Applications of Complex Systems

to Operational Design Alex J. Ryan

Booz Allen Hamilton

alex.ryan@gmail.com

Simon distinguished between the declarative logic of the natural sciences, concerned with

how things are, and the normative logic of design, which is concerned with how things ought

to be [Simon 1996]. According to Simon, everyone who devises courses of action aimed at

changing existing situations into preferred ones is engaged in designing. Where traditionally

design was associated with giving form to tangible artifacts, this broader conception recognizes

that design also applies to more abstract entities. Services, interfaces, networks, projects, and

even discourses are legitimate subjects for design [Krippendorff 2006]. The topic of this paper

is the design of the operations of an organization, which compared to traditional design

concerns in architecture and industrial design, are fundamentally fluid, dynamic, and open

systems. Consequently, complex systems theory contains important insights for operational

design. This paper uses examples from military operations to illustrate complex systems

applications to operational design. We show how an appreciation of complex systems has been

captured in U.S. Army doctrine on design, leading to the most significant change in the Army

operations process in over a generation. We then identify further opportunities to build on this

success within the Army. The implications are quite general, and apply to the design of

business and interagency operations.

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1 Introduction

Much of the complex systems literature is concerned with the science of complex

systems. We know quite a lot about the collective behavior of complex systems, such

as social insect colonies, gene regulatory networks, and social networks. We know

rather less about how these insights apply to the improvement of social systems.

Questions of improvement are inherently systemic: if improvement is assessed only

with respect to the part, there is no guarantee that local improvement will not make

the whole system worse [Churchman 1968]. Questions of improvement are also

questions of design: they conform to a normative rather than a declarative logic

[Simon 1996].

The earliest theories of design were developed by architects. Research into design

methodology as a broader topic common to architects, engineers, industrial designers,

and urban planning is often traced back to the first Conference on Design Methods in

London in 1962 [Cross 2006]. Early models of the design process were elegant,

logical, and bore little if any resemblance to what designers actually did [Lawson

2006]. Consequently, design research in the 1970s and 1980s moved away from

building rational models of the design process to develop participatory methods of

design, and to articulate the unique contributions of designers in situations involving

uncertainty, instability, uniqueness and value conflict [Cross 2006]. This helped

establish design as a discipline, and encouraged its application in new and more

abstract fields. Krippendorff traces a trajectory of the expanding applicability of

design from products to services, interfaces, networks, projects, and discourses

[Krippendorff 2006]. Design is now often portrayed as a broad approach to

understanding and transforming human situations that blends analytic and intuitive

thinking [Martin 2009].

In this paper, we will be concerned with the subject of operational design; that is,

the design of actions taken by an organization to improve a problematic situation

experienced by a social group. Frank Lloyd Wright famously observed that a doctor

can bury his mistakes but an architect can only advise his client to plant vines.

Wright’s observation underscores just how little influence an architect has on a

building after its construction. Although this leaves architects open to criticism for

their mistakes or lack of foresight, it also permits a very useful simplification. In most

circumstances, the architect can approximate their environment as a closed system. In

contrast, an operational designer establishes an ongoing relationship with the affected

social group. The interactions of an operational designer are iterative and exhibit a

greater degree of reversibility than the design decisions of an architect. The

operational designer is part of the social system they are designing, and must

conceive of their environment as open to flows of energy, matter, and information.

Operational design thus conceived is a problem of complex systems design. The

purpose of this paper is to explore this intersection between the interdisciplinary

fields of complex systems and design. We will draw on examples of operational

design from the military domain. Western militaries have developed a number of

sophisticated approaches to operational design in the last decade in response to the

complex challenges of contemporary operations. Section 2 examines the relationship

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between complexity and military operations. Section 3 describes concepts for

operational design developed by Australian, Israeli, and U.S. Armies over the past

decade. Section 4 identifies opportunities for incorporating further insights from

complex systems into operational design. Section 5 concludes with broader

implications for the design of other governmental agency and commercial operations.

2 Complexity and Military Operations

2.1 Complexity is an enduring feature of military operations

…in war, as in life generally, all parts of the whole are interconnected and thus the effects

produced, however small their cause, must influence all subsequent military operations and

modify their outcome to some degree, however slight. In the same way, every means must

influence even the ultimate purpose.

―Carl von Clausewitz [1984]

Clausewitz’s influence on Western military thinking has ensured that Western

militaries, and particularly land forces, have been self-aware of the sheer complexity

of war since On War was published in 1832. Clausewitz [1984] insisted that war does

not follow its own logic, but is an instrument of policy. As such, no military action

can be evaluated in isolation, and has meaning only within its political context.

Restated in modern terms, war is an open system, and the information relevant to its

description is as unbounded as the political system that generates the conflict.

Clausewitz also appreciated the nonlinearity of war. He described the role of

interaction, friction, and chance on the battlefield. Beyerchen [1992] showed that

these concepts align with key concepts from nonlinear science, including positive

feedback, instability, entropy, and chaos. According to Beyerchen, Clausewitz

“perceived and articulated the nature of war as an energy-consuming phenomenon

involving competing and interactive factors, attention to which reveals a messy mix

of order and unpredictability” [Beyerchen 1992].

2.2 Complex systems theory influences modern operational art

While it is arguable that Clausewitz anticipated some developments within the

nonlinear sciences, the science of his time was of course Newtonian. In 1996 and

1997, the U.S. Marine Corps updated their key doctrinal publications to describe the

nature of war using complex systems concepts. The U.S. Marine Corps also funded a

nine year program of international workshops for complex systems modeling, named

Project Albert (see for example [Hoffman & Horne 1998]).

The U.S. Army’s School of Advanced Military Studies (SAMS) began teaching

complex systems theory at about the same time it was introduced into U.S. Marine

Corps doctrine (see for example [Schneider 1996]). SAMS was established in 1984 as

a graduate school to educate military officers on the operational level of war. The

acknowledgement of an operational level of war linking strategic goals with tactical

action had been introduced in U.S. Army doctrine (known as AirLand Battle) two

years earlier [United States Army 1982].

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The operational level was based on the theory of operational art developed by the

Soviet military theorists, especially Svechin and Tukhachevsky, between the two

world wars. Napoleonic warfare between 1805-1815 was seen as ideally consisting of

a single decisive battle, such as Napoleon’s victory at Austerlitz: short, sharp, and

achieving a strategic outcome [Epstein 1992]. The Soviet theorists recognized that

decisive battle in modern industrial warfare was unlikely, and seeking it could lead to

the trench warfare stalemate of World War I. To restore movement on the battlefield,

the Soviets introduced operational art, which integrated multiple parallel and

sequential tactical actions to achieve a strategic goal.

Operational art involves the synthesis of a complex purposeful whole composed

of distributed and dynamic components. More simply, operational art designs a

system. The motivation for the rediscovery of operational art in the U.S. included

Vietnam and the Cold War. The AirLand Battle doctrine writers were part of the

Vietnam reform movement. Among other issues, in Vietnam there was a failure to

convert tactical success into strategic progress. At the same time, the Cold War

context involved planning for conflict on a large scale. The maneuver of large

formations required the grouping of battles and operations into campaigns in order to

match the Soviets’ ability to sequence operations over the full depth of the battlefield.

SAMS was at the center of the introduction of operational art into U.S. Army theory,

doctrine, and education. Consequently, complex systems theory had immediate

relevance to the operational level of war, as well as to theoretical research at SAMS.

Outside the U.S., in 1994 Israeli Brigadier General Or initiated a Special Task

Force to investigate a perceived lack of generalship within the Israeli Defense Force

(IDF). The review resulted in the founding of the Operational Theory Research

Institute (OTRI) in 1996 under the leadership of Brigadier General (Res.) Tamari and

Brigadier General (Res.) Naveh. Naveh’s research into Soviet operational art

connected Tukhachevsky’s Deep Operations theory with its roots in systems theory.

In comparing Russian definitions of operations with von Bertalanffy’s concept of the

universal system, Naveh concluded that “one can rightly claim that the operational

level is the implementation of the universal system in the military sphere” [Naveh

1997]. Additionally, whereas Clausewitz advocated defeating the enemy by the

physical destruction of their source of power, Tukhachevsky advanced a theory for

disintegrating enemy forces through operational shock. Deep operations involved

simultaneous disruption of the enemy’s configuration over its entire depth,

integrating physical strike, cognitive surprise and deception, and momentum

(operational tempo and density) [Naveh 1997]. In addition to the classical Soviet

operational art, U.S. developments in operational art were taught at OTRI. The

influence of complex systems could be found in OTRI’s application of swarming and

network theories to urban operations [Weizman 2006a].

The Australian Army also made important contributions to the application of

complex systems theory to military operations. Within the Future Land Warfare

Branch of Australian Army Headquarters, Brigadier Kelly and Lieutenant Colonel

Kilcullen led the development of Complex Warfighting [Australian Army

Headquarters 2004], the capstone future operating concept for the Australian Army.

Complex Warfighting characterized the future operating environment as complex,

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diverse, diffuse, and lethal, describing a conflict ecosystem with an increasing variety

of state and non-state actors. In response to the challenges of complex warfare, the

future operating concept envisaged small, highly autonomous, modular combined

arms teams, capable of swarming and reconfiguring in different ways.

