Key Constructs and Figures - DST Innovation Lab...Working(Definitions(for(Key(Constructs( ( 3!Dynamical!Systems!Innovation!Lab,!July!8=12,!2013(( Collapse%of% Multi’ dimensionality%

Dynamical Systems Innovation Lab July 8-‐12, 2013

Working Definitions for Key Constructs and Complex Systems Graphics/Figures

Contents:

1. Working Definitions for Key Constructs……………………........................................................................ p. 2

2. Complex Systems Graphics/Figures………………………………………………….……………………………. p. 10

Working Definitions for Key Constructs 2

Dynamical Systems Innovation Lab, July 8-‐12, 2013

Adaptivity

Adaptivity is an individual competency that allows for “the use of different orientations and strategies in order to satisfy goals in a manner not incongruent with the demands of the situations encountered” (Vallacher et al., 2013; p. 85-‐6).

Attractor “An attractor [italics added] refers to a subset of potential states or patterns of change to which a system’s behavior converges over time. Metaphorically, an attractor “attracts” the system’s behavior, so that even very different starting states tend to evolve toward the subset of states defining the attractor.” (Vallacher et al., 2010; p. 264-‐5) In social systems, an attractor represents “stable patterns of thought, feeling, and action on the part of group members.” (Vallacher et al., 2013; p. 105)

Basin of Attraction

“A basin of attraction [sic] specifies the range of states [within the attractor landscape model] that will evolve toward the attractor.” (Vallacher et al., 2010; p. 266) “If a system has multiple attractors, a strong influence on the system can throw the system in to the basin of attraction of a different attractor, resulting in movement toward an entirely different equilibrium state.” (Nowak & Vallacher, 1998; p. 59)

Bifurcation

A bifurcation is a change in the attractor landscape. “Bifurcations can be manifest in several different ways: a change from a single attractor to two attractors, a change from a single attractor to a periodic attractor (oscillation between two or more coherent states on some timescale), and a sequence of changes from a single attractor through periodic and multi-‐periodic attractors to a chaotic attractor (a complex trajectory of behavior that never repeats and is highly sensitive to initial conditions).” (Vallacher et al., 2013; p. 151)

Catastrophe Theory

Catastrophe Theory describes the phenomenon where, in a system of low complexity, a perturbation has the potential to change the system in a disproportionate, non-‐linear manner such that it leads to widespread, perhaps even catastrophic, effects. This is in contrast to a high-‐complexity system, where perturbations are more likely to stay confined to only the relevant components of the system.

Cellular Automata

Cellular automata are a type of aggregation model that demonstrates how a simple system of interdependent actors, following very simple rules, can create randomness or complexity. This type of model was exemplified by Stephen Wolfram, in his book A New Kind of Science (2002), to show how simple rules can be used to explain many different types of complex systems.

Chaos

Chaos, in dynamical systems theory, “means that a deterministic system, which is completely uninfluenced by chance, can generate effects so complex and unpredictable that they appear to be due to chance.” (Vallacher et al., 2013; p. 45) In other words, social behaviors that seem random can actually be the result of deterministic social mechanisms operating in the system.



Collapse of Multi-‐dimensionality

Specific to conflict, a collapse of multidimensionality occurs when individuals and groups who normally share rich, multidimensional dynamics of relations and common goals, lose this multidimensionality and collapse into a one-‐dimensional dynamic system organized around antagonism between parties. (Vallacher et al., 2013; p. 130-‐131)

Complex Systems

A complex system is a system “composed of many interconnected elements.” (Vallacher et al., 2010) A complex adaptive system is a type of complex system in that is composed of multiple interconnected elements, and has the capacity to adapt and change in response to perturbations from the environment.

Complexity Science

“Complexity science [italics added] introduces a new way to study regularities that differs from traditional science. Traditional science has tended to focus on simple cause–effect relationships. In the ideal gas law, a rise in temperature leads to a corresponding rise in pressure. Similarly, Newton’s well-‐known formula that force equals the product of mass and acceleration (F=MA) also expresses a simple relationship… Complexity science posits simple causes for complex effects. At the core of complexity science is the assumption that complexity in the world arises from simple rules. However, these rules…are unlike the rules (or laws) of traditional science…” (p.130). “Traditional science seeks direct causal relations between elements in the universe, whereas complexity theory drops down a level to explain the rules that govern the interactions between lower-‐order elements that in the aggregate create emergent properties in higher-‐level systems.” (Phelan, 2001, p. 132)

Control Parameter

Within the attractor landscape model, a control parameter is an external factor that can “promote quantitative changes in a system’s behavior (e.g. moving the system from a manifest attractor to a latent attractor).” (Vallacher et al., 2013; p. 151) In simulations, control parameters represent the simple rules that operate within each cell of the simulation grid, which are set with a specific level of probability. For example, in a basic simulation of Deutsch’s Crude Law of Social Relations, a rule might be that a competitive behavior will elicit a new competitive behavior.

