"What advantages do diagrams have over verbal descriptions in promoting system understanding? First, by providing a diagram, massive amounts of information can be presented more efficiently. A diagram can strip down informational complexity to its core - in this sense, it can result in a parsimonious, minimalist description of a system. Second, a diagram can help us see patterns in information and data that may appear disordered otherwise. For example, a diagram can help us see mechanisms of cause and effect or can illustrate sequence and flow in a complex system. Third, a diagram can result in a less ambiguous description than a verbal description because it forces one to come up with a more structured description." (Robbie T Nakatsu, "Diagrammatic Reasoning in AI", 2010)
"A conceptual model of an interactive application is, in summary: the structure of the application - the objects and their operations, attributes, and relation-ships; an idealized view of the how the application works – the model designers hope users will internalize; the mechanism by which users accomplish the tasks the application is intended to support." (Jeff Johnson & Austin Henderson, "Conceptual Models", 2011)
"Cyberneticists argue that positive feedback may be useful, but it is inherently unstable, capable of causing loss of control and runaway. A higher level of control must therefore be imposed upon any positive feedback mechanism: self-stabilising properties of a negative feedback loop constrain the explosive tendencies of positive feedback. This is the starting point of our journey to explore the role of cybernetics in the control of biological growth. That is the assumption that the evolution of self-limitation has been an absolute necessity for life forms with exponential growth." (Tony Stebbing, "A Cybernetic View of Biological Growth: The Maia Hypothesis", 2011)
"Stated loosely, models are simplified, idealized and approximate representations of the structure, mechanism and behavior of real-world systems. From the standpoint of set-theoretic model theory, a mathematical model of a target system is specified by a nonempty set - called the model’s domain, endowed with some operations and relations, delineated by suitable axioms and intended empirical interpretation." (Zoltan Domotor, "Mathematical Models in Philosophy of Science" [Mathematics of Complexity and Dynamical Systems, 2012])
"Systems subjected to randomness - and unpredictability - build a mechanism beyond the robust to opportunistically reinvent themselves each generation, with a continuous change of population and species." (Nassim N Taleb, "Antifragile: Things that gain from disorder", 2012)
"When some systems are stuck in a dangerous impasse, randomness and only randomness can unlock them and set them free. You can see here that absence of randomness equals guaranteed death. The idea of injecting random noise into a system to improve its functioning has been applied across fields. By a mechanism called stochastic resonance, adding random noise to the background makes you hear the sounds (say, music) with more accuracy." (Nassim N Taleb, "Antifragile: Things that gain from disorder", 2012)
"Ants exhibit a 'neuron-like' behavior insofar as inactive ants have a low propensity to become spontaneously active, but can become excited by other ants with whom they come into contact. [...] Conversely, ants are prone to lapse back into inactivity if their activation is not sufficiently reinforced, and even exhibit a short refractory period (similar to neurons) before they can be reactivated – a mechanism which keeps the swarm from getting permanently 'locked' into an excitatory state." (Georg Theiner & John Sutton, "The collaborative emergence of group cognition", 2014)
[complex system:] "The occurrence of new phenomena generated unpredictably by the interaction of simple rules and individual mechanisms that are in constant flux and interaction. Emergence suggests something novel is perpetually emerging at a systems/global level as the world and environment constantly shifts and changes at a mechanistic/local level." (Kathy Sanford & Tim Hopper, "Digital Media in the Classroom: Emergent Perspectives for 21st Century Learners", Handbook of Research on Digital Media and Creative Technologies, 2015)
"The work around the complex systems map supported a concentration on causal mechanisms. This enabled poor system responses to be diagnosed as the unanticipated effects of previous policies as well as identification of the drivers of the sector. Understanding the feedback mechanisms in play then allowed experimentation with possible future policies and the creation of a coherent and mutually supporting package of recommendations for change." (David C Lane et al, "Blending systems thinking approaches for organisational analysis: reviewing child protection", 2015)
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