Journal on Policy and Complex Systems
For the purpose of this analysis , the term “ theory ” will be used interchangeably with “ map ” and “ model ” because each may be understood as a collection of related propositions that are used to understand and engage the world ( Wallis , 2014a ). A theory may be represented by text , as in an academic publication describing how a social or political power system “ works ” or as a diagram showing a number of boxes or bubbles ( for concepts / variables ) connected by arrows ( representing causal relationships ). A “ better ” theory is expected to be more useful for understanding and enacting change .
2.0 Methodology and Data
IPA ( Integrative Propositional Analysis ) is the methodology for this research , while the theories , as previously described , serve as the data .
2.1 Method
Because human cognition includes abstract concepts , Craik ( 1943 ) surfaced the idea that we hold “ mental models ” representing our understanding of the world . Kelly ( 1955 ) suggested that those models must have some kind of structure , although those structures may be inconsistent ( Lane , 1992a ). Integrative Complexity ( IC ) was developed to evaluate the structure of mental models based on texts such as correspondence and public speeches . IC found that individuals , teams , and organizations were more likely to be successful when their models were more complex and interconnected
( cf . Curseu , Schalk , & Schruijer , 2010 ; Raphael , 1982 ; Wong , Ormiston , & Tetlock , 2011 ). Or , from another perspective , because such models allow for more successful planning and decision-making .
For the analysis of academic theories , IPA has been developed and applied to rigorously and objectively analyze the structure of theories from multiple fields including systems thinking ( Wallis , 2008a , 2009a ), social entrepreneurship , organizational learning , psychology , sociology , ethics , and management . This logical / rational process involves the following steps ( Wallis , 2016 , p . 585 ):
1 . Identify propositions within one or more conceptual systems ( models , etc .).
2 . Diagram those propositions with one box for each concept and arrows indicating directions of causal effects .
3 . Find linkages between causal concepts and resultant concepts between all propositions .
4 . Identify the total number of concepts ( to find the Complexity ).
5 . Identify concatenated concepts .
6 . Divide the number of concatenated concepts by the total number of concepts in the model ( to find the Systemicity ).
For a very simple example , consider Figure 1 .
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