The Research Colloquium on Computational Social Science/Data Sciences speakers for Friday, October 18, 2019, will be Fahad Aloraini, Computational Social Science PhD student. Fahad’s talk entitled “Modeling Solar-Panel Technology Adoption in Austin: A Test of the Power of Integrating GIS and Cognitive Modeling” will begin at 3:00 in the Center for Social Complexity Suite located on the 3rd floor of Research Hall. The talk will be followed by a Q&A session along with light refreshments.
This session will be live-streamed on the YouTube channel: https://www.youtube.com/channel/UC7YCR-pBTZ_9865orDNVHNA
For announcements regarding this and future streams, please join the CSS/CDS student and alumni Facebook group: https://www.facebook.com/groups/257383120973297/
For a list of upcoming and previous seminars, please visit: https://cos.gmu.edu/cds/calendar/
Abstract: Agent-based models are being created by researchers from increasingly diverse fields. While this wealth of different perspectives enriches the subject of these models, it nevertheless poses problems and challenges such as the depth of knowledge required to construct these models — an aspect where diversity is an issue is agent-decision-making. While there are cognitive architectures that have been validated in lab conditions against human data, they are too computationally expensive to be used for agent-based models with even a moderate number of agents. Moreover, these cognitive architectures do not specify the way to construct the environment and do not leverage the power of agent-based models. As part of my master’s project I have come up with a framework that solves these issues: The Objects Memory Dynamics and Actions framework. The framework moves away from the use of unreliable subjective data (surveys and self-reports) to more objective forms of data (rate of exposure to objects). The framework integrates two cognitive architectures (ACT-R and Fast and Frugal) to include a computationally efficient agent. It specifies the objective data needed for agent learning and decision-making and how to estimate the unique rate at which an agent is exposed to an object in the environment. To test the framework, I model Solar-Panel Technology adoption of households in Austin, Texas, and compare it to work done in the field.