3 Military Responses to Complexity

3.1 The natural response to complexity is adaptation

In 2006, the publication of Adaptive Campaigning [Australian Army Headquarters

2006] built on the framework of Complex Warfighting to recognize that the

appropriate response to complexity is adaptation. Adaptive Campaigning introduced

the concept of operating across five simultaneous, interdependent and mutually

reinforcing lines of operation. The purpose was to recognize that traditional military

combat actions alone could not be decisive in complex warfighting: to cause an

attractor change to a new stable society, multiple concurrent actions are required.

Adaptive Campaigning described an adaptive cycle for operations consisting of four

steps: act, sense, decide, adapt.

Adaptive Campaigning provided the Australian Army with a new framework for

campaign planning, which was immediately tested in Iraq and Afghanistan. The

commander of the first Reconstruction Task Force (RTF), Lieutenant Colonel Ryan,

explained how this new approach was applied. “Possessing this „human map‟,

continually updated using complex adaptive systems theory, equipped the

commanders and staff with insights into the dynamics of the province and the ability

to assess the impact of RTF projects on the various local actors” [Ryan 2007]. In

addition to enhancing the RTF‟s operational adaptation, the concepts from Adaptive

Campaigning were used to implement counter-adaptation operations against the

Taliban, aimed at disrupting their ability to adapt.

The commander of the RTF 3, Lieutenant Colonel David Wainwright, also the

lead author of Adaptive Campaigning, applied complex systems theory to military

operations within the framework of Adaptive Campaigning. The campaign plan

developed six interdependent lines of simultaneous and sequential operations to build

indigenous Afghan capacity towards self-reliance (see Figure 1 for a graphical

reconstruction of the campaign plan). The headquarters developed an adaptation

cycle to implement Wainwright‟s concept for analysis-led reconstruction operations.

In a recent study of the Australian Army‟s application of Adaptive Campaigning

during RTF 3 in Afghanistan and Overwatch Battle Group (West) 1 in Iraq, Major

Bassingthwaighte concluded that “…using Adaptive Campaigning as a framework in

context with a particular situation and mission results in a workable campaign plan

that can guide tactical action that supports the strategic objective” [Bassingthwaighte

2011]. Bassingthwaighte cited examples during Operation Spin Ghar in Afghanistan

and during an incident at the Al Khidr Police Service in Iraq where the campaign plan

provided the rationale for adapting tactial actions in a changing environment to

improve coherence with the strategic goal [Ibid].

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Figure 1. Campaign plan for the operations of RTF 3, courtesy of [Bassingthwaighte 2011].

In 2008, the Chief of the Australian Army, Lieutenant General Ken Gillespie,

launched the Adaptive Army initiative, which included the biggest restructuring of

the Army since 1973 [Australian Army Headquarters 2008]. The objective was

“…improving the quality and timeliness of information flows throughout Army in

order to enhance Army‟s adaptation mechanisms at all levels. [Ibid]” The Army was

reorganized into two Commands responsible for adapting at different time scales.

This extended the application of complex systems principles from operational design

to organizational design, and provided a better fit between the operations and

structures of the Australian Army for complex operations.

3.2 Reframing enables a deeper form of adaptation

Since its inception, Israel‟s OTRI had been developing a novel approach to

operational design. Naveh‟s theory of Systemic Operational Design (SOD) presented

a fundamental challenge to the prevailing paradigm of Western military theory,

which he characterized as driven by the industrialization of war, obeying a linear

order and binary logic [Naveh 2005]. Naveh emphasized a form of adaptation that

required reframing the operational artist‟s theory of reality in order to create new

forms of maneuver. This new way of thinking, first outlined by the Special Task

Force in 1994 [Naveh 2007a], was tested in 2002 when Brigadier General Kokhavi,

commander of the Israeli Defence Force 35 Paratrooper Brigade and graduate of

OTRI, designed the Nablus and Balata components of Operation Defensive Shield.

The following remark during Kokhavi‟s war council provides an illustration of how

the brigade reframed its thinking about urban operations. The prevailing maneuver paradigm is about geometrical order, physical cohesion, and

massed firepower. Its conceptual coherence is embodied in its formal simplicity.

Moreover, since the similar patterns of space are being utilized by the competing

symmetric contenders, the rationale of emerging operations is deterministic and the

problem of self-orientation, both geographically and cognitively, by individual tactical

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commanders is a minor challenge. Once we shift from modes of action based on presence

to modes of action based on disappearance, and from maneuver framework reflecting

Euclidean geometry to maneuver framework reflecting geometry of complexity we

magnify the space for exploiting potential, yet at the same time we pull the cognitive

challenges for warfighters to new extremes. Since every unit commander is an autarkic

fractal component within an emerging fractal system, the cognitive problem of self-

orientation becomes three fold. First one has to refer, at every moment of the evolving

operation his relative position to the geography. Second, one has to refer, at every moment

of the evolving operation, his relative position to sister units functioning within the

relevant operational space. And, thirdly, one has to draw, at every moment of the evolving

operation, the systemic implications from his positioning in relation to the logic of the

emerging maneuver as a whole. The first is about navigation, the second is about

orientation, and the third is about systemic awareness. I mean awareness not in the sense of

recent American clichés but a cognitive quality implying synthesis. Therefore, we need to

prepare navigation aids, to invest in developing common spaces of understanding in the

fighting units, and to design a command architecture enabling dynamic learning in action

[Naveh 2005].

Kokhavi describes how his infestation strategy “inverted the geometry” of the urban

space in order to walk through walls: …this space that you look at, this room that you look at, is nothing but your interpretation

of it. […] The question is how do you interpret the alley? […] We interpreted the alley as a

place forbidden to walk through and the door as a place forbidden to pass through, and the

window as a place forbidden to look through, because a weapon awaits us in the alley, and

a booby trap awaits us behind the doors. This is because the enemy interprets space in a

traditional, classical manner, and I do not want to obey this interpretation and fall into his

traps. […] I want to surprise him! This is the essence of war. I need to win […] This is why

that we opted for the methodology of moving through walls… Like a worm that eats its

way forward, emerging at points and then disappearing. […] I said to my troops, “Friends!

[…] If until now you were used to move along roads and sidewalks, forget it! From now on

we all walk through walls!” [Weizman 2006b]

While the tactic of entering buildings through holes blasted in the walls rather than

through doors or windows is common in urban operations, the novelty of Kokhavi‟s

design was in the scale on which this occurred, and the decentralized command and

control architecture for the infestation operation.

Further applications of SOD in Israel included Kokhavi‟s design for the

evacuation of settlements in Gaza in 2005, and the Northern Storm contingency for a

campaign against Hezbollah, which was designed between 2002-2005 under the

leadership of Major General Gantz [Naveh 2010]. Beginning in 2003, the IDF

rewrote their doctrine, the core of which was inspired by SOD. The new doctrine was

officially published in April 2006, just before the July 2006 Israel-Hezbollah war.

Following the failure of the IDF to meet any of their strategic objectives during the

war, there was a rush to assign blame to the politicians, the Chief of Staff, the Air

Force, the Army, as well as the way the war was planned, communicated, and

executed [The Economist 2006]. The new doctrine was criticized as being “too

complicated, vain, and could not be understood by the thousands of officers that

needed to carry it out” [Matthews 2008]. U.S. historian Matt Matthews drew the

following conclusion from his study of the 2006 war. Shimon Naveh‟s SOD has come under much criticism for being nearly incomprehensible

to those who were charged with its implementation. The core of SOD may not be without

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merit, but it is useless if it cannot be understood by officers attempting to carry out

operation orders using SOD terminology and methodology.

Naveh acknowledged that SOD was difficult to understand, but that “…wars are very

hard to fight and yet we go and fight them…. If you‟re serious about your profession,

then you‟ll go through it” [Naveh 2007b]. He noted that the generals who

orchestrated the 2006 “ill-conceived and poorly executed campaign” [Naveh 2007a]

were not advocates of the OTRI approach but those who, in the words of Dov

Tamari, had been “seduced by the belief that fighting a war can eliminate a threat”

[The Economist 2006]. Certainly, the air-dominated “effects-based operations”

approach that the IDF initiated the conflict with was fundamentally different to the

SOD strategy articulated in the Northern Storm contingency campaign design.

Nevertheless, Matthews‟ warning about the potential for complicated language to

reduce the clarity of military orders was persuasive, particularly in the U.S.

3.3 A simplified design methodology

The U.S. Army Capabilities Integration Center1 (ARCIC) introduced concepts

mirroring the Australian and Israeli responses to complex warfighting to the U.S.

Army. In 2005, the U.S. Army was re-examining its approach to the Iraq war and

searching for fresh thinking. SAMS students coached by Naveh successfully used

SOD to formulate a creative approach to campaign design within the Unified Quest

wargame. Following a five-part Senior Leader Forum on operational art and design,

ARCIC published a pamphlet Commander’s Appreciation and Campaign Design,

which asserted that the “…study of interactively complex systems must be systemic

rather than reductionist, and qualitative rather than quantitative, and must use

different heuristic approaches rather than analytical problem solving” [Department of

the Army 2008]. The pamphlet distinguished between the cognitive functions of

design as an application of operational art to problem framing, and the cognitive

functions of detailed planning as a more procedural approach to problem solving.

