Counterintuitive behavior of social systems
By J. Forrester. 1971
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Vinodkumar Prabhakaran
- Donald Martin
- Fabian Paischer
- Christian Alexander Steinparz
- Andrew Smart
- Angela Bitto-Nemling
- Thomas Schmied
- Vihang Patil
Citations in the corpus
- Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context (2020, background): “inference and goal-directed counter-factual thinking [28, 110] — that various disciplines refer to as mental models [15, 33, 86].”
- How small system dynamics models can help the public policy process (2011, background): “…if a policy increases the standard of living in an urban area, more people will migrate to the area (a balancing loop), consuming resources (e.g., food, houses, businesses), thereby causing the standard of living to decline and reversing the effects of the original policy (Forrester, 1971a).”
- Work System Theory: Overview of Core Concepts, Extensions, and Challenges for the Future (2013, background): “An initial point of comparison is general systems theory (GST), which includes many widely applicable concepts and observations about systems; for example, Boulding (1964), Forrester (1971), Weinberg (1975), Miller (1978), Churchman (1979), Ackoff (1981), Beer (1981), Checkland (1999).”
- Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning (2022, background): “A necessary component facilitating this capability is our internal model of the world, which stores all necessary information to reliably predict future outcomes (Forrester, 1971).”
- Global water resources modeling with an integrated model of the social-economic-environmental system (2011, background): “Modeling and simulation work in combination: modeling determines structure and clarifies ideas and simulation then reveals unexpected behaviors and clarifies their causes [43].”
- System dynamics modeling for public health: background and opportunities. (2006, background): “Sensitivity testing of a well-built system dynamics model typically reveals that its policy implications are unaffected by changes to most calibration uncertainties.(9,10) But even when some uncertainties are found to affect policy findings, modeling contributes by identifying the few key areas— out”