Browsing by Author "Runge, Michael C"
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Item Open Access A typology of time-scale mismatches and behavioral interventions to diagnose and solve conservation problems.(Conserv Biol, 2016-02) Wilson, Robyn S; Hardisty, David J; Epanchin-Niell, Rebecca S; Runge, Michael C; Cottingham, Kathryn L; Urban, Dean L; Maguire, Lynn A; Hastings, Alan; Mumby, Peter J; Peters, Debra PCEcological systems often operate on time scales significantly longer or shorter than the time scales typical of human decision making, which causes substantial difficulty for conservation and management in socioecological systems. For example, invasive species may move faster than humans can diagnose problems and initiate solutions, and climate systems may exhibit long-term inertia and short-term fluctuations that obscure learning about the efficacy of management efforts in many ecological systems. We adopted a management-decision framework that distinguishes decision makers within public institutions from individual actors within the social system, calls attention to the ways socioecological systems respond to decision makers' actions, and notes institutional learning that accrues from observing these responses. We used this framework, along with insights from bedeviling conservation problems, to create a typology that identifies problematic time-scale mismatches occurring between individual decision makers in public institutions and between individual actors in the social or ecological system. We also considered solutions that involve modifying human perception and behavior at the individual level as a means of resolving these problematic mismatches. The potential solutions are derived from the behavioral economics and psychology literature on temporal challenges in decision making, such as the human tendency to discount future outcomes at irrationally high rates. These solutions range from framing environmental decisions to enhance the salience of long-term consequences, to using structured decision processes that make time scales of actions and consequences more explicit, to structural solutions aimed at altering the consequences of short-sighted behavior to make it less appealing. Additional application of these tools and long-term evaluation measures that assess not just behavioral changes but also associated changes in ecological systems are needed.Item Open Access Multiple models for outbreak decision support in the face of uncertainty.(Proceedings of the National Academy of Sciences of the United States of America, 2023-05) Shea, Katriona; Borchering, Rebecca K; Probert, William JM; Howerton, Emily; Bogich, Tiffany L; Li, Shou-Li; van Panhuis, Willem G; Viboud, Cecile; Aguás, Ricardo; Belov, Artur A; Bhargava, Sanjana H; Cavany, Sean M; Chang, Joshua C; Chen, Cynthia; Chen, Jinghui; Chen, Shi; Chen, YangQuan; Childs, Lauren M; Chow, Carson C; Crooker, Isabel; Del Valle, Sara Y; España, Guido; Fairchild, Geoffrey; Gerkin, Richard C; Germann, Timothy C; Gu, Quanquan; Guan, Xiangyang; Guo, Lihong; Hart, Gregory R; Hladish, Thomas J; Hupert, Nathaniel; Janies, Daniel; Kerr, Cliff C; Klein, Daniel J; Klein, Eili Y; Lin, Gary; Manore, Carrie; Meyers, Lauren Ancel; Mittler, John E; Mu, Kunpeng; Núñez, Rafael C; Oidtman, Rachel J; Pasco, Remy; Pastore Y Piontti, Ana; Paul, Rajib; Pearson, Carl AB; Perdomo, Dianela R; Perkins, T Alex; Pierce, Kelly; Pillai, Alexander N; Rael, Rosalyn Cherie; Rosenfeld, Katherine; Ross, Chrysm Watson; Spencer, Julie A; Stoltzfus, Arlin B; Toh, Kok Ben; Vattikuti, Shashaank; Vespignani, Alessandro; Wang, Lingxiao; White, Lisa J; Xu, Pan; Yang, Yupeng; Yogurtcu, Osman N; Zhang, Weitong; Zhao, Yanting; Zou, Difan; Ferrari, Matthew J; Pannell, David; Tildesley, Michael J; Seifarth, Jack; Johnson, Elyse; Biggerstaff, Matthew; Johansson, Michael A; Slayton, Rachel B; Levander, John D; Stazer, Jeff; Kerr, Jessica; Runge, Michael CPolicymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.