While continuing to support the Unified Quest wargames, SAMS developed a

curriculum to educate design, and began to formalize the design methodology

[School of Advanced Military Studies 2008]. The SAMS curriculum included a

significant component of systems thinking and complex systems theory, and the

SAMS student text on design noted: “Complex systems provides an understanding of

the sources of complexity, as well as new approaches to coping with complexity by

becoming more adaptive. Designers can leverage the complex systems approach to

deal with complexity over time” [Ryan et al. 2010].

The influence of Australia‟s Adaptive Campaigning is evident in the U.S. Army‟s

Capstone Concept, Operational Adaptability [Department of the Army 2009]. The

central idea of the capstone concept, operational adaptability, explicitly links

complexity, adaptability, and operational design through idea of developing the

situation through action (called „Adaptive Action‟ in Adaptive Campaigning): Develop the situation through action. Because technology cannot deliver everything that

leaders and units must learn about the environment and enemy organizations, Army forces

must be prepared to develop the situation through action…. Because of the complexity of

1 The U.S. Army Capabilities and Integration Center was previously U.S. Army

Training and Doctrine Command Futures Center until it was renamed in 2006.

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the environment and the continuous interaction with adaptive enemies, understanding in

armed conflict will never be complete…. Leaders must be adept at applying design as a

methodology for framing problems [Department of the Army 2009]. This concept of developing the situation through action – stimulating the system in

order to learn as well as to transform – was a significant departure from the previous

Army Capstone Concept published in 2005. The 2005 version also acknowledged

complexity, but proposed that the solution would be found in “full spectrum

dominance,” “decisive operations,” and “Network-Enabled Battle Command,” which

attempted to control complexity and uncertainty through the use of information and

communications technology. The director of ARCIC, General McMaster, noted that

the biggest change to the Capstone Concept was “recognizing some of the limitations

in technologies that were designed to improve situational understanding and

situational awareness” [Harlow 2009].

In 2010, the U.S. Army updated its Field Manual on the Operations Process, FM

5-0, to include a chapter on the design methodology. While the underlying ideas were

derived from Naveh‟s SOD, the U.S. Army design methodology considerably

simplified the language of design, and integrated design within the Army operations

process. It defined design as “…a methodology for applying critical and creative

thinking to understand, visualize, and describe complex, ill-structured problems and

develop approaches to solve them” [United States 2010]. The design methodology

illustrated in Figure 2 was first described in [Banach and Ryan 2009]. The

methodology iterates between three cognitive spaces focused on understanding the

environment, the problem(s), and potential solutions. The following four paragraphs

briefly describe these three cognitive spaces and the significance of reframing in

design.

Figure 2. Army design methodology.

In the environmental space, designers frame the environment by consciously

choosing the perspective from which a complex situation will be made sense of.

Designers review, and if necessary reframe the guidance they are given by higher

authorities, and map out the dynamic and interactions and relationships between

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relevant actors within a social network. The environmental frame also considers the

dynamics of the system: the history, current state, and future goals of the actors

within their cultural context, to appreciate systemic tendencies and potentials for

change. The recognition of potential enables designers to construct a desired end

state, which articulates a set of conditions that are within the potential of the system.

In the problem space, designers focus on the difference between the current and

desired systems. Problem framing involves constructing three systems. The system of

opposition is a collective of agents that will actively oppose the transformation

towards the desired attractor. The system of support is a collective of actors that will

work together to achieve the desired attractor. The system of energy is the set of

resources that flow through the system, enabling both the system of support and the

system of opposition. Both the system of opposition and system of support are

conceived broadly: often, parts of our own organization work against the desired

transformation, and can be considered part of the system of opposition. Appreciating

the rationale of those who oppose us provides an external perspective for evaluating

our own strategic logic. The problem frame expresses the set of problems the

designers will address in terms of the underlying tensions within the system and the

additional tensions created by transformative change.

In the solution space, designers develop an operational approach to resolve the set

of problems surfaced in the problem frame. The operational approach should be an

integrative solution that addresses the underlying causes and tensions identified

within the problem frame. The operational approach gives tangible form to the

strategic logic for transformation. This connects theory with practice. In addition to

articulating the pattern of simultaneous and sequential actions to transform the

system, the operational approach also specifies how the designers will continue to

learn about the system.

Reframing ensures that the design methodology is adaptive. Because war is an

open system, any “solution” is assumed to be temporary in nature. To account for this

and avoid confirmation bias, designers continually challenge the relevance of their

understanding of the environment and the problem(s). When novel patterns emerge

that erode the explanatory value of the current design, the designers must reframe

their understanding of the problem, which leads to new solutions. Consequently,

design is iterative and continuous throughout an operation, and is integrated with

planning, preparing, executing and assessing the operation.

A notable application of design in the U.S. by Captain Porter and Colonel

Mykleby (a student of Naveh) proposed a new grand strategic narrative for the U.S.

“In one sentence, the strategic narrative of the United States in the 21st century is that

we want to become the strongest competitor and most influential player in a deeply

inter-connected global system, which requires that we invest less in defense and more

in sustainable prosperity and the tools of effective global engagement” [Mr. Y 2011].

The design, developed from within the Pentagon and published under the pseudonym

Mr. Y, reframed the strategic narrative from control in a closed system to credible

influence in an open system. This logic then argued for the replacement of Kennan‟s

containment theory (first published under the pseudonym X) with sustainment:

credible global influence based on internally sustainable development. The authors

have initiated grass roots sustainment movements in counties across the U.S. to grow

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bottom-up solutions to national strategy challenges identified in their strategic

narrative.

4 Opportunities for Harnessing Complexity

4.1 Complex systems techniques are applicable throughout design

We have seen that significant insights from complex systems theory have already

been incorporated into Australian, Israeli, and American approaches to operational

design. The growing awareness of the utility of complex systems theory, not just for

describing complexity, but for transforming systems in the presence of complexity,

opens the door for the application of further complex systems techniques to

operational design.

According to the U.S. Army design methodology, designers strive to transform

existing conditions towards a desired end state. In a dynamical system,

transformation requires a change in attractor. From a complex systems perspective,

designers should not aim for an end state (since end implies finality, state implies

static, and „end state‟ implies reverse planning from a point target). Rather, designers

should seek transformation by moving the system‟s state out of the current basin of

attraction to within the basin of attraction of a more desirable attractor. In this

idealization, tactical action is an injection of energy away from the current attractor,

while strategy alters the manifold of the dynamical system. Strategy affects the

underlying network of positive and negative feedback loops to create new desirable

attractors, deepen the wells of existing desirable attractors, reduce the stability of

undesired attractors, and create repellers to steer the system‟s state away from

undesired regions of phase space. Operational art involves aligning strategic

manipulation of the context with focused tactical action to push the state of the

system across the threshold separating an undesirable basin from a more desirable

basin. Dynamical systems theory provides a richer conceptual framework for

constructing attractors rather than end states and transforming the current state

towards a more desired pattern of behavior.

4.2 Techniques for modeling complex social systems

During environmental framing, complex systems theory enables designers to

appreciate variety, and leverage sources of variation within the system to improve

adaptability. As Axelrod and Cohen observe, “[t]he Complex Adaptive Systems

approach, with its premise that agents are diverse, is well suited to design projects…

It builds in the default assumption that there is variety within a population that could

matter” [Axelrod and Cohen 2000]. As well as variety, complex systems emphasizes

bottom-up and informal sources of order within the system. For example, the success

of “the surge” in Iraq was due to exploiting the self-organizing Sunni uprising in

addition to top-down changes to the strategy.

Designers require complex systems techniques to help identify and model the

self-organizing dynamics of social systems. Gharajedaghi [2006] identifies four

broad perspectives for mapping systems that he claims are mutually exclusive and

collectively exhaustive: structure, function, process, and context. However,

researchers in the field of business process modeling have identified information as a

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distinct additional perspective [List and Korherr 2006]. Complex systems theory,

conceived widely, offers useful tools for each of these five perspectives. Network

theory and cellular automata provide insight into structure; iterated function systems

and „black box‟ functional flow block diagrams represent function; genetic

algorithms, reinforcement learning and system dynamics models represent process;

agent-based models can incorporate each of these perspectives and place them in

context; and almost all measures of complexity are related to the information

embedded in and/or processed by a system. Together, these techniques enable

designers to appreciate from multiple perspectives the relationship between the

calculus of individual agents (their rule sets) and the collective behavior that emerges

from the dynamic interactions between agents.

4.3 Multidimensional problem framing

During problem framing, complex systems theory helps designers to avoid binary,

zero-sum representations of problems in favor of a multidimensional perspective. Multidimensionality is probably one of the most potent principles of systems thinking. It is

the ability to see complementary relations in opposing tendencies and to create feasible

wholes with infeasible parts…. Using a multidimensional representation, one can see how

the tendencies previously considered as dichotomies can interact and be integrated into

something quite new. The addition of new dimensions makes it possible to discover new

frames of reference in which opposing sets of tendencies can be interpreted in a new

ensemble with a new logic of its own [Gharajedaghi 2006].

