Where is the Fastest Way Ahead
to Understand & Design
Complex Human Systems?
The Multi-Agent-based Simulation Path
R. H. Weber, Sr. P.E.
The Aerospace Corporation
(310) 336-5715
“Opposed Systems” (1)
National Utility
Utility
Enterprise Archit Mission
Military Mission Systems
Utility
Political Utility Economic Utility
Military Utility
Design
Air Offense Space Control
Operational Archit
Functional
Performance
Air & Missile Def. Dominant ManeuverLittoral Warfare
National S-A-G* Intell
* S-A-G Comm * S-A-G Navigation Missile TW/AA Space-Based KEW * S-A-G Surveill. & Recon
Product Archit
Performance
and Cost
System Program Level
Constellation
Component
Archit Design
and Cost
Utility
Payload
Spacecraft
Ground
Life Cycle Cost Availability
System Component Level
Propulsion Comm Processing Payload Software Structure Power
Life Cycle Cost ADACS Launch Ground C&DH Thermal
* S-A-G means Space--Air--Ground
(1)
Albert Wohlstetter, “Theory and Opposed-Systems Design”,
RAND Report D(L)-16001-1, August 1967
System Engineering
Mission Design Effects
National
Operations Engr.
System & Operations Engineering Reference Model
Agent-based Engineering
R. H. Weber
• H/W oriented E-O syst. engineer (USAF)
• S/W oriented MS&A (Aerospace Corp)
• Hardened 10 yr vet. of cultural battles
System
Engineering
Agent-Oriented
System Engineering
Software
Engineering
It from bit
Design of
Complex
Systems
Complexity
Science
Barriers to Progress:
• Pardon me, but your obsolete ontology is showing
• US culture of individuals, innovation, adhocracy, organized “stovepipes”
• US neglect of intellectual infrastructure
• Maladaptive effects of Cold War on military-industrial complex
Impact  Loss of US industrial/economic competitiveness at macro-system level
• EU Airbus Consortium (Catia) & Japan Toyota (kanban, kaizen, lean/agile mfg)
• GOOGLE experiment: “agent-oriented system engineering”  2 of 10 hits are US
“agent-oriented”, system, engineering, –software  3 of 10 are US
Recognizing & Overcoming Cultural Barriers
Will Rogers:
“It’s not what people don’t know that hurts them,
it’s what they know that ain’t true.”
Implies that education of government & industry managers
deserves top priority
Map of Complexity & S-o-S Engineering
THEORETICAL
EXPERIMENTAL
Network Models
• RyFragile—Doyle, Willinger
• Hierarchical
• Scale-free
• Random
• Small worlds
Multi-scale Math
• Fractal dimension
• Renormalization Group
• Algebraic Multi-grid
• Non-commuting Geometry
Dynamical Systems
• Continuous, Discrete, Hybrid
• Chaotic, Periodic, Attractor
• Turing Machine (TM)
• Interaction Machine--Wegner
Language of Simulation
• Pedagogy of time/evol.--Wilensky
• Multi-dimen. HCI--Shneiderman
• Visual Programming—Alan Kay
• Digital Philosophy—McKelvey
• Multi-scale Scenario Design
• Robocup Soccer (Grnd Chall)
• Mathematica--Wolfram
• Multi-Resol—Davis
• UML--Booch
Probability & Risk
• Fuzzy Sets/Logic—Zadeh, Kosko
• Power Laws/Fat Tails
• Epistemic Uncertainty—Taleb
COMPLEX HUMAN SYSTEMS
Architecture Design
• Codification—Alexander, Salingoros
• 4-D CAD
• Agent-Orient Rqmts Engr--Yu
• Design for Emergence
Multi Agent-based Models
• Distributed AI
• Vehicle traffic sim & control--Helbing
• Social network analysis--Carley
• Policy analysis—Cederman, Bankes
• Autocatylic behavior--Solomon
Control & Cybernetics
• Hybrid control—The Control Revolution
• Design for Robustness—Doyle, Carson
• SW failure analysis—Leveson, Dornier
• 2nd Order Cybernetics—v.Foerster
• Appl. cognitive Science—Hawkins, Peirce
• Game Theory—Pietarinen, Friedman, Camerer
• Robotics
Multi-Scale Scenario Builder
An intentional agent at the smallest tactical or individual level may perpetrate actions that are intended to have
the major effect at the strategic scale. The two WTC attacks eight years apart illustrate the difference of the
first tactical scale event having only a regional effect and the second a global effect. More commonly it
takes an integration of smaller scale effects within some moving time window to cause an effect at a larger
scale. In addition to effects between different scales, there are possibilities of effects between two players within
the same scale. Both the inter and intra-level effects can be described within the DoD paradigm of “Effects
Based Operations.” Some examples of these varieties are given below. (Strategic= S, Theater=Th, Tactical =
Ta)
•S  Ta
--Constrain movement & check ident. at borders or checkpoints on roads
