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Appendix C: Assessing Agent-Based Models for Regulatory Applications: Lessons from Energy Analysis--Alan H. Sanstad
Appendix C: Assessing Agent-Based Models for Regulatory Applications: Lessons from Energy Analysis--Alan H. Sanstad
Pages 217-248
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These issues have long been present in social science–based and policyfocused computational modeling; computational energy modeling is an important example. This paper reviews energy modeling from methodological and epistemological perspectives to draw lessons for ABM regarding model validity, the treatment of uncertainty, and criteria for decision makers to apply when considering agent-based models for use in regulation.
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The premise of the present paper is that, notwithstanding technical and methodological differences, energy modeling can provide valuable insights and lessons for ABM with respect to model validity and evaluation, uncertainty quantification, and policy applications. 3 Energy modeling is also an academic field; however, the preponderance of such work deals at least implicitly with policy and regulatory applications rather than constituting "basic" research.
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But the reader should understand that these terms are highly approximate and simply provide a shorthand for discussing the assessment of computational models and their usefulness in applications. Energy models here refers to computational models, based on economic and optimization principles, of energy systems, entire economies with particular detail on energy sectors, or specific energy-using sectors, particularly residential and commercial.
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Energy models are not just widely applied to but have become the predominant analytical methodology for energy policy and regulatory analysis in the United States. Although in this, as in other computational modeling applications, the models are often referred to as "tools," such a characterization understates their role and influence.
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uses several types of policy models for energy regulatory analysis. It uses two CGE models for analyzing environmental policy problems related to energy: the Applied Dynamic Analysis of the Global Economy (ADAGE)
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For example, using a version of the linear programming model mentioned above, the Natural Resources Defense Council developed a policy architecture for electricity CO2 emissions abatement (Lashof et al., 2013)
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models that have considerably different structure from and simpler structure than numerical energy models.12 In the common circumstance in which a range of estimates appears in the literature, the mean estimate is often used. Second, parameter values can be set by "tuning." The most important examples in energy modeling are parameters determining the magnitude of aggregate improvements in energy productivity, which are directly analogous to the labor-productivity parameters that are common in macroeconomic models.
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. Energy models are simplified representations of [the energy system]
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(2001) discuss calibration in considerable detail; although their focus is macroeconomic modeling and applied general equilibrium modeling especially in international trade applications, their observations apply to energy modeling as well (including both computable general equilibrium and other types)
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The reason is that it is rarely the case that the particular set of input parameter values chosen for the underlying model is uniquely justified.17 The evidence often suggests no more than that the appropriate magnitudes of specific parameters, such as substitution elasticities governing economic choices among different commodities, probably fall within a particular range. However, it is customary for modelers to choose, for example, the midpoints of the ranges for the values of the parameters.
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In some instances, the available evidence may support the specific parameter values that are chosen as opposed to suggesting only a range of equally plausible values. If the exact values themselves are known with certainty, sensitivity analysis provides no information, because nearby alues cannot occur.
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. Thus, although still informative, sensitivity analysis is an element of uncertainty quantification.19 Complexity and Validity Although, as noted above, there is an absence of concepts and methods for assessing energy models objectively, contemporary energy-modeling discourse and practice reveal a widely held belief that increasing levels of model detail and complexity yield greater validity or verisimilitude and improved usefulness for policy applications.
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. Those concepts have no well-established analogues in calibration-based computational policy modeling in general (including energy modeling)
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. The purpose of dynamic pricing is to align marginal prices with marginal costs to increase the economic efficiency of electricity markets and the operation of the electric power system.
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Under uniform pricing, that is straightforward. In contrast, at the beginning of a day, a household under dynamic pricing calculates the cost of its desired use and timing for that day under the hourly prices.
