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Responsive Governance and Harmful Microbial Blooms on Lake Erie: An ABM Approach
Author(s) -
D. G. Webster,
Tyler Pavlovich
Publication year - 2019
Publication title -
complexity governance and networks
Language(s) - English
Resource type - Journals
eISSN - 2214-3009
pISSN - 2214-2991
DOI - 10.20377/cgn-72
Subject(s) - corporate governance , politics , process (computing) , watershed , government (linguistics) , algal bloom , action (physics) , environmental resource management , perception , metapopulation , political science , computer science , business , ecology , economics , sociology , biology , phytoplankton , law , population , philosophy , nutrient , biological dispersal , linguistics , operating system , quantum mechanics , machine learning , physics , demography , finance , neuroscience
In general, decision makers tend to respond to problems rather than prevent them. In political science, this process of responsive governance is associated with complex dynamics such as availability cascades and punctuated equilibrium. However, most authors treat problems as one-time events, like oil spills or political scandals. Here, we use an agent based model loosely based on the Lake Erie watershed to explore how responsive governance evolves along with an on-going but noisy environmental problem: harmful microbial blooms. This conceptual model features a two-level decision process based on Jones and Baumgartner (2005). Meta-agents representing the individual level of analysis “perceive” blooms either directly via observation if they are near a bloom or indirectly through the media. As a meta-agent observes more blooms, their concern increases until it crosses an action threshold, at which point they use simple cost-benefit analysis to select from a range of options. One of these options is to send a signal to their policy agent, which aggregates these political signals based on a range of assumptions and then decides on actions in much the same way as the metapopulations themselves. We examine two major scenarios, one in which there is a single policy maker managing the entire region (e.g. the national government) and one where there are 5 policy makers, each separately regulating a demographically and geographically distinct region. Although the model is relatively simple, it lets us explore how variability in risk perception and responsive governance shape the functioning of the entire coupled human and natural system, including biophysical feedbacks.

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