--Media reports that inhibit or encourage individual violence in public
•S  Th
--Failure of a leader’s action encourages factions or coup attempts
--National oppression of ethnic or religious groups generates resistance
•S  S
--National leader stimulates change in leader of rival nation regarding policy risk or priority of resource allocation
--Nations negotiate for trade leverage (OPEC) or economic embargo (UN towards Saddam)
•Th  Ta
--Affiliation group leaders incite followers to riot or attack other groups
--Success of black market economy encourages size and number of gangs
--Coordinated attacks on utility networks increases disrespect for state
•Ta  Th
--Siphoning off resources (oil) from state increases size of black market
•Ta  Ta
--Witnesses to violence have agent state change from policy of avoid enemy to revenge
•Ta  S
--Public violence triggers govt. tighter movement constraints & violence towards public in streets
Why Agent-Oriented M&S Needed for Design?
•
a priori argument– it’s required for conceptual modeling
– When “complex system” being designed involves “humans in the loop”
for operations or processes involving that system (intention, goal
conflicts, human/soft factors & cognitive limitations)
– When “complex system” has autocatalytic or autonomous control
subsystems with discrete, multi-modal adaptive responses to
environment (hybrid control theory, behavior of ecology and of adaptive
life forms)
– When complex physical phenomena involve moderate number of
heterogeneous objects and symmetry breaking or phase change
boundaries (eg. far from equilibrium condensed matter physics)
In such cases equation-based models don’t pass face validity!
•
a posteriori argument—case history of successful design impact
– Comm & computer network management via distributed control
– Designing protocols for use of anti-biotics
M&S Needs Agents That are Discrete & Autocatalytic
“….The discrete character of the individuals turns out to be crucial for the macroscopic
behavior of complex systems….the slightest microscopic granularity insures the emergence of …
localized macroscopic collective objects with adaptive properties….
The exact mechanism by which this happens depends crucially on the other unifying concept
appearing ubiquitously in complex systems: auto-catalyticity. The dynamics of a quantity is said autocatalytic if the time variations of that quantity are proportional (via stochastic factors) to its current
value. It turns out that as a rule, the "simple" objects responsible for the emergence of most of the
complex collective objects in nature have auto-catalytic properties. Autocatalyticity insures that the
behaviour of the entire system is dominated by the elements with the highest auto-catalytic
growth rate rather than by the typical or average element. This has profound implications on the
very concept of scientific explanation: the fact that the dynamics is dominated by the exceptional
individual/events (that enjoyed fortuitously the fastest stochastic growth factor) invalidates
"reasonable" arguments based on the … 'average' or 'representative' case. This in turn
generates the conceptual gap separating...disciplines: in conditions in which only a few exceptional
individuals dominate… in the emergence of nuclei from nucleons, molecules from atoms, DNA from
simple molecules, humans from apes, there are always the un-typical cases…that carry the day.
This is the challenge of complexity: understanding the basic objects (e.g. cells) in one science
(biology) in terms of the collective dynamics of objects (molecules) belonging to another science
(chemistry). Moreover the mandate of complexity is to uncover the determinism that hides behind the
systematic and fateful recurrence in various sciences of seemingly fortuitous autocatalytic accidents.
The conceptual and practical rewards for such a trans-disciplinary effort are inestimable."
Ref: http://www.giacs.org/expertreport3
Roughly Three Regimes of Problems
Law of Large
Numbers
e ~ ( n ) ** 1/2
2) Unorganized
Complexity
(Aggregates)
Randomness
Quantity of Objects
MACRO
MESO
3) Organized
Complexity
(Systems)
MICRO
1) Organized
Simplicity
(Machines)
Law of Medium Numbers is…
Murphy’s Law
Complexity
Combinatoric
Exponential
Explosion
How many Types of Objects & Interactions?
From: G.M. Weinberg, An Introduction to General Systems Thinking,
John Wiley & Sons, New York, 1975, p 18.
