Journal on Policy & Complex Systems Volume 5, Number 2, Fall 2019 | Page 133

Journal on Policy and Complex Systems
pothesis formulation and are consistent with a critical realist framework within which complex dynamics are examined . The partial nature of knowledge and causes means that any given phenomena can be scrutinized empirically from a variety of non-exclusive angles ( Mir & Watson , 2001 ). Fragmented or incomplete knowledge is important and is the primary means by which we understand the dynamics of physical and social worlds .
The variety and nature of human social interactions in high density locations like cities are extremely complex and new forms of synthetic research design and analysis will undoubtedly lead to new insights about these phenomena ( Dandekar , 2005 ).
7 . Spatial Data
Figure 6

GPS data provides a concrete

linkage between human behavior and survey data ( social capital ) that can be analyzed using network science . This is consistent with our understanding of complex systems in the context of a critical realist philosophical framework .
Recording the movement patterns of humans in their home territories provides an opportunity to examine how individual agency interacts with constraining structures related to geography , income , education , institutional experience , and a range of other perceptions . Knowledge can be legitimate as both homeostasis ( stationary stability ) and homeorhesis ( evolutionary stability ) ( Sieweke , 2014 ), which has two direct implications for designing spatial data collection methods that explore complex social capital phenomena .
First , it means that it is possible to learn from other phenomena that are better understood than the one in question . We understand a complex phenomenon by using a known measurement method that corresponds to it . This learning is facilitated through an interplay of deduction , induction , abduction ( Hintikka , 1998 ), and replication ( Mir & Watson , 2001 ). Deduction is most suited to settings where defined logical relationships between all variables are possible , such as in mathematics , controlled laboratory settings , formalized philosophical argumentation — contexts where we are filling gaps in knowledge within a wellknown space ( Carnap , 1970 ). Induction is better suited to the exploration of phenomena that are not as well-known as it better serves an exploratory mode where understanding ( generalization ) grows through observation of instances and patterns ( Chomsky , 1996 ). Proceeding in this way , explanations may be partial , but useful , and need not require direct cause and effect conclu-
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