Complex systems theory shows how design variables that are often traded off against

one another can create synergies when combined from a multidimensional and multi-

level perspective.

For example, the U.S. Central Command‟s Air and Space Operations Center is

currently facing a design tradeoff between being adapted for the current fight (low-

intensity, decentralized counterinsurgency operations in Afghanistan and Iraq) versus

being adaptable for potential future threats (such as high-intensity, large-scale

conventional operations at very short notice). Both extremes of the tradeoff are

unsatisfactory. The current centralized structure maintains readiness against the most

dangerous threat but is inefficient for supporting decentralized operations in

Afghanistan. Restructuring to better match the low-level autonomy of the land forces

in Afghanistan would be more effective, but creates unacceptable vulnerabilities in

the case of unanticipated high-intensity conflict in the region. A multidimensional

approach recognizes that there are some functions that can be decentralized to

improve current operations, some central overheads that must be maintained to deter

and counter future threats, and many ways to improve the agility of the Air and Space

Operations Center to allow rapid switching between regional priorities.

4.4 Designing for emergence

Complex systems theory contributes novel architectures for the operational approach.

One example is indirect design. Rather than designing the exact form for tactical

action, the environment within which tactical decisions are made is designed. This

includes specifying the purpose, constraints and rule sets to guide action. Most

importantly, indirect design concentrates on designing mechanisms for adaptation in

a changing environment. The selection of action is left to tactical commanders on the

ground, who can adapt their action to the local context. In this situation designers

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create the conditions for the emergence of a successful design. As the designers

observe the emerging patterns, they can refine the constraints and rule sets to exploit

new opportunities and counter threats.

A similar concept already exists in military theory: auftragstaktik, or mission

command, communicates intent but not detailed control between a superior and

subordinate. However, complex systems theory adds insights on how to design

development environments, how to connect multiple scales within the system, and

how to accelerate adaptation. Indirect design presents the possibility of designing for

emergence.

4.5 Simple rules, second-order adaptation, and multiscale assessment

As was discussed above, reframing in design is intimately connected with the concept

of adaptation. However, complex systems theory emphasizes the value of simple

rules, contributes the concept of second order adaptation, and informs assessment

within operational design. Complex adaptive systems usually follow simple rule sets.

This is because complicated rule sets do not generate behavior that is qualitatively

any more complex, and simple rules can be adapted more rapidly in a changing

context. Second-order adaptation applies adaptation (variation, selection, and

replication) to the adaptation process itself. Designers that experiment with new

methods for reframing, select methods that improve relevance, and replicate those

methods by sharing them with other designers are utilizing second-order adaptation to

continuously improve their capacity to design.

Complex systems theory enables designers to assess the effectiveness of their

design across multiple temporal and spatial scales. Assessment of improvement is

often averaged over space and assumes incremental progress over time. These

assumptions do not hold in general for complex systems. Self-organization generates

patterns that maintain themselves far from well-mixed spatial averages, while phase

transitions produce nonlinear and often discontinuous progress over time. Instead of

using average, incremental movement in a positive direction to drive feedback to

adapt the operational design, a complex systems approach requires a nonlinear,

multiscale, dynamic model that connects a network of measures throughout the

system and its environment. The model provides an explicit theory of reality that

allows designers to make sense of observed spatial patterns and aggregate these in a

nonlinear way. It connects strategic, operational, and tactical assessment such that the

strategic assessment is not merely the sum of tactical measurements. The model does

not need to be perfect. As long as it is subject to an adaptive process, it will be

increasingly effective over time.

5 Conclusion

In this paper, we have seen how military theorists have acknowledged complexity

and responded effectively through the design of adaptive operations. We then

explored ways in which complex systems theory applies throughout design: modeling

complex social systems; multidimensional problem framing; designing for

emergence; and using simple rules for intervention, constructing second-order

adaptive mechanisms, and multiscale assessment frameworks.

1264

It would seem that these approaches to operational design developed within the

military domain have broader applicability and could be tailored to the design of

other governmental and commercial operations. For interagency operations, where no

single agency is in charge and different organizational interests and cultures create a

high degree of internal complexity, there is an obvious need for design. Businesses

also operate in a highly competitive and often rapidly evolving environment, and can

benefit from operational designing to connect strategy and execution. The military

experience reveals the central cognitive tension between developing deep, nuanced

understanding versus constructing relatively simple and clearly communicable

interventions to learn about and improve conditions in a complex, open system. A

design that creatively resolves this tension between theory and practice is, in a word,

elegant.

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PrefaceContentsI Invited Plenary Talks[409] Johan Bollen: Measuring Fluctuations of Collective Mood States from Large-Scale Social Media Feeds: From Stock Markets to Subjective Well-Being[414] Len Fisher: Can We See the Future? Early-Warning Signs for Critical Transitions in Nature and Society[413] Yaneer Bar-Yam: Complexity and Human Civilization[385] Alfred Hubler: Emergence of Functionality in Physically Evolving Networks[406] Stuart Pimm: Food Webs: Structure, Dynamics, Assembly, and What Features are General[403] H. Eugene Stanley: Economic Fluctuations and Statistical Physics: Quantifying Extremely Rare Events with Applications to the Present World Crisis[404] Mark Newman: Influence in Networks and the First-Mover Advantage[410] Eshel Ben-Jacob: Bacterial Collective Intelligence[395] Kunihiko Kaneko: Complex Systems Biology: Exploring the Logic of Life through Consistency Principle[402] Didier Sornette: What Percentage of Our Ancestors Were Men? The Most Underappreciated Fact About Men![398] Leah Edelstein-Keshet: Understanding Cell Motility with Mathematical Models[405] Alex Pentland: How Social Networks Shape Human Behavior[399] Mark Klein: Managing Emergence in Large-Scale Deliberation[412] Thomas Schelling: Why, in Thirty Years, No Nuclear Terrorism?[400] John Hopfield: Animal Behavior and Emergent Computational Dynamics[396] James Mcclelland: Emergence in Cognitive Science[393] Steven Bressler: The Expectant Cortex[408] Jerome Kagan: The Tapestry of Variation in Human Traits[376] Geoffrey West: The Complexity, Simplicity, and Unity of Living Systems from Cells to Cities; Towards a Quantitative, Unifying Framework of Biological and Social Structure, Organization and Dynamics[407] Nassim Taleb: Model Error, Convexity and Skewness[392] Eric Beinhocker: Complexity, Economics, and Public Policy: New Tools for Real World Challenges[401] Sorin Solomon: Autocatalytic Feedback Loops Amplify Microscopic Random Events to Systemic Complex Changes[394] Dennis Bushnell: Societal Future States[386] Neil Gershenfeld: Computer Science = Physical Science

II Contributed PresentationsHighlight Plenary Session[146] Charlotte Hemelrijk, Ivan Puga-Gonzalez and Jan Wantia: The Self Organization of Primate Social Systems[380] Scott Turner: Emergence of a Superorganism

Emergence, Complexity and Information[110] Hugues Bersini: Emergent phenomena only belong to biology[175] Guillaume Chérel, Jacques Gignoux and Jean-Daniel Zucker: The multi-scale nature of emergent properties: a formalism[180] Abd-El-Kader Sahraoui: On Emergent Properties When Complexity Is Dealt With Simplicity , a case study with co simulation[296] Kawandeep Virdee, Yavni Bar-Yam, Dion Harmon, Yaneer Bar-Yam, Giovanni Fusina and Gerard Pieris: Quantifying Multi-Scale Structure and Capabilities in Complex Systems[55] Georgi Georgiev: Least action as a quantitative measure for organization of a system[39] Jeff Levin and Ann Gossard: The Importance of Organizational Process Towards Reducing Computational Complexity[208] Ben Allen, Blake Stacey and Yaneer Bar-Yam: A Formalism for Multiscale Structure in Complex Systems[149] William Sulis: Causal Tapestries[243] Shawn Pethel and Daniel Hahs: Distinguishing Anticipation from Causality: Anticipatory Bias in the Estimation of Information Flow[125] Hector Sabelli: Causal creation: methods, mathematical model, and quantum, cosmic, biological and socioeconomic processes

Ecology and Organism Behavior[261] Marcus Frean and Richard Mansfield: Emergence of "Rock-Paper-Scissors" ecologies from intra-specific competition[187] Amanda Galante, Susanne Wisen, Devaki Bhaya and Doron Levy: Stochastic Models and Simulations of Phototaxis[81] Elske Van Der Vaart, Rineke Verbrugge and Charlotte K. Hemelrijk: Corvid Cache Protection: Alternative Explanations from a Computational Model[158] Andrew Hein and Scott Mckinley: Navigating spatial environments: a probabilistic approach to understanding biological search