Matching Analysis to Types of M&S
Law of Large
Numbers
e ~ ( n ) ** 1/2
Algorithmic, homogeneous
statistical models support this
region (eg., Boltzmann for gases
or Poisson telephone traffic)
Randomness
2) Unorganized
Complexity
(Aggregates)
3) Organized
Complexity
(Systems)
1) Organized
Simplicity
(Machines)
Equation-Based, deterministic
science & engineering models
support this region (eg., linked
spreadsheets for concurrent
engineering trades)
X
Complexity
Legacy campaign
simulations are misfit of
algorithmic, homogeneous
statistical models to
represent complex
adaptive systems
X
Multi-Agent-Based
interaction models
support this region
Combinatoric
Exponential
Explosion
From: G.M. Weinberg, An Introduction to General Systems Thinking,
John Wiley & Sons, New York, 1975, p 18.
Limits of Equations: More Specifics
“...there are also a number of quite concrete limitations to mathematical
representation….
The difficulties of such a representation fall into two complementary classes:
those caused by an unrealistic treatment of time and those resulting from an
attempt to represent multiple agency as an ordered sequence of individual
actions. These difficulties are complementary because the unrealistic
treatment of time is both a consequence and a partial cause of the unrealistic
treatment of multiple agency….
The modeler who arranges an equation system to guarantee its solubility
does so because he or she must solve it sequentially, it is not feasible for
certain processes to be carried out ``in the background'' or for the actions of
several agents to be revised at once. Thus only one agent can act at a
time in such models. Everyone else must freeze while this action is taking
place. The richness of the environment is thus restricted to suit the attention of
the modeler. This is plainly unrealistic.”
Edmund Chattoe , “Why Are We Simulating Anyway? Some Answers from Economics,”
ESRC Project L122-251-013, Nov 95
http://www.sociology.ox.ac.uk/people/chattoe.html
Equation-based Models in Social Sciences are…
…frequently the tools of charlatans.
“…in economics, and the social sciences, engineering has been the
science of misplaced and misdirected concreteness. Perhaps old
J.M. Keynes had the insight of the problem when he wrote: ‘To convert
a model into a quantitative formula is to destroy its usefulness as
an instrument of thought.’
….Marshall, Allais and Coase used the term charlatanism to describe
the concealment of a poor understanding of economics with
mathematical smoke. Philosophers of science used the designation
charlatanism in a the context of a theory that does not lend itself to
falsification (Popper) or gradual corroboration (the Bayesians).”
Against Value-at-Risk: Nassim Taleb Replies to Philippe Jorion, 1997.
http://www.fooledbyrandomness.com/jorion.html
Interactive Models more Powerful than Algorithmic
•
“The irreducibility of object behavior to that of algorithms has
radical consequences for both the theory and the practice of
computing….
•
The negative result that interaction cannot be modeled by
algorithms leads to positive principles of interactive modeling
by interface constraints that support partial descriptions of
interactive systems whose complete behavior is inherently
unspecifiable. The unspecifiability of complete behavior for
interactive systems is a computational analog of Goedel
incompleteness for the integers….
•
"Incompleteness is a key to expressing richer behavior shared
by empirical models of physics and the natural sciences.
Interaction machines have the behavioral power of empirical
systems, providing a precise characterization of empirical
computer science.”
Peter Wegner, OOPSLA'95 Tutorial
http://www.cs.brown.edu/people/pw/
Control Theory View of Conflict
Legacy M&S
(Equation- based
animation)
Controller
Coupled
Diff Eq
Numb.
of Wpns
Numb.
of Wpns
Sensor
Weight
Sensor
Factors
Weight
Plant
MOEs &
MOOs
Environ
Factor
Factors
Agent-based
Simulation
Conflict
Decentralized
Control
Sensor
Feedback
Perturbation
by Environment
Blue Goals
• Strategic
• Theater
• Tactical
Plant
Red Goals
• Strategic
• Theater
• Tactical
Sensor Feedback
MOEs &
MOOs
Math Modeling Resources (J. Doyle)
• Networks of distributed sensing, computation, comms, and
actuation will depend on all:
–
–
–
–
Thermodynamics (Carnot)
Communications (Shannon)
Control (Bode)
Computation (Turing/Gödel, P. Wegner)
Rhw amendments
• Cognition (Peirce, von Foerster, Kahneman, Schelling, Taleb, Hawkins, )
• Of these, only control addresses dynamics, latency, and
real-time issues --True only if cognition is regarded a subset of “Control”
• Claim: control must be the foundation for any network
capacity theory that deals with real time
• Focus initially on integrating comms and controls
Robustness—Fragility Trade-off (J. Doyle)
This tradeoff is a law:
 log F( x ) d
n

constant
Biological complexity is dominated by evolution of mechanisms to
more finely tune this robustness/fragility tradeoff.
