Journal on Policy and Complex Systems
would lead to complete knowledge of the world and ourselves . This encyclopedic aspiration was seen in , for example , statements of some late 19 th century scientists who believed that all that was left for science to discover in their time was decimal places and details ( Badash , 1972 ). Scientific knowledge , like other forms of knowledge , is always partial and can shift significantly with new discoveries , such as has occurred in experiments on entanglement in quantum physics ( Varnava et al ., 2016 ). Our most sophisticated and intensive efforts to explore , experiment , and formalize knowledge have led to a realization that what we know , in both degree and kind , is very partial indeed , even in the most formalized fields of science . Significant causal complexity and the deep interrelatedness of all phenomena suggest that there are in-principle limits , not something that will be remedied by better research or more sophisticated computation . Even where causal assumptions are made or patterns are noted , the nonlinear nature of interacting dynamics results in a temporal reality that is never free of contingencies ( Steinmetz , 1998 ; Taleb , 2010 ). This is something which planning and policy have increasingly recognized as a permanent feature of the dynamic theatres they operate within ( Allmendinger , 2009 , pp . 18-23 ), including in the evaluation of complex phenomena , such as social infrastructure .
3.2 . Historical Knowledge
Second , historical knowledge , including social patterns that we can perceive through statistical data , direct experience , and cultural knowledge , has always been used to guide decisions about the future . From a critical realist perspective , cognitive abilities , such as reason , logic , deduction , induction , and related analytic devices , enable us to synthesize information and improve our judgment . However , historical knowledge , however complete , cannot lead us to predictive certainty ( Reed & Harvey , 1992 , p . 357 ). Prediction premised on causal , linear processes has yielded important knowledge , but much of the world around us — both social and natural — is not linear . Deep contingencies give rise to interconnections of nonlinear interacting systems at all scales and critical realism insists that in any setting ( natural , social , or otherwise ), there are “ constellations of causal factors ” ( Steinmetz , 1998 , p . 172 ). Highly controlled laboratory settings provide the most significant degree of prediction , generating critical knowledge of how causal-mechanical interactions operate , but these settings also have limits ( Feyerabend , 1970 ).
3.3 . Human Agency
Third , critical realism recognizes that one of the logical consequences of partial knowledge ( historical and future ) is that human agency — the ability to act independently of external constraints — is limited . Our expectations about what may be gained in studying social capital phenomena must consider that knowledge gained is always incomplete . Two events or conclusions that appear the same could have different causes . Single causal mechanisms , given the nature of reality , are in principle impossible
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