revealed from the topology and if so what are they?
Pragmatics
– methodologies for influence, tools, techniques, guidelines, human factors, errors and
accident.
15. Finding useful subsets of complex problems
Preamble: Research into complex decision making suggests that it is not necessary to
completely understand the complex causal network underlying a complex situation to be
successful in influencing its development. In fact what potentially separates successful
decision makers in complex causal systems from unsuccessful ones is the ability to develop a
useful conceptual model of a subset of the causal network that is adequate for their decisionmaking tasks without overloading their cognitive faculties.
Question: Do you agree with these premises? Do you know of evidence to support or
contradict these views? How do you think one should go about selecting the subset of a
system to understand? Is there an optimal ‘size’ of conceptual causal network model for an
International Collaborative Research
Program: Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks
individual to apprehend and use in decision-making? What factors do you think influence
this?
16. Humans as part of a complex system
Preamble: Problems involving human action and human decision making are of particular
significance in many serious and complex global problems.
Questions: Focussing on humans as part of a complex system (that is, not as external
actors) does it make sense to attempt to discriminate the effects of human intervention/action
from the effects of inherent system dynamics? Can we identify states of the system in which
human intervention is more likely to be effective? Can we identify a relation between the
timing for human intervention and its spreading over the system at different time and spatial
scales?
17. Prodding for answers
Preamble: Trying to understand complex causal systems from the outside can be difficult,
especially if the aim is to understand the system dynamics. Often the only way to do this is to
act within the system in some way and observe the consequences. However this is turn
changes the nature of the system.
Question: Is it possible to understand a complex causal system without any kind of
intervention? Do you know of any successful intervention approaches that have been used to
understand causal networks? How can one intervene in a system and still get scientifically
solid data about the system?
18. Visualising complex systems
Preamble: Research has shown that people have difficulty understanding feedback and time
delays, which are recurrent features of complex causal systems. However studies have also
shown that at least in some cases performance can be highly dependent on the way the
problem is framed or the interface used to display the problem.
Question: Are there any “best-practice” techniques or methods for framing or displaying a
causal network so as to assist the decision making process? What room is there for
delegating some decisions to computers in areas where human decision making performance
is poor? How can interfaces be designed better to accelerate understanding complex causal
networks?
19. The Causation of Errors / Analysis of Error Causation
Preamble: The large number of hardware and software components in a computer system
can be thought of as a complex system, in that a large number of heterogeneous parts are
interacting to produce output. We can examine causality in this complex system by looking at
computer faults, and the process of tracking down the causes of these faults. In some cases
we may end up with just simple causation - for example an error in a single line of code may
cause an unexpected output from software. Often, however, there is no single cause - it may
be an interaction between software running on the computer, USB device drivers, and printer
firmware that causes a printing error. It is this causality in a web of linked components that we
call “complex causality”
Questions: What frameworks, methodologies, and tools are used to detect and analyse
faults? How well do they handle complex causality? If any of them do handle it well, how do
they go about their analysis?
International Collaborative Research
Program: Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks
20. The Causation of Accidents / Analysis of Accident Causation
Preamble: There are a large number of methodologies and frameworks for trying to identify
the causes of accidents in systems. Many of these fall into a human trap of looking for a
single cause.
Questions: To what extent do some of the more modern systems approaches capture the
fact that there may be a complex web of causal factors leading to accidents? Can you list
examples of accidents where multiple social & technical causes have been found (in
particular those where a “causal web” has been identified”), and by what methodologies they
were found?
21. Defending against Black Swansxvii
Preamble: “Black swans” are events that break a previously observed pattern (“all swans are
white”) in esssentially unpredictable ways, thus demonstrating the dangers of inductive
reasoning. While ‘black swans’ are difficult or impossible to imagine occurring (one cannot
know if a black swan exists until the first one is spotted), they can in some circumstances be
imagined. For example in sandpile, one can certainly imagine large avalanches but while it's
just hard to predict them, it's not impossible to prevent them. At every time-step one could do
a global analysis of where the dangers lie and eliminate them. (Of course one could always
level the sandpile completely at each time step. But presumably one wouldn't want that sort of
stasis either.) The problem is that such a global analysis and risk reduction work is relatively
expensive.
Questions: Has there been any effort to study, quantify or bound the predictability and
preventability of such events? What do you think is possible to do and how should we go
about it?
22. Role of AI in dealing with C&INs
Preamble: Dealing with real C&INs will require us to make use of artificial intelligence and
autonomous systems to complement and extend human capabilities so the ability of such
machines to make sense of the complexity in any particular domain becomes a critical issue if
they are going to effectively team with us.
Questions: What can we say about the kind of human-machine teaming necessary? What is
the level of what is realistically achievable? Could human teaming with such machines
actually impair our ability to perceive the complex world – eg through leading us (because of
their own inherent limitations) to only see deterministic aspects? To the extent that this
happens, how might we mitigate its impact?
23. Optimal Location of External Control and Influence
Preamble: In many large complex adaptive systems it is necessary to apply external control
or influence to achieve a given goal. Existing work in control and influence of networks
typically assumes that every element/node of the system can be controlled independently. In
many complex systems of interest, especially biological systems, it is not possible to control
each element directly but rather externalities can be applied at a limited number of locations
xvii
Taleb, N., The Black Swan: The Impact of the Highly Improbable, New York: Random House 2007
International Collaborative Research
Program: Understanding and Intervening in Systems characterised by Complex Causal and Influence Networks
in order for the whole network to perform as required. Given the magnitude and distributed
nature of many of these systems it is interesting to know how best to apply these external
influences to achieve a required goal.
Question: How do we characterise the optimal location and magnitude of external control
and influence to ensure a given complex adaptive system will function as desired? Is it
possible to characterise networks for which it is impossible to provide external control and
influence to achieve a given aim? In the case of limited external influence or control how
should the control be applied? (i.e. is it best to apply the entire control energy at a single
location? or is it better to have a larger number of smaller control sites?)
24. FINAL QUESTIONS:
Have we missed something that you think is important?
What additional questions would you like to see posed?
What are your thoughts about answering them?
PART 2 - What can we learn from disciplines that deal with real C&INs?
Preamble: Many disciplines deal with C&INs in the real world. Examples include medicine,
politics, finance, meteorology, social sciences, business, oganisational science, agriculture,
genomics, ecosystem management, cognitive science, etc.
Given that practitioners in these fields have been dealing more or less successfully with such
real-world complex C&INs for many years, they have obviously developed domain-specific
approaches that work for them, probably by taking advantage of specific features of the
C&INs of interest to them. It is reasonable to expect that some of those approaches and
concepts would be transferable to analogous complex problems in other domains, but to do
that one needs to understand why particular approaches work and under what conditions.
Questions: In your discipline, what theoretical and conceptual approaches are used to:

discover and model the C&INs operating in your systems of interest?

explain how observed properties and behaviour have come about?

think and reason about the possible consequences of observed or hypothetical states
or processes?

estimate the probablilities of various outcomes? and

explore how hypothetical properties or events might come about?
What is it about these approaches that enables them to make sense of the complex
phenomena?
What 'mechanisms' are employed for engagement and influence?
What do you think the wider community may be able to learn from this discipline?