a
log|S |

Robust
 log S 

Yet fragile
d    log S  d

Benefits
• attenuate disturbance
• as negative as possible
 a
Stabilizer
Costs
• amplifies high freq disturb
• as small as possible
Critique of NRC Study on Defense M,S,&A
Constructive-•
Recommendation 4: DoD should establish a comprehensive and
systematic approach for developing the MS&A capabilities to
represent network-centric operations:
– Enhance and sustain collaborations among the various parties developing
network-centric MS&A capabilities
“…the committee found little evidence of significant interaction and crossfertilization across the application communities ….collaboration might be
facilitated by a DoD-sponsored series of workshops…leading to a…report
synthesizing the views of the different communities and identifying
opportunities for cross-fertilization.”
– Continue and extend the development of existing approaches to modeling
network-centric operation.
“Since the basic architecture and functioning of traditional models
reflect a pre-network perspective on military operations, those models are
not adequate…. Attention should be given to the use of complex agents
with sizable rule sets governing behavior to provide quantitative models and
to the continued coupling of agent-based models with the techniques
of dynamic network analysis….”
– Establish a new mathematical basis for models describing network-centric
operation, drawing on an array of approaches, particularly complex, adaptive
systems research.
Critique of NRC Study on Defense M,S,&A
Misguided & Subject to Misinterpretation—
“Exploratory analysis is arguably best accomplished with a good
aggregate-level model that can cover the entire possibility
space clearly, albeit at low resolution. Such a model might have
6 to 10 variables….If one does such a synoptic exploration and
finds that only two or three of the variables are particularly
important, then with MRM or a suitable family of models, one can
zoom to higher resolution on those variables.”
How Will M-ABMs Improve System Design?
•
Ability to address & apply Wohlstetter’s “Opposed System Design
Theory” for systems with goal conflicts in quantitative simulation
•
Explicitly represent C2 system design & policy (CONOPS) factors &
integrate with physical system engineering
•
Factor in aspects of near real-time situation awareness in context of
scenarios with asymmetric, adaptive opponents
•
Allows Exploratory Analysis as a form of “stochastic engineering” (see
N. Taleb in The Edge World Question Center) to produce more
sustainable/adaptable systems & address the “Robust yet Fragile”
conundrum at lower cost by showing how far adaptive C2 will allow
relaxing constraints or MOP levels on other high-cost system elements
(eg., comm bandwidth)
Nassim Taleb’s Vision for “Stochastic Science”
“Rigorous reasoning applies less to the planning than to the selection of what works.
I also call these discoveries positive "Black Swans": you can't predict them but you
know where they can come from and you know how they will affect you. My optimism
in these domains comes from both the continuous increase in the rate of trial and
error and the increase in uncertainty and general unpredictability.
The world is giving us more "cheap options", and options benefit principally from
uncertainty…. But if the success rate is very low, the more we search, the more likely
we are to find things "by accident", outside the original plan — or the more an
unspecified original "plan" is likely to succeed…. I see the sign of fractal randomness
in these payoffs from the fact that results are more linear to the number of
investments than they are to quantities invested — thus favoring the multiplication of
small bets.
All the while institutional science is largely driven by causal certainties, or the illusion
of the ability to grasp these certainties; stochastic tinkering does not have easy
acceptance. Yet we are increasingly learning to practice it without knowing — thanks
to overconfident entrepreneurs, naive investors, greedy investment bankers, and
aggressive venture capitalists brought together by the free-market system. I am also
optimistic that the academy is losing its power and ability to put knowledge in
straightjackets and more out-of-the-box knowledge will be generated Wiki-style.”
