Policy and Complex Systems
“ In [ this approach to modeling ], we explicitly follow the basic research program of science : the explanation of observed patterns . Patterns are defining characteristics of a system and often , therefore , indicators of essential underlying processes and structures . Patterns contain information on the internal organization of a system , but in a “ coded ” form . The purpose of [ pattern-oriented modeling ] is to “ decode ” this information … A key idea [ in these models ] is to use multiple patterns observed in real systems to guide design of model structure . Using observed patterns for model design directly ties the model ’ s structure to the internal organization of the real system . We do so by asking : What observed patterns seem to characterize the system and its dynamics , and what variables and processes must be in the model so that these patterns could , in principle , emerge ?” ( p . 987 )
Pattern-oriented approaches are pursued because they help to focus and reduce the uncertainty found in any model of a complex adaptive system . Grimm et al . ( 2005 , p . 990 ) add that ,
“[ Pursuing a pattern-oriented ] strategy is a way to focus on the most essential information about a complex system ’ s internal organization . Multiple patterns keep us from building models that are too simple in structure and mechanism , or too complex and uncertain . Using patterns to test and contrast alternative theories for agent behavior or other low-level processes is a way for [ modelers ] to get beyond clever demonstration models and on to rigorous explanations of how real systems are organized and how they respond to internal and external forces .”
In the literature , classical research methods , such as case studies , interviews , and surveys , have been used for analyzing the functions , capacities , and dynamics of intergovernmental policy implementation networks ( Agranoff , 2007 ; Agranoff & McGuire , 2003 ; Rhodes , 1997 ). While these classical methods are useful in providing insights about analyzing governance networks , we argue in this study that computational approaches , especially “ pattern-oriented ” ABMs , provide a complementary powerful and evidence-based methodology to analyze the allocation of resource flows across intergovernmental policy implementation networks while accommodating for complex interactions of institutional rules , both formal and informal , at multiple levels of government . In particular , ABMs enable the modeling of emergent and selforganization phenomena , as well as lags and inertias that are typically observed in resource allocation decisions across intergovernmental policy implementation networks ( Koliba , Meek , & Zia , 2010 ; Zia & Koliba , 2015 ).
Next , in Section 2 , we briefly describe the federal and state transport policy context of this intergovernmental transportation policy implementation network that governs its dynamic operations in the specific context of intergovernmental transportation project prioritization . In Section 3 , we present research methods that were used to model roadway project prioritization processes and to elicit decision heuristics of multilevel agents in the simulation model . In Section 4 , we present the fundamental structure of the stochastic , multilevel ABM . Section 5 presents findings from experimental simulations to address the two research questions posed above . Section 6 discusses the limitations of the current simulation
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