Simulating Heterogeneous Farmer Behaviors
Introduction and Literature Review
Non-point source ( NPS ) pollution in water systems mainly comes from rainfall and snowmelt that move over and through the ground , bringing natural and human-made pollutants into waterbodies . NPS pollution , which comes mostly from nutrients and chemicals carried by agricultural runoff , is the primary cause of water pollution in the United States today . Unfortunately , regulation and remediation of NPS water pollution is a difficult task . It typically is hard and at times impossible to identify individual contributors to such pollution , and policies designed to address it must be designed to take polluters ’ hidden actions and asymmetric information into account . The cost of this type of individual monitoring and enforcement is often prohibitive ( Xepapadeas , 2011 ).
Theoretical work ( Segerson , 1988 ; Xepapadeas , 1992 ) has shown that policies based on ambient levels of pollution can lead to reductions of NPS pollution to a regulator-specified target level . However , since no program has implemented an ambient-pollution-based policy on a large scale to provide empirical data , researchers have often turned to economic experiment laboratory settings as test beds for such policies ( Miao et al ., 2016 ; Poe , Schulze , Segerson , Suter , & Vossler , 2004 ; Spraggon , 2002 ; Suter , Vossler , & Poe , 2009 ). In addition , since researchers must recruit and compensate participants in economic experiments , the experiments generally have been limited in scale and have restricted the ability to draw conclusions in contexts outside the laboratory . Thus , researchers have been interested in finding other ways to study the effects of these policies as part of efforts to improve their outcomes in terms of reducing NPS pollution .
Agent-based modeling ( ABM ) can help fill this gap by providing a mechanism for scaling up the findings in experiments to contexts that are closer to reality . With ABM , researchers can use findings from an experiment to create model agents that behave according to patterns identified in the experiment , and conduct simulations using an environment that better mimics a real-world setting . ABM also allows the researcher to observe the results of those agent interactions , which are extremely difficult to capture using other methods . Furthermore , we compared to traditional top-bottom methods such as econometric techniques , ABM imposes less distributional restrictions or assumptions .
ABM has been applied in various fields in recent years ( Farmer & Foley , 2009 ), such as ecological modeling ( Grimm & Railsback , 2005 ), population growth ( Axtell et al ., 2002 ), business strategies ( Khouja , Hadzikadic , & Zaffar , 2008 ), landuse policy ( Tsai et al ., 2015 ), transportation policy ( Zia & Koliba , 2015 ), and education ( Johnson , Lemasters , & Bhattacharyya , 2017 ). In the context of agricultural and environmental applications , it has been used mainly for problems associated with changes in land cover to develop models that simulate land-use decisions by farmers facing multiple constraints ( Matthews , Gilbert , Roach , Polhill , & Gotts , 2007 ; Veldkamp & Verburg , 2004 ), especially in studying coupled human and natural systems ( An , 2012 ). In such systems , agent decisions generate environmental consequences , which could in turn affect human decisions and behavior . Recently , Tesfatsion , Rehmann , Cardoso , Jie , and Gutowski ( 2017 ) developed the Water and Climate Change Watershed ( WACCShed ) platform that allows the systematic study of interactions of hydrology , human , and climate in a watershed over time . Ng , Eheart , Cai , and Braden ( 2011 )
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