AEGIS: Agent-based Evaluation Guiding Implementation of Solar

DE-FOA-0001496: SOLAR ENERGY EVOLUTION AND DIFFUSION STUDIES II – STATE ENERGY STRATEGIES (SEEDSII-SES)

Business contact: Mr. Vijay Kohli President Syntek Technologies 703.522.1025 ext. 201 vkohli@syntek.org

Technical contact: Jonathan A. Morell, Ph.D. Director of Evaluation Syntek Technologies 734 646-8622 jmorell@syntek.org

Confidentiality statement:This proposal includes information and data that shall not be disclosed outside the Government and shall not be duplicated, used, or disclosed – in whole or in part – for any purpose other than to evaluate this proposal. Howev-er, if a contract is awarded to this participant as a result of – or in connection with – the submission of this information and data, the Government shall have the right to duplicate, use, or disclose the data to the extent provided in the resulting contract. This restriction does not limit the Government’s right to use information contained in these data if they are obtained from another source without restriction. The entirety of this proposal is subject to this restriction.

Introduction

AEGIS (Agent-based Evaluation Guiding Implementation of Solar) demonstrates a novel approach to doing program evaluation: combining agent-based modeling with traditional program evaluation, and doing so continually, as the evaluation work unfolds. We propose to test the value of this approach for evaluating programs that promote the goals of SEEDS II, Topic 1, specifically, “Development of new approaches to analyze and understand solar diffusion and solar technology evolution; developing and utilizing the significant solar data resources that are available; improvement in applied research program evaluation and portfolio analysis for solar technologies leading to clearer attribution and identification of successes and trends.”

The field of evaluation has historically fallen short in providing the conceptual understanding and instrumental knowledge that policy makers and planners need to design better programs, or to identify and measure impact. Our hypothesis, supported by our work to date, is that agent-based modeling can improve the quality and contribution of evaluation. Specifically, we will increase stakeholder involvement and the adoption of evaluation recommendations. We propose to apply and evaluate our approach on programs that are designed to reduce the soft costs of solar deployment and to overcome barriers to diffusion, commercialization, and acceptance.

Scientific Justification and Work to Date

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Over the past year or so I have been thinking a lot about novel methodologies and approaches to help evaluators understand unexpected program behaviors. This is part of my general view that it’s possible to increase the lead time between when indicators of unexpected change first pop up, and when the need arises to adjust evaluation methodologies. And, I believe that the longer the lead time, the better the adjustments.

One result of my ponderings has been the idea that agent based modeling (ABM) should be tightly integrated into traditional program evaluation methods. ABM approaches are desirable for a host of reasons, not the least of which is that they are based on the principles of complex adaptive systems (CAS). Because of this connection, combining ABM and traditional evaluation has two advantages. First, it will help with the “unexpected behavior” problem. Second, it can help shift the way in which CAS is presently used in our business.
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