Coordination

Coordination refers to the pattern of mutual influences between elements of a complex system that maintains the “coherence and stability of the higher-‐order state.” (Vallacher et al., 2013; p. 61) A related concept is, synchronization. This occurs when individuals within a system begin to align their mental models of the system, which contributes to the construction of a shared reality. For example, “synchronization of different mental models of a conflict …can contribute to the construction of a shared reality regarding a conflict that might otherwise be intractable.” (Vallacher et al., 2013; p. 217)

Dynamical Minimalism

Dynamical minimalism describes the seemingly paradoxical insight, from the nonlinear dynamical systems perspective, that extremely complex phenomena can be understood based on a small number of simple rules.



Dynamical Systems (Time and Space)

“A dynamical system [italics added] is defined as a set of inter-‐connected elements (such as beliefs, feelings, and behaviors) that change and evolve over time in accordance with simple rules. A change in each element depends on influences from other elements. Due to these mutual influences, the system as a whole evolves in time. [For example], the effects resulting from changes in any element of a conflict (such as level of hostilities), depends on rules-‐based influences of various other elements (each person’s motives, attitudes, actions, etc.), which evolve over time to affect the general pattern of interactions (positive or negative) of the disputants.” (Coleman, in press; p. 9)

Emergence Emergence refers to the observation that “the properties of the whole system are often quite different from the properties of its parts. This is widely recognized in the physical sciences. For example, hydrogen and oxygen together are an explosive mixture of gasses, but water—which represents the interaction of hydrogen and oxygen—is stable and wet. Examples of emergence abound as well in the social sciences. For example, individually peaceful people can assemble into a dangerous, violent mob” (Vallacher et al., 2013; p. 11). Or, viewed in another way, “[emergence] simply means that the higher-‐order property or behavior that results from the mutual influence among elements cannot be reduced to the properties of the elements.” (Vallacher et al., 2013; p. 60). Burns (2007), from an action research perspective, suggests that emergence is also important when responding to complex social systems. He notes that in addition to understanding systems through the lens of emergence, emergence should also characterize action research design.

Feedback Loops

“Each element [of a dynamical system] can be stimulated and perpetuated along its current path through reinforcing feedback loops [sic] between elements, where one element stimulates another along its current trajectory and this element, in turn, stimulates the first – thus making a loop. We see this when a negative act by an outgroup member links to negative memories and feelings from previous encounters and increase a general sense of animosity toward the outgroup and the likelihood that they will perceive future acts as negative. Elements can also obstruct or reverse one another via inhibiting feedback loops [sic] where one element constrains another…” (Vallacher et al., 2013, p. 121).

Fixed-‐Point Attractor

“A fixed point attractor describes a system in which all trajectories tend to a single point in phase space, regardless of the system’s initial conditions. This means that the set of all dynamical variables converges on some set of time-‐independent constant values corresponding to an equilibrium point for the system. Fixed-‐point attractors may prove useful in describing thoughts and behaviors that tend to a particular set of values over time… despite differences in initial conditions and external factors…” (Nowak & Vallacher, 1998; p. 58-‐9)



Game Theory

Game theory is a theoretical approach to measuring conflicts of interest over time, which emphasizes the interdependent nature of competition. The approach assumes rational decision-‐making, and is most effective at predicting behaviors and outcomes in “zero-‐sum” situations. Overall, research using this approach has established that competition is not the best strategy, and that instead parties that respond in a “tit-‐for-‐tat” manner with a readiness to move to cooperation achieve the best outcomes. The “prisoner’s dilemma” is a well-‐known version of this approach.