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The quantitative measurement of consumer responses to dynamic pricing, including the effects of such a factor measured through detailed econometric (statistical) analysis (see, e.g., B orenstein, 2013, and the references therein)
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They motivate their work on the basis of the importance of better understanding of the determinants of consumers' decisions of whether to adopt dynamic pricing. They argue that, given both the high cost of empirical research on consumer adoption and the special capabilities of ABM for studying it, ABM is not merely an acceptable method but a preferred method for analyzing the adoption problem.
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First, the basic rationale for dynamic pricing is that it will improve the economic efficiency of the electric power system by aligning retail electricity prices with marginal costs of generation, which vary over a 24-hour period. That is in contrast with "green" electricity pricing, which refers to tariffs that electricity consumers voluntarily pay to be supplied by renewable or low-carbon generation sources.
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Second, much of the other work that the authors cite focuses on the adoption of energy-efficient household equipment and on reducing energy use itself. However, dynamic pricing is not itself an "energy-saving" mechanism: Higher prices during hours in which electricity demand is relatively high are intended to reduce demand during these hours, but this may occur through consumers' shifting consumption to off-peak periods rather than reducing their total electricity consumption during a 1-day cycle.22 That contrasts, for example, with a carbon-emission tax, which -- if passed along to electricity consumers in the form of higher retail prices -- would be expected to reduce overall energy consumption.
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(1998) found that volunteers for dynamic pricing were distinguished from nonvolunteers by their understanding of their own electricity use patterns and their belief in their ability to respond effectively to the new rates.
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(Also, as noted, sources of several key parameter values are not given at all.) By using only several or single values of parameters that are identified as having values that could lie anywhere within specified ranges, the K owalska-Pyzalska model embodies unaddressed Knightian uncertainty.
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In this approach, the space of model solutions generated by using an entire set of equally plausible inputs is computationally generated and explored by using machine learning, visualization, and other 24 It is important to point out that in contrast with the situation in energy modeling and social science–based and policy modeling more generally, validation, verification, and uncertainty quantification in computational modeling in the physical and engineering sciences have become active and productive topics of basic and applied research. Oberkampf and Roy (2010)
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This point is taken up in the next section. Lessons for Agent-based modeling and Recommendations for Regulatory Applications In its present stage of development, ABM is a heterogeneous field in many respects, including the level of attention paid among its sub-disciplines to empirical foundations, validation, and uncertainty quantification.
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Thus, like energy modeling, ABM involves a high degree of Knightian uncertainty by virtue of typically not exploring entire plausible input parameter spaces. The characteristic computational intensity of ABM reflects the computational demands of simulating the behavior of agent populations given specific values of key input parameters.
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For all but one, the justification is based partially or wholly on results of sensitivity analysis. These issues related to ABM parameter choice and sensitivity analysis and the problem of Knightian uncertainty in ABM indicate the potential usefulness in ABM of the scenario discovery and robustness approach of Lempert and colleagues (e.g., Lempert, Popper, and Bankes, 2003; Lempert, Bryant, and Bankes, 2008)
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It reviewed energy modeling methods, applications, and epistemology, particularly the issues of calibration and its relationship to Knightian uncertainty, the relationship between complexity and model validity, and questions that arise when computational modeling is used for public decision making. It also analyzed several examples of agent-based energy modeling.
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Environmental effects of energy production and use had been brought to public attention by energy industry experts, scientists, and activists, and the first "oil crisis" sparked by conflict in the Middle East had resulted in extreme concern regarding fuel supplies. "Energy independence" attained high policy and political priority, and one of the first major examples of computational energy modeling during that era was a linear program created for Project Independence, which was initiated by President Nixon in 1973 (Hogan, 1975)
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is a notable exception to the contemporary paucity of attention to energy model validation. It was prepared as background on energy modeling for U.S.
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2013. Effective and equitable adoption of opt-in residential dynamic electricity pricing.
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1975. Energy policy models for Project Independence.
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1980. Validation and assessment issues of energy models: Proceedings of a workshop held at the National Bureau of Standards, Gaithersburg, MD.
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