Nassim Taleb, “The Birth of Stochastic Science”, in The Edge World Question Center, 2007
http://edge.org/q2007/q07_5.html#taleb
What this Workshop Can Do
Microscale
• Instigate new interactions & persistent collaboration among individuals
Mesoscale
• Provide new guidance for Community of Practice
• Share materials to educate management on Complexity Science via
website
• Propose new collaborative R&D projects (eg. )
• Plan successor workshop events with narrower focus that varies
annually
Macroscale
• Platform for organizing response to Europe’s GIACS (General
Integration of the Applications of Complexity in Science) & growing a
global collaboration language for design of Complex Systems
• Propose professional society recommendation for Chief Simulation
Officer of each engineering corporation above 1000 employees
• Follow-up on the more compelling recommendations of NRC Report:
Defense Modeling, Simulation & Analysis: Meeting the Challenge
Second Mover Contribution
• First movers generally sacrifice peripheral vision in favor of
focus & drive
• Building intellectual capital & infrastructure for architecture
design involves collaboration & integration of “best of breed”
concepts & language that gain dominant “mindshare” of the
technical community (eg., why we use Leibnitz notation for
calculus rather than Newton’s & VHS rather than Betamax
format for video tape)
• Those who follow also serve…
Backups
Overcoming Fear of Modeling: 3 Stages
• Each has own undocumented
or unconscious “mental models”
- Concept design arguments based on often
conflicting assumptions
Barrier 1
no context for resolving conflict
• Each has own software models
- Quantitative outputs based on assumptions &
algorithms invisible to all but model developer
Barrier 2
no std models--little context for resolving conflict
• Analysts & Decisionmakers use common
software models
- Shared experience of running models with
assumptions, algorithms & data bases visible to all
Co-evolution (re: Brooks Turing Lecture-99)
• Model of co-evolution from Maher & Cross
• The effective problem space evolves
as the solution space evolves by being explored.
P1
P2
PROBLEM THREAD
S1
SOLUTION THREAD
S2
“Outside-In” Mental Modeling
By far the most common way to deal with something new is by trying to
relate the novelty to what is familiar…: we think in terms of analogies and
metaphors.
The only feasible way of coming to grips with really radical novelty is
orthogonal to the common way of understanding: it consists in
consciously trying not to relate the phenomenon to what is familiar
from one’s accidental past, but approach it with a blank mind and to
appreciate it for its internal structure.
The latter way of understanding is far less popular that the former one, as it
requires hard thinking. (And as Bertrand Russell has pointed out, “Many
people would sooner die than think—in fact they do.”) It is beyond the
abilities of those—and they form the majority—for whom continuous
evolution is the only paradigm of history: unable to cope with
discontinuity, they cannot see it and will deny it when faced with it.”
Edsger W. Dijkstra, Mathematicians & Computing Scientists: The Cultural Gap,
ABACUS, vol. 4 no. 4, Summer 1987.
Interactionist Approach to Architecture & Design
“When computer chips outnumber humans on this earth…their mediation can
fundamentally alter how people interact. Engineers, psychologists, ethnographers,
architects, and cultural geographers have only begun to grasp the consequences of
all this mediation….Much of what has passed for design has been an
unconstrained accumulation of features, or at best, interfaces for measurable firsttime usability. The new field of interaction design raises this work to a cultural level. As
the study not only of how people deal with technology, but also how people deal with
each other through technology, interaction design brings notions of premise,
appropriateness, and appreciation to the conception of digital systems. The
more that pervasive computing challenges designers to bring such notions to physical
contexts, the more interaction design shares with architecture…. pervasive computing
challenges us to re-express all that we value most about embodiment in persistent
structures….Now architecture incorporates interactivity; and increasingly,
interaction design affects architectural experience.