Hysteresis

A signature of non-‐linear dynamic systems, hysteresis describes a social phenomenon where, upon the introduction of a stimulus, the initial point at which a non-‐linear increase in a behavior is observed is not the same point for which a decrease is observed following the withdrawal of that stimulus. (Vallacher et al., 2013; p. 92) Simply, hysteresis is the “the tendency for a system to remain at its current attractor” (Coleman et al, 2011).

Internal-‐external Complexity Fit

“External complexity measures the amount of input, information, energy obtained from the environment that the system is capable of handling, processing….Internal complexity measures the complexity of the representation of this input by the system” (Vallacher et al., 2013; p. 71).“The adaptive capacity of complex systems is thought to depend on the match between internal complexity in an organization and the complexity of its environment, which is deemed the law of requisite complexity…” which “…supports adaptation by engaging networks of interacting agents for learning, creativity, and adaptability.” (Lord, Hannah & Jennings, 2010; p. 105) “The aim of the system then is to handle as much input, as many data as possible with as simple a model as possible…Thus, the system will try to increase, to maximize its external complexity, and to reduce, to minimize its internal complexity” (Jost, 2004; p. 71). “Each of these two processes will operate on its own time scale(s), but they are also intricately linked and mutually dependent upon each other.” (Jost, 2004; p. 70)

Initial Conditions

Dynamical systems are extremely sensitive to the initial conditions of the system. Even slight differences in initial conditions can lead to very different outcomes. This is more commonly referred to as the “Butterfly Effect.” (Vallacher et al., 2013; p. 44)

Intrinsic Dynamics

Intrinsic dynamics refer to the internally generated processes that occur within intrapersonal, interpersonal, or even macro-‐societal level systems.

Latent Attractor

With regards to the attractor landscape model, a latent attractor represents “an alternative range of possible behaviors for the system.” (ç p. 107) In other words, latent attractors are possible alternative attractors that the system can shift to following a change in the control parameters, or rules, that describe the system.

Mouse Paradigm

The mouse paradigm is computer program based, dynamic measurement tool designed to record the location of the mouse cursor on the screen second by second. During measurement, constructs are presented on the left, right and (sometimes) center



sections of the screen. Participants, while listening to an audio recording, are asked to indicate their moment-‐to-‐moment reaction to the recording by moving the cursor to the appropriate side of the screen. For example, positive and negative emotions can be recorded dynamically: The participant would move the mouse to the side of the screen displaying marked ‘positive’ when they are reacting positively to what they hear, and to the side marked ‘negative’ when they are having a negative response. The tool can be used to collect both categorical and continuous data.

Multi-‐dimensionality

Multidimensionality refers to situations or systems that are inherently complex and are composed of multiple interacting elements. A related concept is integrative complexity, which is a construct used to describe an individual’s ability to integrate and differentiate within a multidimensional cognitive space – i.e. a complex system.

Multilevel “Fundamental to the levels perspective is the recognition that micro phenomena are embedded in macro contexts and that macro phenomena often emerge through the interaction and dynamics of lower-‐level elements… The macro perspective is rooted in its sociological origins. It assumes that there are substantial regularities in social behavior that transcend the apparent differences among social actors… In contrast, the micro perspective is rooted in psychological origins. It assumes that there are variations in individual behavior, and that a focus on aggregates will mask important individual differences that are meaningful in their own right. Its focus is on variations among individual characteristics that affect individual reactions.” (Kozlowski & Klein, 2001; p. 7) “A levels approach, combining micro and macro perspectives, engenders a more integrated science…” (Kozlowski & Klein, 2001; p. 8)

Networks

“A network (or graph) is simply a collection of nodes (vertices) and links (edges) between nodes. The links can be directed or undirected, and weighted or unweighted. Many—perhaps most—natural phenomena can be usefully described in network terms. The brain is a huge network of neurons linked by synapses. The control of genetic activity in a cell is due to a complex network of genes linked by regulatory proteins. Social communities are networks in which the nodes are people (or organizations of people) between whom there are many different types of possible relationships. The Internet and the World-‐Wide-‐Web are of course two very prominent networks in today’s society” (Mitchell, 2006, p. 1196).