Environment and Sustainability[214] Clément Chion, Lael Parrott, Samuel Turgeon, Philippe Lamontagne, Cristiane C. A. Martins, Jacques-André Landry, Danielle Marceau, Robert Michaud, Nadia Ménard, Guy Cantin and Suzan Dionne: An agent-based model to support the sustainable management of a complex social-ecological system[23] Anthony Halog, Najet Bichraoui, Yosef Manik and Binod Neupane: Advancing Integrated Methodological Framework for Developing Sustainable and Resilient Systems[159] Fred M. Discenzo and Kenneth A. Loparo: Process Design and Optimization for Algae-Oil Systems[156] David Keith, John Sterman and Jeroen Struben: Understanding Spreading Patterns of Hybrid-Electric Vehicle Adoption[71] Dimitris Papanikolaou: The Market Economy of Trips

Networks[242] David Arney and Elisha Peterson: Embracing the Complexity of Networks[84] Scott Pauls: A new notion of centrality for dense graphs[183] Joon-Young Moon, Dong-Myung Lee, Jacobus Koolen and Seunghwan Kim: Core-periphery disparity in fractal behavior of complex networks[140] Takeshi Ozeki, Hideyuki Koto, Hajime Nakamura and Teruhiko Kudo: Unique mode structure of BA network dominates Network Dynamics[63] Bing Wang, Lang Cao, Hideyuki Suzuki and Kazuyuki Aihara: Bond percolation on clique random networks and their applications to arbitrary interacting epidemics[215] Gergely Palla, Peter Pollner and Tamas Vicsek: Rotated multifractal network generator[78] Leonardo Morsut, Lorenzo Barasti, Domenico Salvagnin, Jonathan So and Arrigo Zanette: Spontaneous emergence of a new robustness motif in networks under intrinsic noise evolution[290] Greg Ver Steeg, Armen Allahverdyan and Aram Galstyan: Semi-supervised Clustering in Sparse Networks[52] Eric Minch: Semantics of Network Display and Navigation[280] Huiqi Zhang and Ram Dantu: Human Relationship and Behavior Analysis in Mobile Social Networks

Social Systems (1): Human Behavior, Opinion and Cultural Dynamics[229] Megan Olsen, Kyle Harrington and Hava Siegelmann: Computational Emotions in a Population Dynamics Cellular Automata Encourage Collective Behavior[346] Lilianne Mujica-Parodi, Anca Radulescu, Denis Rubin, Tomer Fekete and Helmut Strey: Diagnosing Emotional Stress Resilience from Limbic Regulation: fractals, pheromones, and fear[172] Christian Alis and May Lim: Utterance length distributions of conversations with coding limits[268] Amer Ghanem and Ali Minai: Patterns of Interaction Behavior in Online Social Networks[22] Andreas Ligtvoet: Cooperation as a complex, layered phenomenon[127] Burton Voorhees: Trade-offs Between Accuracy and Quickness in Risk/Reward Situations[58] Ihor Lubashevsky and Shigeru Kanemoto: Complex dynamics of systems with motivation caused by scale-free memory[244] Alexander Outkin and Robert Glass: Applications of Self-calibrating Hybrid Causal-Learning Systems to Opinion Dynamics Modeling[100] Anthony Woolcock, Colm Connaughton and Yasmin Merali: Linked and Weighted Features: Extensions to Axelrod's Cultural Model[313] Sarjoun Doumit and Ali Minai: Online News Media Bias Analysis using an LDA-NLP Approach[269] Katalin Martinas: Greatest Happiness Principle in a Complex System Approach

Complex Systems and Health[165] Masayuki Kakehashi: A fundamental mathematical model of the health care system[3] Stefan Topolski: Understanding Health from a Complex Systems Perspective[6] Stefan Topolski: The Nature of Virtue in Health Care Reform[226] Thomas Moore, Patrick Finley, John Linebarger, Alexander Outkin, Stephen Verzi, Nancy Brodsky, Daniel Cannon and Robert Glass: Extending Opinion Dynamics to Model Public Health Problems and the Evaluation of Policy Interventions[224] Thomas Moore, Patrick Finley, John Linebarger, Walter Beyeler, Victoria Davey and Robert Glass: Analyzing Public Health Care as a Complex Adaptive System of Systems[248] Michael Widener, Sara Metcalf and Yaneer Bar-Yam: Modeling Food Preference within Urban Environments

Complex Systems and Medicine[4] Stefan Topolski: Improving the Medical Home through an Understanding of Complex Systems[8] Stefan Topolski: An ethnographic landscape of clinicians' understanding of Complex Systems Principles[249] Bo Sun, Alex Klinke, Francisco Garcia and Paul Halpern: Tumor Vascular Permeability Measurement Using the MRF Model and a Genetic Algorithm[186] Anisoara Paraschiv-Ionescu, Christophe Perruchoud, Eric Buchser and Kamiar Aminian: Complexity of human activity patterns in chronic pain: from models to clinical assessment tools[228] Albaraa Salama, Robert Mart, Steven Nowicki and Stefanie Lopatkin: An Improved Algorithm That Strategically Determines When to Test ICD Lead Integrity

[MASCC] Workshop: Mathematical Aspects of Social and Cognitive Complexity[131] Ju"rgen Klu"ver: Meaning, Information, and the Understanding of Ambiguity[198] Brian Castellani and Rajeev Rajaram: Social Complexity Theory: A Mathematical Outline[33] Dwight Read: Cultural Kinship as a Computational System: From Bottom-Up to Top-down Forms of Social Organization[169] Robert Reynolds and Yousof Gawasmeh: Evolving Heterogeneous Social Fabrics for the Solution of Real valued Optimization Problems Using Cultural Algorithms[130] Christina Stoica-Kluever: Solving problems of project management with a Self Enforcing Network (SEN)

[CIF] Workshop: Complex Information Flow[50] Ashley Beattie: Modeling the Complex Information Network at Naval Reserve Headquarters[66] Karthika Raghavan and Heather Ruskin: Computational Epigenetic Micromodel - Framework for Parallel Implementation and Information Flow[109] Dimitri Perrin and Hiroyuki Ohsaki: Applications of Epidemic Diffusion to Complex Systems: from Communication Networks to Autonomous Exploratory Robots[113] Martin Rosvall: Hierarchical organization of large integrated systems[144] Hideaki Suzuki, Mikio Yoshida and Hidefumi Sawai: Knowledge Transitive Network: A data-flow network for backward deduction[168] Yusuke Sakumoto, Takeaki Nishioka, Hiroyuki Ohsaki and Makoto Imase: On the Effectiveness of Stable Numerical Solution for Flow-Level Network Simulation[176] Yosuke Yamada, Hiroyuki Ohsaki, Dimitri Perrin and Makoto Imase: Impact of Mobility Constraints on Epidemic Broadcast in DTNs[221] Lilian Weng, Alessandro Flammini and Filippo Menczer: An Information Propagation Model Based on User Interests

[ACS] Workshop: Aesthetics in Complex Systems[202] Stanislas Renard and Philip Fellman: What is Romani Music? An emerging definition learned from social network analysis[239] Raoul Rickenberg: Generating insight by design[265] Leslie Alfin: Complexity & Evolved Aesthetics the Enlightened "Hopeful Monster": The Next Great Leap[295] Daniel Kohn: Embracing Complexity: A Context for Meaning in Art and Science[314] Daniel Jones and Mark D'Inverno: Amplifying scientific creativity with agent-based simulation[321] Vaidehi Venkatesan and Ali Minai: Cuisines as Complex Networks[329] Arturo Buscarino, Luigi Fortuna, Mattia Frasca and Angelo Lamia: Foggy painting complexity[330] Arturo Buscarino, Paola Belluomo, Luigi Fortuna and Mattia Frasca: Aesthetics and emergent behaviour

[STCAN] Workshop: State-Topology Coevolution in Adaptive Networks[1] Hiroki Sayama: Adaptive networks: An emerging research theme on state-topology coevolution in complex networks[203] Rob Mills, Richard A. Watson and C. L. Buckley: Emergent associative memory as a local organising principle for global adaptation in adaptive networks[211] Blake Stacey, Andreas Gros and Yaneer Bar-Yam: Beyond the Mean Field in Host–Consumer Spatial Ecology[182] Yasmin Merali: Information Dynamics and the Resilience of Social Systems[195] Bing Wu: Effects of Incentive Mechanism to Knowledge Transfer Network in Enterprise[256] Hiroki Sayama and Junichi Yamanoi: An Adaptive Network Model of Cultural Integration in Corporate Merger[178] Benjamin J. Bush, Jeffrey Schmidt and Hiroki Sayama: Behavior and Centrality in Idea Exchanging Adaptive Social Networks[89] Jeffrey Schmidt, Benjamin Bush and Hiroki Sayama: A Python Implementation of Generative Network Automata

[SFC] Workshop: Science Fiction and Complexity[391] Leonid Korogodski: Complexity in Science Fiction: Pink Noise

Nonlinear Dynamics, Chaos, Fractals and Stochastic Systems[25] Kevin Hernandez-Pardo and Roozbeh Daneshvar: Dynamics of a Self-Adjusting Random Fibonacci System[128] Burton Voorhees: The Value of Non-Attachment in Complex Adaptive System Behavior[122] Philippe Binder and Brian Wissman: Mutual information and chaotic systems[72] Takashi Iba and Kazeto Shimonishi: The Origin of Diversity: Thinking with Chaotic Walk[48] Andrew Seely: Fractal structures optimize entropy production in complex dissipative systems[164] Irina Trofimova: Functional differentiation (FD) and the phenomena of fractal functionality (FF)[153] Florentin Paladi: Stochastic versus agent-based models in theory and applications[199] Benjamin Aas: Body-Go"del-Mind: The unsolvability of the hard problem of consciousness

