Learning from Evidence in a Complex World Export

@article{citeulike:828180, abstract = {Policies to promote public health and welfare often fail or worsen the problems they are intended to solve. Evidence-based learning should prevent such policy resistance, but learning in complex systems is often weak and slow. Complexity hinders our ability to discover the delayed and distal impacts of interventions, generating unintended "side effects." Yet learning often fails even when strong evidence is available: common mental models lead to erroneous but self-confirming inferences, allowing harmful beliefs and behaviors to persist and undermining implementation of beneficial policies. Here I show how systems thinking and simulation modeling can help expand the boundaries of our mental models, enhance our ability to generate and learn from evidence, and catalyze effective change in public health and beyond. 10.2105/AJPH.2005.066043}, author = {Sterman, John D.}, citeulike-article-id = {828180}, citeulike-linkout-0 = {http://dx.doi.org/10.2105/AJPH.2005.066043}, citeulike-linkout-1 = {http://www.ajph.org/cgi/content/abstract/96/3/505}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/16449579}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=16449579}, comment = {This is a paper that looks at complex decision making from a medical policy side, but also considers examples from a number of other areas. It notes that due to cognitive failures, biases and the complexity of the environments, decisions often cause unintended problems "today's solutions are tomorrow's problems". It echoes much of what the other papers in this area have said. }, day = {1}, doi = {10.2105/AJPH.2005.066043}, journal = {Am J Public Health}, keywords = {cognition, complex\_decision\_making, feedback, feedback\_delay}, month = {March}, number = {3}, pages = {505--514}, posted-at = {2009-01-22 06:58:11}, priority = {2}, title = {Learning from Evidence in a Complex World}, url = {http://dx.doi.org/10.2105/AJPH.2005.066043}, volume = {96}, year = {2006} }

TY - JOUR ID - citeulike:828180 L3 - citeulike-article-id:828180 N2 - Policies to promote public health and welfare often fail or worsen the problems they are intended to solve. Evidence-based learning should prevent such policy resistance, but learning in complex systems is often weak and slow. Complexity hinders our ability to discover the delayed and distal impacts of interventions, generating unintended "side effects." Yet learning often fails even when strong evidence is available: common mental models lead to erroneous but self-confirming inferences, allowing harmful beliefs and behaviors to persist and undermining implementation of beneficial policies. Here I show how systems thinking and simulation modeling can help expand the boundaries of our mental models, enhance our ability to generate and learn from evidence, and catalyze effective change in public health and beyond. 10.2105/AJPH.2005.066043 IS - 3 JF - Am J Public Health EP - 514 TI - Learning from Evidence in a Complex World VL - 96 SP - 505 KW - cognition KW - complex_decision_making KW - feedback KW - feedback_delay N1 - This is a paper that looks at complex decision making from a medical policy side, but also considers examples from a number of other areas. It notes that due to cognitive failures, biases and the complexity of the environments, decisions often cause unintended problems "today's solutions are tomorrow's problems". It echoes much of what the other papers in this area have said. AU - Sterman, John PY - 2006/03/01/ UR - http://dx.doi.org/10.2105/AJPH.2005.066043 ER -