40
in natural systems, but in human systems, we see “trigger events” that mobilize
action and fundamentally shift the landscape. Trigger events can include, for
instance, major disasters, “viral” cultural moments, key elections, successful terrorist attacks, the opening of a new communication or transportation route, or the
introduction of disruptive technology. For example, a 5-year drought in Syria is
thought to be a trigger event that led, in part, to the Syrian Civil War (see Chap.
20). The dramatic shifts in the energy systems of many countries as a result of the
energy crisis of 1973 is another example.
• Sensitivity to Initial Conditions: How a system evolves is dependent on the
starting conditions of a system. In many cases, the evolution of a system is dramatically different based on small changes in the initial state. The widely known
“butterfly effect” illustrates this point, but is commonly misunderstood. The classic “butterfly effect” is the best example of the importance of initial conditions—
and of chaos in systems; when a butterfly flaps its wings in China, the tiny
alteration in the system’s condition can produce dramatically different weather
in the USA—through a series of amplifying feedback loops and processes in the
atmosphere.
In practice, most chaotic systems tend to fall into one or more relatively stable
states regardless of their initial conditions. The precise state of these systems
cannot be predicted or controlled, but the general “ballpark” state of the system
(the attractor) can be predicted and controlled. The important difference between
projections and predictions was noted above (Sect. 1.5.3). Estimates of future
outcomes (projections or forecasts) are based on specified assumptions relevant
to a question and decision option.
• Complex Adaptive Systems: Inherent in human decision-making, natural evolutionary processes, especially ecosystems, physics, and recently AI-based
machine learning is the ability of complex systems to learn from experience,
experimentation, observation, and study, and to adjust system structure and control to achieve a more preferred or optimal outcome. Often these changes are
thought to occur in pursuit of some optimality principle, such as maximization of
information. Thus, complex systems that connect human and natural biophysical
elements, including many that will be examined in this book, have the additional
attribute of being adaptive. Adaptive systems sense, anticipate, learn, and act;
complex adaptive systems must do these things rapidly and skillfully because
they change so frequently and unpredictably.
The deployment of new technology in all sectors results in a period of learning and adaptation. Changes in how consumers understand FEW commodities
also results in adaptation. For example, a better understanding of the nutritional
value of food products, or household energy consumption, or the environmental
consequences of certain products or practices frequently results in changes in
consumer behavior and adaptations to FEW systems.
• Sentience: Human beings and social organizations are particularly adept at the
invention of new ideas, memes, and values. Sentient behavior can include the
pursuit of idea-based and value-based objectives that appear to be maladaptive
but which nevertheless have a rationale. With sentient agents controlling a system,
P. Saundry and B. L. Ruddell
in natural systems, but in human systems, we see “trigger events” that mobilize
action and fundamentally shift the landscape. Trigger events can include, for
instance, major disasters, “viral” cultural moments, key elections, successful terrorist attacks, the opening of a new communication or transportation route, or the
introduction of disruptive technology. For example, a 5-year drought in Syria is
thought to be a trigger event that led, in part, to the Syrian Civil War (see Chap.
20). The dramatic shifts in the energy systems of many countries as a result of the
energy crisis of 1973 is another example.
• Sensitivity to Initial Conditions: How a system evolves is dependent on the
starting conditions of a system. In many cases, the evolution of a system is dramatically different based on small changes in the initial state. The widely known
“butterfly effect” illustrates this point, but is commonly misunderstood. The classic “butterfly effect” is the best example of the importance of initial conditions—
and of chaos in systems; when a butterfly flaps its wings in China, the tiny
alteration in the system’s condition can produce dramatically different weather
in the USA—through a series of amplifying feedback loops and processes in the
atmosphere.
In practice, most chaotic systems tend to fall into one or more relatively stable
states regardless of their initial conditions. The precise state of these systems
cannot be predicted or controlled, but the general “ballpark” state of the system
(the attractor) can be predicted and controlled. The important difference between
projections and predictions was noted above (Sect. 1.5.3). Estimates of future
outcomes (projections or forecasts) are based on specified assumptions relevant
to a question and decision option.
• Complex Adaptive Systems: Inherent in human decision-making, natural evolutionary processes, especially ecosystems, physics, and recently AI-based
machine learning is the ability of complex systems to learn from experience,
experimentation, observation, and study, and to adjust system structure and control to achieve a more preferred or optimal outcome. Often these changes are
thought to occur in pursuit of some optimality principle, such as maximization of
information. Thus, complex systems that connect human and natural biophysical
elements, including many that will be examined in this book, have the additional
attribute of being adaptive. Adaptive systems sense, anticipate, learn, and act;
complex adaptive systems must do these things rapidly and skillfully because
they change so frequently and unpredictably.
The deployment of new technology in all sectors results in a period of learning and adaptation. Changes in how consumers understand FEW commodities
also results in adaptation. For example, a better understanding of the nutritional
value of food products, or household energy consumption, or the environmental
consequences of certain products or practices frequently results in changes in
consumer behavior and adaptations to FEW systems.
• Sentience: Human beings and social organizations are particularly adept at the
invention of new ideas, memes, and values. Sentient behavior can include the
pursuit of idea-based and value-based objectives that appear to be maladaptive
but which nevertheless have a rationale. With sentient agents controlling a system,
P. Saundry and B. L. Ruddell