System Research

“[L]everage points” […] are places within a complex system […] where a small shift in one thing can produce big changes in everything.’ (Meadows, 1999:1)

STOCK-AND-FLOW STRUCTURE

Simple systemic flows connected together create complex systems. Flows consist of stocks moving according to set parameters, constants, and numbers. According to Donella Meadows, a system has a stock-and-flow structure. Its stock represents its state, and its flow represents the inflow and outflow that reflect changes in the system’s stock volume. This flow has temporality and depends on the parameters existing in the system. Parameters indicate the rate at which flows increase (inflow) and decrease (outflow) the system’s stock volume. A system may be stable, slow or rapid (imbalanced). Because this stock-and-flow structure entails that the stock volume and stability depends on the rate of the flows (in and out), a system’s stability requires the leveraging of a stock’s buffer capacity so that, if slow, the buffer decreases, and if rapid, the buffer increases. Decreasing or increasing the size of buffer capacity in a system stabilizes and leverages its stock.

The leverage point is in proper design in the first place. After the structure is built, the leverage is in understanding its limitations […] and refraining from fluctuations or expansions that strain its capacity.” (Meadows, 1999:8)

Oscillations in a system result in delays in feedback loops. Short or long delays account for imbalances in a system, describe the rate of changes in the state of the system, and determine the efficiency of its feedback loops. Meadows calls short feedback loops “overreaction” –oscillations that are too short, rapid, and amplified. When speed of changes and size of delays don’t coincide, one sees imbalances in the system.

DRIVING POSITIVE FEEDBACK LOOPS

Long feedback loops, those that create slowness in the system’s responses to action, cause chaos, collapse, and irreversible damage. However, most important to a system’s stability is its growth rate. Changing delays in a system can have drastic implications on the stability of the system –its inflow and outflow dilemma. Complex systems contain negative feedback loops that are responsible for regulating these changes (oscillations).

A delay in a feedback process is critical relative to rates of change in the system state that the feedback loop is trying to control. […] The strength of a negative feedback loop is important relative to the impact it is designed to correct.” (Meadows, 1999:8-10)

Chaos takes place when strong positive loops take over weak negative loops resulting in an unstable system with unpredictable growing rates –a behavior which may cause the system to destroy itself. “Control must involve slowing down the positive feedbacks.” (Meadows, 1999:12) Control, then, involves delaying the positive loops to allow the negative feedback the necessary interval to react and regulate the system.

On the one hand, positive feedback loops in a system are self-reinforcing. With high positive feedback, a system may destroy itself by self-multiplying and causing itself to collapse. On the other hand, negative feedback loops are leverage points in a system where intervention can be fruitful. Adjusting the buffer capacity (delays) and thereby recalibrating stock flows (“emergency response mechanisms”) help the system sustain itself by self-correcting in response to changes and oscillations in feedback loops. Because the strength of impacts and feedback must coincide, when one strengthens a system’s negative feedback, one raises its self-correcting abilities.

Negative feedback loops become regulating sources for reducing and slowing the growth of positive loops by giving it time and delays to recalibrate and stabilize itself.

COMPELLING FEEDBACK

In some cases there may be missing feedback in a system which causes it to malfunction. These instances indicate leverage point opportunities to create a “new loop” in a system (Meadows, 1999:13). Making information salient creates awareness and a bifurcation in one’s relationship to the environment, objects, and/or one’s beliefs, in turn redirecting one’s behavior towards and perception of a system. Turning no feedback into persuasive feedback generates a new systemic loop. However, persuasiveness occurs when information is configured in a meaningful and compelling way (i.e. comparative juxtaposition of selected data reveals another layer of understanding –new loop). New loops generate mass behavioral shifts as they raise the notion of accountability for individual actions and decisions –a paradigm shifter.

SELF-ORGANIZING POWER

[R]ules for self-organization […] govern how, where, and what the system can add onto or subtract from itself under what conditions.” (Meadows, 1999:15)

Self-organizing structures allow a system to change, evolve, and sustain itself as external actors and internal entities affect and impact its systemic structure overtime; thus, developing new response mechanism and enacting new rules and behaviors. Self-organizing rules dictate the emergence of complex adaptive structures and behavioral patterns in a system. These rules help the system deal with unpredictable behavior of external and internal actors, leaving the system open to changing conditions, and variable and open-ended in itself to evolve, adapt, and mutate over time.