Complex Systems Education[141] Ronald Degray and Shyamala Raman: Promoting the Study of Complex Systems into K-16 Curricula[148] Federica Raia and Neel Patel: Questions and Explanations –Students' Understanding of Complex Systems[210] Odette Lobato, Yoguez Amalia and Orlando Susarrey: Exploring the characteristics of the Mexican Higher Education System in the context of Complex System Approach

Systems Biology[323] Mario Pavone: P system Reverse Engineering for Gene Regulatory Networks in S-system Model[276] Andrea Velenich, Mingjie Dai and Jeff Gore: Genetic interactions in yeast are described by heavy-tailed distributions[298] Michelle Girvan: The Effects of Network Structure on the Stability of Genetic Control: From Models to Data[237] Micah Brodsky and Gerald Sussman: White Lies About Biology: Programming Deformable Surfaces

Systems Pathology[281] Len Troncale: Could A Unified Natural Systems Science Produce a Top-Down, Systems-Level Systems Pathology?[250] Andreas Gros, Marcus A.M. De Aguiar, Tilo Winkler, Irving R. Epstein and Yaneer Bar-Yam: Asthma: Pattern formation in bronchial networks[283] Len Troncale: Case Studies in Systems Pathology: Recognizing Domain-, Discipline-, Tool- & Scale-Independent Diseases

Epidemiology[7] Stefan Topolski and Joachim Sturmberg: Epidemiologic Validation of a Complex Systems Health Model[181] David Hiebeler and Isaac Michaud: Targeted Treatment of Outbreaks In a Community-Structured Model[225] Gavin Fay, Megan Olsen, Joseph Gran, Anne M. Johnson, Vanessa Weinberger, Julie Granka and Oana Carja: Agent-based model of Tasmanian Devils examines spread of Devil Facial Tumor Disease due to road construction

Collective Behavior[277] Francisco J. Sevilla and Alexandro Heiblum Robles: Collective Motion in a System of Brownian Agents[152] Charlotte Hemelrijk and Hanno Hildenbrandt: Causes of the variable shape of flocks of birds[204] Victor Dossetti and Francisco Javier Sevilla: Intermittency and ergodicity breaking in a system of interacting self-propelled particles[264] Zoltan Neda, Erna Kaptalan and Artur Tunyagi: Emerging synchronization as a result of optimization[288] Ashish Umre and Ian Wakeman: Social Foraging Dynamics in Distributed Systems[284] Abbas Sarraf Shirazi, Sebastian Von Mammen, Iman Yazdanbod and Christian Jacob: Self-Organized Learning of Collective Behaviours in Agent-Based Simulations[145] Erbo Zhao and Zhangang Han: An agent-based model for studying on flocking behaviors with competition and cooperation[102] Ferenc Ja'rai-Szabo', Bulcsu' Sa'ndor and Zolta'n Néda: Spring-block modeling of highway traffic[167] Nguyen Thi Ngoc Anh, Zucker Jean Daniel, Nguyen Huu Du, Drogoul Alexis and Vo Duc An: Hybrid Equation-based and Agent-based Modeling of Crowd Evacuation on Road Network[207] Pedram Hovareshti, Hua Chen and John Baras: Motif-based Topology Design for Effective Performance by Networks of Mobile Autonomous Vehicles

Social Systems (2): Market, Innovation, Land Use and Public Policies[29] Mark Bedau, Noah Pepper, Devin Chalmers and Charles Francis: Statistical measures of the evolution of the drivers of technology as reflected in the patent record[173] Pragya T. Gupta: Behavior, Social identity and Labour Market Processes as a Complex System[292] Nancy Brodsky, Arlo Ames, Robert Glass, Theresa Brown, Patrick Finley, Thomas Moore, John Linebarger, Aldo Zagonel and Louise Maffitt: Application of Complex Adaptive Systems of Systems Engineering to Tobacco Products[75] Pierpaolo Andriani: Tiny initiating condition triggers emergence of new market: the case of the appearance of the quality coffee sector in the Brazilian market[67] Manuela Korber and Manfred Paier: Exploring the Effects of Public Research Funding on Biotech Innovation: An Agent-Based Simulation Approach[129] Laetitia Gauvin, Annick Vignes and Jean-Pierre Nadal: Modeling urban housing market dynamics: can the socio-spatial segregation preserve some social diversity?[54] Daniel Vasata, Pavel Exner and Petr Seba: Built-up structure criticality[306] Roberto Murcio and Suemi Rodriguez-Romo: Modeling Mexican Urban Metropolitan Areas by a Self-Organized Criticality approach[205] C. Schilling, C. Bryant and L. Parrott: Exploring constraints to environmentally supportive behaviour in a municipal setting: Is all self-organized urban structure alike?[223] Patrick Finley, Robert Glass, Victoria Davey, Thomas Moore, Arlo Ames, Leland Evans, Daniel Cannon, Jacob Hobbs and Matthew Antognoli: Integrating Uncertainty Analysis into Complex-System Modeling to Design Effective Public Policies

Complexity and Management[174] Pierpaolo Andriani, Bill Mckelvey and Renata Kaminska: Managing in a Pareto World Calls for New Thinking[76] Renata Kaminska, Bill Mckelvey and Catherine Thomas: Building Dynamic Capabilities in Times of Drastic Change: Lessons from Complexity Science[137] Denise Easton and Lawrence Solow: Navigating the Complexity Space[18] Sharon Ackerman and Jennifer Jenkins: Conceptualizing Distributed Workplace Environments Through the Lens of Complex Adaptive Systems Theory[53] Ian Wilkinson, Louise Young, Robert Marks, Terry Bossomaier and Fabian Held: A Business Network Agent-Based Modeling System for domain specialists[91] Lorraine Dodd: A theory of choices: melding black swans, butterflies and swallowtails[27] Kevin Mcdermott: Engaging Expectant Stakeholders: A complexity theory approach to engaging stakeholders in complex organizational stakeholder environments

Decision Making[177] Anne-Marie Grisogono and Vanja Radenovic: The Adaptive Stance - steps towards teaching more effective complex decision-making[233] Simona Doboli and Vincent Brown: A Neural-Network Model of Conceptual Combinations[163] Hadassah J. Head, Benjamin James Bush, A. Gupta, Hiroki Sayama and Shelley D. Dionne: Network-Informed Idea Selection Strategies for Electronic Brainstorming

Poster Session A[11] Hugh Trenchard: Energy savings in bicycle pelotons, a general evolutionary mechanism and a framework for group formation in eusocial evolution[364] Igor Yevin and Nokolay Shuvalov: The Theory of Complex Networks in Painting Studying[45] Mary Keeler, Josh Johnson and Arun Majumdar: Crowd-Sourced Knowledge: Peril and Promise for Complex Knowledge Systems[56] Georgi Georgiev: Quality as a function of quantity[70] Takashi Iba: Hidden Order in Chaos: The Network-Analysis Approach to Dynamical Systems[73] Boming Yu: A study of the stability of deterministic complex networks[86] V Anne Smith: Tailoring Bayesian networks for recovering structure of different types of biological networks[107] Ming-Chang Huang: Characterizing the process of reaching the state of consensus in a social system[108] Yu-Pin Luo: The effect of the cliques of networks on the occurrence probabilities and the robustness of the equilibrium states of local majority-rule[116] Sarah Symons and Derek Raine: Agent Based Modelling of State Formation in Ancient Egypt[133] James Putnam: The Nature of Thermodynamic Entropy[154] Czeslaw Mesjasz: Can Physics Help Social Sciences in Prediction of Complex Collective Phenomena?[155] Yoritaka Iwata: Percolation approach for cosmic matter[162] Lui's Vilar, Duarte Arau'jo, Keith Davids, Pedro Esteves and Vanda Correia: Spatial-temporal constraints on successful performance in team sports[184] Martine Barons: A mathematical approach to medical complexity[194] Uday Kulkarni: Dynamics of Exchange[212] Maurice Passman, Philip Fellman and Jonathan Post: Ontological Determinism, non-locality and the system problem in quantum mechanics[272] Lech Schulz: Practical Applied Theory of Abstract Complex Systems - An Introduction via Fuzzy Set Based Comparisons and Optimization[297] Kawandeep Virdee, Alex Rutherford and Yaneer Bar-Yam: Characterizing Complex Terrains: Mathematical Foundations and Applications[299] Yavni Bar-Yam, Dion Harmon and Yaneer Bar-Yam: Calculating the Complexity Profile[303] Vedant Misra, Dion Harmon and Yaneer Bar-Yam: Vulnerability Analysis of High Dimensional Complex Systems[307] Karla Bertrand, Alexander Gard-Murray and Yaneer Bar-Yam: A systems approach to improving healthcare[357] Jose Padilla, Saikou Diallo and Andreas Tolk: Complexity as a Function of Understanding

