Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence
Exposure to a chemical is a critical consideration in the assessment of risk, as it adds real-world context to toxicological information. Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments...
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| Veröffentlicht in: | Journal of exposure science & environmental epidemiology 2020-01, Vol.30 (1), p.184-193 |
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| container_title | Journal of exposure science & environmental epidemiology |
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| creator | Brandon, Namdi Dionisio, Kathie L. Isaacs, Kristin Tornero-Velez, Rogelio Kapraun, Dustin Setzer, R. Woodrow Price, Paul S. |
| description | Exposure to a chemical is a critical consideration in the assessment of risk, as it adds real-world context to toxicological information. Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that simulates longitudinal patterns in human behavior. By basing the ABM upon an artificial intelligence (AI) system, we create agents that mimic human decisions on performing behaviors relevant for determining exposures to chemicals and other stressors. We implement the ABM in a computer program called the Agent-Based Model of Human Activity Patterns (ABMHAP) that predicts the longitudinal patterns for sleeping, eating, commuting, and working. We then show that ABMHAP is capable of simulating behavior over extended periods of time. We propose that this framework, and models based on it, can generate longitudinal human behavior data for use in exposure assessments. |
| doi_str_mv | 10.1038/s41370-018-0052-y |
| format | Article |
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Woodrow ; Price, Paul S.</creator><creatorcontrib>Brandon, Namdi ; Dionisio, Kathie L. ; Isaacs, Kristin ; Tornero-Velez, Rogelio ; Kapraun, Dustin ; Setzer, R. Woodrow ; Price, Paul S.</creatorcontrib><description>Exposure to a chemical is a critical consideration in the assessment of risk, as it adds real-world context to toxicological information. Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that simulates longitudinal patterns in human behavior. By basing the ABM upon an artificial intelligence (AI) system, we create agents that mimic human decisions on performing behaviors relevant for determining exposures to chemicals and other stressors. We implement the ABM in a computer program called the Agent-Based Model of Human Activity Patterns (ABMHAP) that predicts the longitudinal patterns for sleeping, eating, commuting, and working. We then show that ABMHAP is capable of simulating behavior over extended periods of time. 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Woodrow</creatorcontrib><creatorcontrib>Price, Paul S.</creatorcontrib><title>Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence</title><title>Journal of exposure science & environmental epidemiology</title><addtitle>J Expo Sci Environ Epidemiol</addtitle><addtitle>J Expo Sci Environ Epidemiol</addtitle><description>Exposure to a chemical is a critical consideration in the assessment of risk, as it adds real-world context to toxicological information. Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that simulates longitudinal patterns in human behavior. By basing the ABM upon an artificial intelligence (AI) system, we create agents that mimic human decisions on performing behaviors relevant for determining exposures to chemicals and other stressors. 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| ispartof | Journal of exposure science & environmental epidemiology, 2020-01, Vol.30 (1), p.184-193 |
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| language | eng |
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| source | MEDLINE; Springer Nature - Complete Springer Journals |
| subjects | Agent-based models Artificial Intelligence Behavior Commuting Computer simulation Consumer products Environmental Exposure - statistics & numerical data Epidemiology Exposure Human acts Human behavior Humans Indoor environments Medicine Medicine & Public Health Risk assessment Risk Assessment - methods |
| title | Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence |
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