Non-‐linearity

“Linearity refers to proportionality between a source of influence (e.g., a cause) and the resultant change (e.g., the effect). Non-‐linearity [italics added] refers to any other type of influence relation. In a threshold function, for example, a cause has no effect until a particular level of intensity is reached, beyond which the effect appears at full-‐strength. Other examples of non-‐linearity include inverted-‐U functions, in which moderate values of a cause have greater effects than do extreme values of the cause, and U functions, in which both extremes of a cause promote the same extreme effect, while moderate values of the cause produce no (or minimal) effect.” (Dynamics of Conflict: FAQs; retrieved from: http://www.dynamicsofconflict.iccc.edu.pl/index.php?page=faq )



Perturbation

A perturbation represents a significant disruption to a system that can occur either due to changes within the system that “pull parts and actions out of alignment with each other or the environment…” (Gersick, 1994; p. 21) or environmental changes that disrupt the system’s ability to survive. An intervention designed to bring about change in a system would be described as a perturbation.

Phase Transition

A phase transition represents a rapid change in a system that occurs in a non-‐linear manner. Bifurcation and hysteresis are closely related concepts.

Reciprocal Causality

Reciprocal causality is a temporal pattern where the effect of a causal factor in a system subsequently functions as a cause in an unfolding reciprocal process. Over time this process can intensify, diminish in intensity, or follow a more complex course. (Vallacher et al., 2013; p. 9)

Repeller

A repeller, conceptually, is the opposite of an attractor. While an attractor represents a stable equilibrium for a system, a repeller is state of unstable equilibrium that the system attempts to avoid.

Resilience

Resilience is the capacity of an individual to react adaptively to complex, extremely difficult circumstances. Individuals high in resilience demonstrate higher levels of cognitive complexity, emotional complexity, tolerance for contradiction, and openness and uncertainty. In the context of complex social conflicts, resilience can be described as an individual’s capacity to “[maintain] an adaptive course of identity development and a constructive orientation to conflict despite a highly polarized environment” (Coleman and Lowe, 2007, p.382).

Resistance to Perturbation

An open, complex adaptive system – a system capable of changing its structures and functions in response to an environmental change – would be described as resistant to perturbation. Resistance to perturbation can occur in response to environmental stressors, as well as interventions aimed at introducing positive change to the system.

Resonance At points of convergence within social systems there is ‘resonance’ and an increased energy for change. Burns (2007) uses the word ‘resonance’ to mean that • “people ‘see’ and ‘feel’ the connection between things • they ‘know’ that it is related to their experience • they are ‘energised’ and motivated …Resonance enables sense making, and change occurs where there is resonance” (p. 53). Burns (2007) encourages action research facilitators to “design spaces within which resonance can be tested” (p. 54), for example through large events and the collection and analysis of narratives (p. 54). He also suggests that "[r]esonance may be a more useful concept than representativeness for both identifying issues of concern and possibilities for mobilization" (p. 54).



Semantic Networks

A sematic network, also known as a shared reality or mental model, is a pattern of the relations between ideas/concepts representing the shared knowledge and understanding among individuals or groups within a system.

Self-‐Organization

Self-‐organization is an ongoing process where “higher-‐level properties and behaviors emerge from the internal workings of the system” (Vallacher et al., 2013; p. 60). “Local interactions can create large-‐scale patterns. The movement of tiny patches of moist hot air forms a hurricane extending over hundreds of miles. Actions of individual investors create economic bubbles and then burst them. The decisions by a few local Liberian mothers and grandmothers to employ non-‐violent forms of anti-‐war civil-‐ disobedience result in the downfall of the strongman Charles Taylor and the emergence of peace in Liberia” (Vallacher et al., 2013; p. 11). The challenge is to recognize the self-‐organizing and emerging patterns as they evolve and change over time.

Unintended Consequences

Unintended consequences -‐ in complex, dynamical systems, small changes in one place can lead to completely unanticipated results in another part of the system and even across system boundaries. “Small tinkering with, or changing the pieces of a system can lead to surprising and completely unanticipated results. A tree falls on an electrical transmission wire in a forest in the U. S. Midwest and cascading electrical failures put out the lights of tens of million of people in the Northeast. The Internet, originally designed to transfer data files between military computers, leads to on-‐line social networks that mobilize average citizens into toppling a dictatorship” (Vallacher et al., 2013; p. 11-‐12). An intervention in one part of the system can even “affect the ability of the system as a whole to coordinate its activities, thereby disabling the system at another level” (Burns, 2007; p. 29).

