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AbstractComplex systems exhibit dynamics across multiple timescales. Strong couplings across timescales can be important, but common practice has been to ignore the couplings. This paper describes my experience modeling electric power systems when clients argued for models including both fast and slow dynamics. Their goals were realism, transparency for policymakers and fast simulations to enable policy design and sensitivity analysis in live workshop settings. I describe different methods developed to meet these needs using five modeling studies conducted over three decades. The studies focused on important policy issues, which are described for context. I summarize the strengths and weaknesses of each method, with emphasis on their applicability beyond the power industry. The paper concludes with an endorsement of a coupled system with a stand‐alone model of fast dynamics to support the design of the long‐term model. The appendices provide details to help those interested in applying the methods.Copyright © 2018 System Dynamics Society

AbstractSystem dynamics is used to simulate the impact of feebates to encourage the sale of cleaner vehicles in southern California. With a feebate system, fees would be imposed on dirty vehicles to finance rebates for clean vehicles. The model is used to learn whether the State of California could operate a feebate system in a financially prudent manner. I consider an example in which fees are imposed on conventional vehicles, and rebates are used to encourage the sale of electric vehicles. The simulations show that feebates can be controlled despite the many uncertainties in predicting the future sales of both conventional and electric vehicles.

Following its decision in Furman v. Georgia, the Supreme Court of the United States has attempted to standardize procedures that states use to subject offenders to the ultimate penalty. In practice, this attempt at standardization has divided capital sentencing into two distinct parts: the death eligibility decision and the death selection decision. The eligibility decision addresses whether the sentencer may impose the death penalty, while the selection decision determines who among that limited subset of eligible offenders is sentenced to death. In Ring v. Arizona, the Court held for the first time that the Sixth Amendment right to a jury trial requires a jury to decide each fact necessary to justify a death sentence. The Court re-affirmed Ring in Hurst v. Florida. The Court, however, has never explicitly clarified the proper role of judges in capital sentencing beyond death eligibility. This Comment takes the position that the Court's decisions in Ring and Hurst are narrow and only implicate the eligibility decision. This Comment examines the history of the Court's modern capital punishment jurisprudence relating to jury sentencing and relevant non-capital cases that implicate the Sixth Amendment right to a jury trial. Further, this Comment argues that judicial sentencing creates a capital sentencing structure that is fairer, more uniform, and more harmonious with the public policy rationales for capital punishment than sentencing by a jury. Finally, this Comment argues that, because juries play a valuable role in democratizing the law, there should still be a role for juries beyond the constitutional minimum requirement of a jury finding aggravating circumstances or convicting a defendant of a capital felony.

AbstractThis paper describes a pragmatic method of searching for the key inputs to a system dynamics model. This analysis is known as screening. The goal is to learn which of the many uncertain inputs stand out as most influential. The method is implemented with readily available software and relies on the simple correlation coefficient to indicate the relative importance of model inputs at different times in the simulation. The screening is demonstrated with two examples with step‐by‐step instructions. The paper recommends that screening analysis be used in an iterative process of screening and model expansion to arrive at tolerance intervals on model results. The appendices compare screening analysis with analytical methods to identify the key inputs to system dynamics models. Copyright © 2005 John Wiley & Sons, Ltd.

AbstractThe Bonneville Power Administration uses system dynamics to assist in its analysis of conservation policies for the Pacific Northwest electric system. This article describes the system dynamics models—the Conservation Policy Analysis Models—and explains why they are useful. After briefly describing model design and assumptions, the article gives examples of model application. One example involves simulation studies of a hypothetical policy by contractors. The second example involves Bonneville's own study of policies under consideration in the 1986 resource strategy. Key features that have made the models useful at Bonneville are noted.

AbstractThis paper provides an introduction to the special issue on environmental and resource systems. The quantity and quality of previous system dynamics publications related to environmental and resource systems are briefly outlined. The background to the special issue is provided, together with a summary and comparison of the five papers and models in the issue. The papers relate to forestry in Indonesia, irrigated lands in Spain, renewable resource management in Norway, wildlife management in USA and blue‐green algae bloom in the coastal waters of Australia. Copyright © 2004 John Wiley & Sons, Ltd.

AbstractSystem dynamics models are often constructed to improve system performance by identifying and modifying feedback mechanisms that drive system behavior. Once identified, these feedback mechanisms can be used to design and test policies for system performance improvement. A preliminary step in developing policies is the identification of high‐leverage parameters and structures, the influential model sections that drive system behavior. The current work clarifies and extends the use of statistical screening as a tool to improve model understanding, explanation, and development with a six‐step process. Statistical screening adds rigor to model analysis by objectively identifying high‐leverage model parameters and structures for further analysis. Statistical screening offers system dynamicists a user‐friendly tool that can be used to help explain how model structure drives behavior. Copyright © 2009 John Wiley & Sons, Ltd.