A note on disorganized complexity and organized complexity in Housing Studies research

A note on disorganized complexity and organized complexity in Housing Studies research (information from perplexity.ai)

My question 1: "What are organized complexity and disorganized complexity in systems thinking all about?"

The reply: "Organized complexity in systems thinking refers to systems with a substantial number of interconnected parts that function together as an integrated, purposeful whole. These systems exhibit emergent properties where the collective behavior is more than just the sum of individual parts. Organized complexity is characterized by patterned, non-random interactions among components, requiring system sciences and systems thinking approaches to understand and manage their dynamics effectively.

Disorganized complexity, also known as unorganized complexity, describes systems composed of a vast number of components acting randomly or chaotically, without intentional design or purposeful coordination. The interactions in these systems are unpredictable at the micro-level but show statistical regularities at the macro-level. Such systems are best understood using statistical and probabilistic tools rather than traditional analytical methods.

In summary, the key difference is that organized complexity involves many interconnected parts operating in a structured, purposeful manner leading to emergent behaviors, while disorganized complexity involves numerous parts interacting randomly, with overall behavior analyzed statistically rather than through direct causal relationships."

My question 2: "What research methods are more suitable to study disorganized complexity and what research methods are more suitable to study organized complexity in the context of Housing Studies research?"

The reply: "In Housing Studies research, different research methods are suited to studying disorganized complexity and organized complexity due to the nature of systems involved.

For disorganized complexity, which involves many components interacting randomly or chaotically, research methods that can handle large sets of heterogeneous data and identify statistical patterns are more suitable. These include quantitative approaches such as:

• Statistical analysis

• Large-scale surveys

• Big data analytics

• Probabilistic modeling
  These methods help capture aggregate behaviors and trends across diverse populations or housing environments without focusing on direct causal mechanisms, making them effective for studying complex, unstructured housing challenges like homelessness or social housing dynamics.

For organized complexity, characterized by interconnected components with purposeful relationships and emergent patterns, qualitative and mixed methods are more appropriate. These methods facilitate understanding of the system’s internal interactions and contextual meanings, such as:

• Qualitative case studies

• Participatory research and design methods

• Systems dynamics modeling

• Mixed-methods combining interviews, observations, and surveys
  These approaches support deep exploration of social processes, actor interactions, and policy impacts in housing systems, where structured but varied relationships exist among stakeholders and institutions.

In summary, quantitative and statistically driven methods are generally more effective for studying disorganized complexity in housing systems, while qualitative, participatory, and mixed methods are better suited for unpacking organized complexity. This distinction helps align methodological choices with the complexity characteristics of housing phenomena under investigation."

A collection of blog notes on using chatgpt for research purpose.