complex adaptive systems

Often the term ‘complexity science’ is used interchangeably with ‘complexity theory’, ‘complex adaptive systems’ or even just ‘complexity’.

Systems Thinking is an attitude to how one approaches problems/situations that is informed by complexity theory. In this way, complexity becomes a meta-discipline, that can be applied to other disciplines.

from this new perspective, the implication of the systems idea is not that we must understand the whole system, but rather that we critically deal with the fact that we never do - Ulrich

Complexity as a shift in ontology and epistemology from the classical scientific, mechanistic view of nature (ways of looking) to a process-relational worldview (heraclitus, Taoism)

What is a Complex System?

• A system consists of interconnected elements (agents)
  • It’s more than the sum of its parts (agents interact in nonlinear ways)
    • Elements are interconnected and interdependent
    • An element could be its own system with its own purpose, behavior and sub-systems
    • Each element may be changing all the time, making it difficult to predict their behavior or come up with definite solutions
    • immune system as a good example of a system that cannot be pinpointed. it is the result of complex interactions of its elements
  • No central control (self-organizing in a de-centralized way)
  • Unpredictability
    • Emergence
    • Hierarchical organization
    • Information processing
    • Evolution and adaptation
    • will involve surprises
      • more data and better models will not solve all uncertainties
  • Relations (between elements), though intangible, are recognized to have real effects on the system as a whole
• We cannot predict behavior of a system only based on its elements
• Different systems may respond differently to the same outside event
  • You could list elements and sub-elements indefinitely
    • Important to identify the “boundaries” we want to consider for our system analysis

6 Principles of CAS

1. Constituted relationally
   • Complex behavior emerges as a result of patterns of relations among components. Each element influences and is influenced by many others
2. Adaptive, Self-organizing and evolving
   • systems can develop complex structures from unstructured beginnings and without any external intervention
   • Elements coevolve through the mutual interaction and influence on one another
3. Dynamic (non-linear)
   • Constantly changing in rich and unexpected ways
   • Change is the norm
     • shifting the focus from analysing stable states to analysing transient processes
4. Radically Open
   • The environment co-constitutes the identity of teh syste,
   • Cannot clearly discern boundaries
5. Context-dependent
   • changing the context will impact the function of the system
   • “there is no objective position from which to study complex phenomena, as knowledge of CAS is always context sensitive” (Biggs et al., 2021, p. 39) (pdf)
6. Complex causality and emergence
   • non-linear cause-and-effect interactions
   • emergent behavior (system’s effects are different from those of its individual parts)

Complex and Simple Problems

• Problems of simplicity involve a few variables
  • Temperature and pressure, population and time, current resistance and voltage
• Problems of Organized Complexity
  • moderate number of variables show strong nonlinear relationships (as opposed to simple problems)
• Problems of Disorganized Complexity
  • millions/billions of variables show strong nonlinear relationships (as opposed to simple problems)

Examples

• Immune system (cannot be pinpointed to a specific organ)
• Ant armies
  • Individual ant’s behaviors is very simple, but when together they self-organize to produce a complex behavior
• Human brain - composed of simple cells (neurons)
• Networks
  • Financial Networks
  • Food Webs
  • Social Networks

Tools

• Loopy
• Kumu

Heuristics

• Enabling constraints

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Created on: 2020-11-28 Related: Systems Thinking |