Late-Breaking News Session A[356] Seung Kee Han: Estimating the Network Link Weights from the Inverse Phase Synchronization Matrix[358] Francesco Sorrentino: Synchronization of hypernetworks of coupled dynamical systems[359] Robert Scheidt, Nicole Salowitz, Janice Zimbelman, Aaron Suminski, Kristine Mosier, Lucia Simo and James Houk: Learning visualized in cerebral cortex, basal ganglia and cerebellum[360] Woon Seon Jung, Dong-In Lee and Kyungsik Kim: Characteristics of the sea surface distribution using the rescaled range analysis in three seas[361] Jae Won Jung, Gyuchang Lim and Kyungsik Kim: Aanlysis of multifractal structures in two-dimensional Lattices[362] Michel Ducharme, Daniel Lafond and Bradley Rathbun: A Gaming Approach to Train Systems Thinking in the Military[389] Joon Kim, Jewoo Hong, Juyeol Yun and Hyojung Kwon: Ecohydrologic and Biogeochemical Process Networks in Forest Ecosystems

Engineering Systems[294] Kyle Harrington, Emma Tosch, Lee Spector and Jordan Pollack: Compositional Autoconstructive Dynamics[143] Xiaocong Gan and Zhangang Han: Control The Game Ms. Pac-Man Automatically – Using The Real Time Search Method[123] Robert Glass, John Linebarger, Arlo Ames, Walt Beyeler, Patrick Finley and Theresa Brown: Complex Adaptive Systems of Systems (CASoS) Engineering: Mapping Aspirations to Problem Solutions[10] Anthony Masys: Safety Culture- Revealing the complexity[328] Daniel Sturtevant, Alan Maccormack and James Paul Peruvankal: Network Structure and Bugs in Complex Software Systems[218] Touria El Mezyani, Ralph Wilson, Michael Sattler, Sanjeev Srivastava, David Cartes and Chris Edrington: Complexity Quantification to Enhance the Power Systems Design and Modeling[236] Shashank Tamaskar, Tatsuya Kotegawa, Kartavya Neema and Daniel Delaurentis: Measuring Complexity of Aerospace Systems[209] Justin W. Gillespie and David J. Singer: Gaining insight into the structure of an early-stage ship design[112] Nelson A. Go'mez Cruz and Carlos Eduardo Maldonado: Biological Computation: A Road to Complex Engineered Systems[170] Rui Teng, Bing Zhang and Nori-Shirazi Mehdad: Dynamic and Adaptive Organization of Data-Collection Infrastructures in Sustainable Wireless Sensor Networks[246] Sarah Sheard and Ali Mostashari: Use of Complexity Types in the Realms of Systems Engineering[316] Anindya Deb and Amardeep Singh: A Semi-integrated CAE Approach for Analyzing a Complex IC Engine System

Physical and Chemical Systems[310] Emmanuel Landa Hernandez, Irving O. Morales Agiss, Rubén Fossion, Pavel Stransky, Juan C. Lo'pez Vieyra, Vi'ctor M. Vela'zquez and Alejandro Frank Hoeflich: Scale Invariance and Criticality in Classical and Quantum Examples[21] Nadeem Bashir and G M Peerzada: Effect of counter-ions in different salts on the Resorcinol-BrO3–Mn2+-H2SO4 Oscillatory Chemical Reaction[139] Fabrice Debbasch and Jean-Pierre Rivet: New stochastic models of thermodiffusion: entropic validation through kinetic theory[279] Santiago Nunez-Corrales and Eric Jakobsson: Hierarchical Modularity: The Description of Multi-Level Complex Systems as Nested Coupled Fokker-Planck Equations[119] Gaetano Campi, Alessandro Ricci, Michela Fratini, Nicola Poccia, Gabriele Ciasca, Alessandra Mari, Lorenza Suber, Mario Altamura, Augusto Pifferi and Antonio Bianconi: New experimental approaches for evolution and control of complexity in out-of equilibrium systems[69] Georgi Georgiev: The principle of least action and the second law of thermodynamics

Cosmology[85] Christoph Raeth: Probing non-Gaussianities in the cosmic microwave background by means of nonlinear data analysis techniques[93] Philip Fellman, Maurice Passman and Jonathan Post: Time, Uncertainty and Non-Locality in Quantum Cosmology

Origin of Life[38] A. Karim Ahmed: Complexity, Affinity-Bias and Systems Biology: An Ecosystem Model of the Origin and Evolution of Life[115] Derek Raine and Sarah Symons: An agent-based model of coding and inheritance in a prebiotic ecology[9] Walter Riofrio: Cellular Dynamics at the Beginning of Prebiotic World

Evolution[304] Stephen Serene, Longzhi Tan, Hui Xiao Chao and Jeff Gore: Hidden randomness between fitness landscapes limits reverse evolution[121] Marcus A.M. de Aguiar, Elizabeth Baptestini, Ayana Martins and Yaneer Bar-Yam: A neutral speciation theory for one-dimensional and ring geometries[260] Marcus Frean, Gareth Baxter and Paul Rainey: Ongoing evolution on networks[120] Chris Phoenix: Animals Made of Stem Cells: Immortality and Regeneration vs. Brains, Blood, and the Cambrian Explosion[240] Kirill Korolev, Joao Xavier, David Nelson and Kevin Foster: Genetic demixing in a Petri dish[217] Walt Beyeler, Robert Glass, Patrick Finley and Theresa Brown: Modeling Systems of Interacting Specialists[343] Alexis Morris, William Ross, Mihaela Ulieru and James Whitacre: The Evolution of Cultural Resilience and Complexity[317] Zann Gill: Collaborative Intelligence and Effective Complexity

Economic and Financial Systems[397] Dror Kenett and Eshel Ben-Jacob: Uniformity, Multiformity and Systemic Collapses in the Global Financial Village[287] Dion Harmon, Marcus A.M. de Aguiar, David Chinellato, Dan Braha, Irving Epstein, Marco Lagi and Yaneer Bar-Yam: Predicting economic market crises using measures of collective panic[305] Vedant Misra and Yaneer Bar-Yam: Why the stock market crashed: market instability and financial regulations[118] J. J. Farias Neto: The Financial Market as a Complex System[270] Gyuchang Lim, Kyungsik Kim, Soo Yong Kim and Jin Min Kim: Regular pattern in the collective behavior of investors[150] Victor Yakovenko: Entropy maximization and distributions of money, income, and energy consumption in a market economy[114] Walid Nasrallah: The dynamics of micro-economically relevant institutions[263] Sehyun Kim, Min Jae Kim, Soo Yong Kim and Kyungsik Kim: Intra- and inter-sector dependence structure in stock markets[282] Daniele Signori and Ramazan Gencay: Economic Links and Counterparty Risk[135] Jean-Pierre Nadal, Mirta B. Gordon, Denis Phan and Viktoriya Semeshenko: Pricing of Goods with Bandwagon Properties: Entanglement between Demand and Supply[90] Hugues Bersini: Why should the economy be competitive ?[60] Jacky Mallett: Analysing the behaviour of the textbook fractional reserve banking model as a complex dynamic system

[EE] Workshop: Engaging Emergence: Working with Changing Social Systems in Complex, Volatile Times[105] Peggy Holman: Engaging Emergence: Turning Upheaval into Opportunity[245] Lawrence Solow and Denise Easton: Learning in Complex Times: Complex Adaptive Learning[142] Ed Addison: Understanding Organizational Design, Culture and Change Management from a Complex Systems Perspective

Biomolecular and Cellular Systems[36] Val Bykovsky: Molecular Experimentation and Evolutionary Biology[193] Chi-Han Chang, Rayomond Dinshaw and Gregory D. Scholes: Quantitative Fit of Linear Spectra to Refine the Electronic Hamiltonian of the Photosynthetic Protein, Phycocyanin 645[124] Mauricio Garcia-Vergara and Guillermo Ramirez-Santiago: Positional and Temporal Information Transmitted by a Cell Signal Cascade with n-Modules[19] Weijiu Liu and Yuee Chen: Modeling store-operated calcium entry with membrane potential regulation[289] Megan Brady, Paul Frisch, Kenneth Mcleod and Craig Laramee: Emergent Responses of Cellular Systems to Static Magnetic Field Exposure

Neural and Psychological Systems[47] Ursula Drager: Postnatal Changes in Retinoic Acid Signaling Delineate Domains with Anti-Correlated Gene Expression in the Medial Cerebral Cortex of Adult Mice[220] Jeongkyu Shin, Uncheol Lee, George Mashour, Seungwoo Ku and Seunghwan Kim: Dynamic functional backbones of brain networks during anesthetic state transitions[338] Daniel Remondini, Sebastiano Stramaglia, W. Liao, G. Castellani and D. Marinazzo: Exploring brain dynamics complexity by fNMRi[266] Michael Norman, Larry Liebovitch, Paul Peluso, Jessica Su and John Gottman: Mathematical Model of the Dynamics of Psychotherapy[5] Stefan Topolski: Pre-existing complex systems concepts among complexity naive physician peers[44] Steven Hassan: Complex systems approach to dealing with destructive cult groups