Malcolm McCullough, Visiting Associate Professor, School of Architecture & School of Design,
Carnegie Mellon University
http://www-personal.umich.edu/~mmmc/#
Interactionist approach to Design is another trend supporting multi-agent based simulation
a priori vs a posteriori Aggregation
• System Dynamics Models (SDM)--mature, best for pure physics with homogeneous
elements
– Uses equations which represent observables averaged over time & space
– Architecture follows equations & simplest way to model flow rates & levels
– No explicit model of spatial relationships with ODE, can do with PDEs but then no
way to differentiate between physical space & network topology
– Not good for behavioral discontinuities
– Assumes homogeneity at the entity level
– Single level of aggregated detail & validation, no “generative” or “atomic” behavior
• Multi Agent-Based Model (MA-BM)--new, best for cases of heterogeneous elements
and human C2/policy issues
– Begins with object/agent behavior rules governing interactions and aggregate
observables “emerge” (multi-resolution model)
– Natural modularity follows the types of objects (real world analog)
– Can distinguish between physical space & interaction topology
– Handles large heterogeneity of objects
– Behavioral validation at both object and aggregate levels
Refs: http://www.erim.org/cec/projects/dasch.htm
Attributes of Major M&S Types
System Dynamics Models (SDM/EBM)
--Macro/aggregate observables
generated by equations
Multi-Agent-Based Models (M-ABM)
--Agent states change via local interaction rules
( includes but not limited to equations )
NON-Isomorphic Model
MACRO
INPUT
Partially Isomorphic Model
MICRO
INPUT
Equations
Population of AGENTS
& Interaction Rules
MACRO
OUTPUT
aggregate data (m1 , m2 , …)t1 , t2 ,
…
Output agent histories (s1 , s2 , …)t1 , t2 , …
MACRO
OUTPUT
Data Analysis of Aggregate Observables
limited to theory implied by generative equations
Descriptive only--does not allow
“emergent” effects nor help understand
their causal mechanism
Analyst aggregates micro-level, Agent
Observables into macro-level Populations
Possible to discover of macro-level Effects
which can be explained by micro-level Causes
Parunak, Savit & Riolo. “Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users’
Guide,” presented at Modeling Agent Based Systems, 1998. http://www.erim.org/~vparunak/papers.htm
Simulations as Generative or Descriptive
“Instead of being restricted to representing mathematical
models of social processes,
there is no reason why simulation should not enable us to
represent the processes themselves.
It seems appropriate to refer to simulations of this sort as
generative and contrast them with the process of instrumental
simulation discussed at the beginning of this section.”
Edmund Chattoe , “Why Are We Simulating Anyway? Some Answers from
Economics,” ESRC Project L122-251-013, Nov 95
MultiResolution
Science Limited by Infrastructure of Eq.s
Q: “What’s the Story behind this new kind of science?”
A: “Around 1980, I had become interested in several really different
questions—galaxy formation and how brains work….the real problem was with
the basic infrastructure of science. For about 300 years, most of science has
been dominated by…using mathematical equations to model nature. That
worked really well for Newton…but it’s never really worked with more
complicated phenomena in physics….in biology it’s been pretty hopeless.”
Q: “If equations aren’t the right infrastructure for modeling the world,
what is?”
A: “Simple programs….systems in nature had better follow definite rules. But
why should those rules be based on the constructs of human
mathematics?….now you can think of them as being like computer
programs.”
Stephen Wolfram, Interview in “New Scientist.com”
http://www.newscientist.com/opinion/opinterview.jsp?id=ns230516
Open questions (J. Doyle)
Nonlinear/uncertain
hybrid/stochastic etc.
Single
Agent
?
?
Complexity
of
dynamics
Complex
networked
systems
Flocking/synchronization
consensus
Multi-agent
systems
Complexity
of interconnection
Control Model Integrates Bode & Shannon (J. Doyle)
-
e=d-u
Plant
u
CC
Control
Channel
Control
 log S  d  log(a) 

d
Disturbance
d
Remote
Sensor
CS
Sensor
Channel
CC
r
Encode
Should also include data
fusion/cognition--rhw
 log S  d CS

Nuno C Martins and Munther A Dahleh, Feedback Control in the Presence of Noisy Channels: “Bode-Like” Fundamental
Limitations of Performance.
(Submitted to the IEEE Transactions on Automatic Control) Abridged version in ACC 2005
Fundamental Limitations of Disturbance Attenuation in the Presence of Side Information
Nuno C. Martins, Munther A. Dahleh and John C. Doyle
http://www.glue.umd.edu/~nmartins/
(Submitted to the IEEE Transactions on Automatic Control) Abridged version in CDC 2005
Van Riper LGen (ret) Msg to CJCS Dec 2005
"Systems can be complex based on the numbers of elements they
have: the greater the number of elements, the greater the
complexity. This is structural complexity. Systems can also be
complex in the ways that their elements interact: the greater the
degrees of freedom of each element, the greater the complexity….Of the
two, the latter can generate greater levels of complexity -- by orders
of magnitude….
• Within interactively complex systems it is usually extremely
difficult, if not impossible, to isolate individual cases and their
effects….
• Reductive analysis will not work with such systems: the very act of
decomposing the system changes the dynamics of the system…
• Most social systems, such as economies, governments, diplomacy,
culture, and war, exhibit rich interactive complexity."