TRANSCENDING PARADIGMS

Donella Meadows suggests that transcending paradigms lies in one’s ability and willingness to perceive multiple mindsets where no paradigm is true or right. With this enlightened view, flexible and open-ended paradigms evolve in relation to a system’s variable purpose, goal, or belief.

Source: Meadows, Donella H. “Leverage Points: Places to Intervene in a System.” Sustainability Institute, December, 1999.

Decentralized Computing emerges from the understanding of self-organizing systems in nature. This model, by its implicit plurality and distributed scope, makes use of the behavioral patterns and systemic structures of micro-world organisms to facilitate communication between objects (hardware) and non-objects (software). It allows individual devices to communicate as a unified whole. For Mitchel Resnick (1994) decentralization is crucial to redrafting our images of ourselves and the larger social and environmental system we live in. The decentralized mindset allows our thinking to expand beyond sequential causality and to begin grasping the integral cybernetic layering of our worlds.

When people observe patterns and structures in the world […], they often assume centralized causes where none exist. And when people try to create patterns and structures in the world […], they often impose centralized control where none is needed.” (Resnick, 1994:120)

Unlike the centralized mindset, digitally constituted worlds are comprised of aggregate objects that share a collective behavior while individually navigating space (peer-to-peer), such as leaderless birds will flock and ant colonies will self-organize by means of pheromone transfer. According to Resnick, self-organizing systems (Resnick, 1994:14) emerge from decentralized settings wherein orderly patterns are the result of lower-level randomness (or local interactions) of individual objects and those in their contextual vicinity. The non-prescriptive or non-deterministic nature of systems accounts for the unpredictability of its micro behaviors and the larger forming structure of its collective behavior over time.

Resnick begun his investigation of the dominant centralized mindset in 1982 by questioning: “How can a mind function so effectively and creatively without anyone (or anything) in charge?” (Resnick, 1994) This led him to explore the mysterious beauty of self-organizing and emergent collective organisms and systems in the world; what he has called “massively parallel microworlds”. His research incited people into new ways of thinking of structures and patterns through experiencing the world under a very different lens. His approach took the form of digitally simulated worlds (a series of StarLogo projects) in which objects collectively self-organize by following decentralized rulesets. These use dedicated programming software and behavioral algorithms to generate virtual social objects, neighborhoods, cities, and worlds. In these micro-worlds, no one object is neither a leader nor a maker nor a seed of complex phenomena that form in their collective worlds; and the environment in which objects interact is not a passive entity, but rather is itself an actor affecting the larger structure or system.

It is more intriguing if a complex, orderly pattern arises from interactions among simple, homogeneous objects than if the same pattern arose from interactions among complex, heterogenous objects.” (Resnick, 1994:121)

Clustered interactions emerge from computationally replicating micro-world networks so as to reveal and exploit the range of possibilities present in the complex nature of our social and environmental conditioning. Simulated worlds (real or imaginary) help us rethink our collectively created social worlds and the significance of our individual actions as potentially seedless and non-causal manifestations; that is, as a decentralized system wherein individual behavior relies very much upon local interactions to produce large-scale patterns. Those patterns represent the non-causality of the emergence of self-organizing wholes which continuously fluctuate, evolve, and reorganize.

Source: Resnick, Mitchel. Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds. The MIT Press, USA: 1997.

The term affordance was coined by J. J. Gibson in 1977. He borrowed the word from ‘afford’ to mean: perceived elements in the environment that explain how the environment behaves. Gibson’s affordance includes both the environment and the animal; which is to say that it explains how both environment and animal affect one another’s behavior as they adapt to fluctuations in time and space.

According the Mark Weiser and John Seely Brown (1996), “peripheral information” extends the notion of “affordances” to describe action enabling technologies that are reachable, yet on the periphery of perception and therefore encalming. Designing for calm technology would then mean to provide non-invasive tools or cues for action that encalm while stimulating the senses.

source: Weiser, Mark; & John Seely Brown. “The Coming Age of Calm Technology.” Xerox PARC: October 5, 1996.