Visualization “Conflict maps provide a sketch of the process architecture [sic]… Ultimately… it [is] useful to move from mapping to working with a simple visualization software program to begin to see how the different elements of a conflict interact together over time [sic]. This is critical for focusing our understanding on how the conflict system evolves and establishes temporal patterns or attractors over time.” (p. 155)



References

Burns, D. (2007). Systemic action research: a strategy for whole system change. Bristol: Policy Press. Coleman, Peter T. (unpublished). A dynamical-‐systems model of intractable conflict. Concept paper for

Dynamical Systems Innovation Lab, July 2013. Coleman, Peter T., and J. Krister Lowe (2007). "Conflict, Identity, and Resilience: Negotiating Collective

Identities within the Israeli and Palestinian Diasporas." Conflict Resolution Quarterly 24.4 (2007): 377-‐412.

Gersick, C. J. (1991). Revolutionary change theories: A multilevel exploration of the punctuated

equilibrium paradigm. Academy of Management Review, 16(1) 10-‐36. Jost, J. (2004). External and internal complexity of complex adaptive systems. Theory in Biosciences, 123,

69-‐88. Kozlowski, S. W. J., & Klein, K. J. (2000). A multilevel approach to theory and research in organizations:

Contextual, temporal, and emergent processes. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations. San Francisco: Jossey-‐Bass.

Lord, R. G., Hannah, S. T., & Jennings, P. L. (2011). A framework for understanding leadership and

individual requisite complexity. Organizational Psychology Review, 1(2), 104-‐127. Mitchell, M. (2006). Complex systems: Network thinking. Artificial Intelligence, 170(18), 1194-‐1212. Nowak, A. S., & Vallacher, R. R. (1998). Dynamical social psychology. New York: The Guilford Press. Phelan, S. E. (2001). What is complexity science, really?. Emergence, A Journal of Complexity Issues in

Organizations and Management, 3(1), 120-‐136. Vallacher, R. R., Coleman, P. T., Nowak, A., & Bui-‐Wrzosinska, L. (2010). Rethinking Intractable Conflict.

American Psychologist, 65, 262-‐278. Vallacher, R. R., Coleman, P. T., Nowak, A., Bui-‐Wrzosinska, L., Liebovitch, L., Kugler, K., Bartoli, A.

(2013). Attracted to conflict: Dynamic foundations of destructive social relations. Berlin, Heidleberg: Springer-‐Verlag.

Wolfram, S. (2002). A new kind of science (Vol. 5). Champaign: Wolfram media.

Complex Systems Graphics/Figures 10


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Figure 2: Visual and Organizational Map of Complex Systems

“Visual, organizational map of complex systems broken into seven sub-‐gorups, create by Hiroki Savama, D. Sc.” (http://www.sandia.gov/CasosEngineering/images/Sayama_Complex_systems_organizational_map.png.) Also found in http://commons.wikimedia.org/wiki/File%3AComplex_systems_organizational_map.jpg .



Figure 3: Components of Web Science

Attributed to Sir Nigel Shadbolt, University of Southampton as found at http://intersticia.com/blog/?p=1059)

Figure 4: Ordered, Complex and Random Source: University of Southampton Computational Modeling Group website (http://cmg.soton.ac.uk/research/categories/transdisciplinary/complexity/)



Figure 5: Simple, Complicated and Complex

Source: ODI presentation, Exploring the Science of Complexity of Aid Policy and Practice, London, 09 July 2008. (http://www.slideshare.net/ODI_Webmaster/exploring-‐the-‐science-‐of-‐complexity-‐in-‐aid-‐policy-‐and-‐practice-‐presentation )



Figure 6: Complex Systems, Topics and Tools

“In the figure [above], the left side lists some biological complex systems, and the right side list some example systems from ICT (information and communication technology) that need new approaches to handling complexity. The topics in the centre are examples of subjects that help connect the biological inspiration on the left with the challenges on the right.” (http://www.complexity.ecs.soton.ac.uk/index.php?page=q3)



Figure 7: Obesity Systems Map (http://www.shiftn.com/obesity/Full-‐Map.html )

“The Obesity Systems Map has been developed by shiftN in the context of the Foresight ‘Tackling Obesities-‐Future Choices’ Project (2006).” (http://www.shiftn.com/news/detail/interactive_functionality_obesity_systems_map_restored )

Key Constructs and Figures - DST Innovation Lab...Working(Definitions(for(Key(Constructs( ( 3!Dynamical!Systems!Innovation!Lab,!July!8=12,!2013(( Collapse%of% Multi’ dimensionality%

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