Complex Systems Design[369] William Ross, Alexis Morris and Mihaela Ulieru: Exploring the Impact of Network Structure on Organizational Culture Using Multi-Agent Systems[227] Anne-Marie Grisogono: Design as a strategy for dealing with complexity[40] Alex Ryan: Applications of Complex Systems to Operational Design[251] Paul Seguin: Complex Systems Science: Moving from Theory to Application in the World's Largest Public Engineering Agency

Modeling Methodology[273] Juan Fernandez-Gracia, Vi'ctor M. Egui'luz and Maxi San Miguel: Update rules and interevent time distributions: Slow ordering vs. no ordering in the Voter Model[222] Wei An, Dalei Wu and Song Ci: Modeling Dynamical Complex System with Partial Observations[312] Artemy Kolchinsky and Luis M. Rocha: Prediction and modularity in complex dynamical systems[324] Sarjoun Doumit and Ali Minai: Bayesian Analysis and Prediction of News using an LDA-Based Approach[30] Mathias Beck and Andrea Schenker-Wicki: How to manage complex systems: new qualitative methods[26] Rajeev Rajaram: The use of a density based approach in study of complex non-equilibrium dynamics

Social Games and Evolution[37] Eric Beinhocker: Evolution as Computation: Integrating Self-Organization with Generalized Darwinism[213] Benjamin Allen: Population structure and the evolution of social behavior[326] David Rand: Noise, heterogeneity and the evolution of human cooperation[160] Lui's Vilar, Duarte Arau'jo, Keith Davids, Bruno Travassos and Ricardo Duarte: Emergence of patterns of coordination in attacker-defender dyadic systems in team sports[88] Walid Nasrallah: Whence Complexity: On the evolutionary advantage of complexity in tribal artificial life[278] Bin Xu and Zhijian Wang: Evolutionary Dynamical Pattern of "Coyness and Philandering": Evidence from Experimental Economics[259] Marcus Frean and Joseph Bulbulia: Neutral evolution as a route to large-scale cooperative equilibriums in the Stag Hunt game[262] Sanghyun Ahn, Kyungsik Kim, Dong-In Lee and Soo Yong Kim: Mechanism on nonmyopic agents changing the endogenous decision rules[219] Christoforos Somarakis and John Baras: On the complexity of a social behavior model[92] Paul Smaldino and Jeffrey Schank: How We Move Around, Not Just That We Do: Random Walks, Agent-Based Models, and the Evolution of Cooperation[309] Jeff Schank, Paul Smaldino and Matt Miller: Is sharing irrational?: An agent-based modeling analysis of the ultimatum game with spatially explicitly mobile agents

Safety and Security[77] Arthur Tomasino: Complex Adaptive Systems and Public Safety[59] Irene Pestov: Dynamical Networks with Embedded Heterogeneous Agents[190] Jérôme Levesque, Kate Kaminska and Sean Norton: Information sharing network in a community of senior security planners for the Vancouver 2010 Olympics[57] Ali Bas and Volkan Karaca: A Simulation on Organizational Communication Networks During a Terrorist Attack[95] Philip Fellman, Kathleen Carley and Gregory Parnell: Biowar and Bioterrorism Threat Risk Assessment[98] Philip Fellman, Dinorah Frutos, Nathan Thanakijsombat and Pard Teekasap: The Complex Landscape of Maritime Piracy

International Issues and Globalization[318] Walter Clemens: Toward a New Paradigm for International Studies: The Complexity of Interdependence[192] Michael Hu"lsmann, Richard Colmorn and Viktoryia Bakhrutdzinava: Understanding International Supply Networks as Complex Adaptive Logistics Systems - A Complexity-based Approach for Formal Representation[97] Jorge Riveras and Philip Fellman: Dynamic Modeling of International Distributor Agent Networks[286] Alex Rutherford, Dion Harmon, Justin Werfel, Alexander Gard-Murray, Shlomiya Bar-Yam, Andreas Gros and Yaneer Bar-Yam: Predicting Locations of Ethnic Violence[96] Philip Fellman: The Complexity of Intelligence Estimates[285] Alexander Gard-Murray, Karla Bertrand and Yaneer Bar-Yam: Applying complexity theory to real-time geopolitical crises

[SB] Workshop: Syntax in the Brain[340] Jim Houk: Syntax in the Brain: Motor Syntax Agents[325] Neil Berthier: The Syntax of Human Infant Reaching[331] Paul Reber: Computational models of sequential processing[333] Whitney Tabor: Recursion and Recursion-Like Structure in Ensembles of Neural Elements[341] Hiroyuki Ohta, Yasuhiro Nishida and Jim Houk: Presynaptic inhibition and incremental learning in the striatum of the basal ganglia[332] David Fraser and Jim Houk: Motor Syntax Disorder in Schizophrenia[334] Barry Peterson: Pursuit of moving visual targets as a syntactic behavior[322] Howard Eichenbaum: Towards a model of recollection[345] James L. Mcclelland: Temporal structure: Its nature and how it is learned[134] Jim Houk: Can DPM Brain Agents Write Stories and Sing Songs?

Poster Session B[28] Hiroki Sayama: PyCX Project: Complex systems simulation made simple in Python[31] Reut Livne-Tarandach: Change Resurrected: Examining How and Why Change Initiatives Re-emerge[61] Maxim Mozgovoy and Iskander Umarov: Believable Team Behavior: Towards Behavior Capture-Based AI for the Game of Soccer[62] Galina Korotkikh and Victor Korotkikh: From Space and Time to a Deeper Reality as a Possible Way to Solve Global Problems[64] James Murphy and Ray Walshe: Analysing Emergent Complexity in Microbial Populations[68] Xuguang Leng: Survival of the Fittest – Over the Long Run[79] Liz Johnson, Klaus J. Diepold and James Mathieson: World Cup Soccer ABM Simulation: Demonstrating a Hybrid Multi-Scale CAS for Real-World Dynamics[101] Tatyana Unkovskaya and Andrey Grytsenko: Systemic risks of financial crises as effects of emergence in complex economic systems[111] Genki Ichinose and Mio Kobayashi: Frequent propagation of cooperators by random intensity of selection on a network[151] Philip Fellman: Understanding the Complexity of IIED Terrorist Networks[161] Lui's Vilar, Duarte Arau'jo, Keith Davids, Bruno Travassos and Ricardo Duarte: Interdependence between perceptual and motor sub-systems in pattern-forming dynamics in team sports[166] Akshay Patil and Alpana Dongre: Insight into Urban Complexity[196] Zoheir Mottaki: Collective housing as an emergent phenomenon in design knowledge : A case study of hillside Terrace project (Tokyo) as a complex collective form[230] Jorge Barros Pires, Alexandre Evsukoff, Lauro Silveira and Fernando Miranda: Pragmatism, Existential Graphs and Fuzzy Logic: Logic systems Integration for Data Analysis[231] Tama's Kalma'r-Nagy and Kevin Hernandez-Pardo: Revisiting the Physicist's Approach to the Toroidal Traveling Salesman Problem[234] Martin Tensuan and May Lim: Semi-empirical modeling of a microcredit market[235] Zhangang Han: The contribution of the power-law distribution in a trust and reputation evolution in P2P consulting systems[302] Thomas Raway, Craig Laramee, Hiroki Sayama, Shelley Dionne and David Sloan Wilson: Teaching Social Complexity and Multidisciplinary Team Building: An Experimental Engineering Approach[339] Mithilesh Salunke and Ali Minai: Building Modular Animals: A Computational Framework[344] Alexander M. Duda and Stephen E. Levinson: Complex Networks of Spiking Neurons: Collective Behavior Characterization[350] Jonathan Post: Quantum Coincidence with Amino Acid Molecular Weight[353] Ali Minai, Laxmi Iyer, Kiran Byadarhaly and Chandrika Sagar: An Integrated Neurodynamical Framework for Thought and Action[354] Willow Hallgren, Adam Schlosser and Erwan Monier: Global change and the interaction of Human and natural systems: The Impact of Land Use and Biofuel Policies on Future Climate[382] Dong Zhou and Zhangang Han: Effects of Social Identity on Opinion Dynamics with Heterogeneous Agents

Late-Breaking News Session B[372] Pratim Sengupta: Design Principles for a Visual Programming Language to Integrate Agent-based modeling in K-12 Science[378] Sara Sadvandi, Hycham Aboutaleb and Cosmin Dumitrescu: Systemic approach for negotiation process[384] Harsha K, Onkar Hoysala and Bharath M. Palavalli: An Approach to Disaster Management using Games and Agent Based Models[387] Arturo H. Arin o, Samy Gaiji, Javier Otegui, Vishwas Chavan and A. Townsend Peterson: Signal Discovery in World's Available Biodiversity Data[388] Chun Wong, Hiroki Sayama, Manoj Agarwal, Kenneth Chiu and Kevin Heard: Modeling Geographical Diffusion of Broadband Internet Use from Household Survey Data[390] Ajay Deep Kachhvah and Neelima Gupte: Avalanche Transmission and Explosive Percolation in the Criticality of Branching Hierarchical Networks

IndexesPaper ID IndexKeyword IndexAuthor Index