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Agent Based Model

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Tatiana Filatova – One of the best experts on this subject based on the ideXlab platform.

  • intelligent judgements over health risks in a spatial Agent Based Model
    International Journal of Health Geographics, 2018
    Co-Authors: Shaheen A Abdulkareem, Tatiana Filatova, Ellenwien Augustijn, Yaseen T Mustafa

    Abstract:

    Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among Agents and their environment by means of AgentBased Models. Yet, current disease Models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend AgentBased disease Models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of Agents in a spatial disease AgentBased Model are discussed. We present a spatial disease AgentBased Model (ABM) with Agents’ behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent Agents are compared to those produced by a population of intelligent Agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-Based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior). We run three experiments (zero-intelligent Agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of several macro metrics of interest: an epidemic curve, a risk perception curve, and a distribution of different types of coping strategies over time. Our results emphasize the importance of integrating behavioral aspects of decision making under risk into spatial disease ABMs using machine learning algorithms. This is especially relevant when studying cumulative impacts of behavioral changes and possible intervention strategies.

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Shaheen A Abdulkareem – One of the best experts on this subject based on the ideXlab platform.

  • intelligent judgements over health risks in a spatial Agent Based Model
    International Journal of Health Geographics, 2018
    Co-Authors: Shaheen A Abdulkareem, Tatiana Filatova, Ellenwien Augustijn, Yaseen T Mustafa

    Abstract:

    Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among Agents and their environment by means of AgentBased Models. Yet, current disease Models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend AgentBased disease Models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of Agents in a spatial disease AgentBased Model are discussed. We present a spatial disease AgentBased Model (ABM) with Agents’ behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent Agents are compared to those produced by a population of intelligent Agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-Based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior). We run three experiments (zero-intelligent Agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of several macro metrics of interest: an epidemic curve, a risk perception curve, and a distribution of different types of coping strategies over time. Our results emphasize the importance of integrating behavioral aspects of decision making under risk into spatial disease ABMs using machine learning algorithms. This is especially relevant when studying cumulative impacts of behavioral changes and possible intervention strategies.

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Qin Bao – One of the best experts on this subject based on the ideXlab platform.

  • Carbon emissions trading scheme exploration in China: A multi-AgentBased Model
    Energy Policy, 2015
    Co-Authors: Ling Tang, Jiaqian Wu, Lean Yu, Qin Bao

    Abstract:

    To develop a low-carbon economy, China launched seven pilot programs for carbon emissions trading (CET) in 2011 and plans to establish a nationwide CET mechanism in 2015. This paper formulated a multi-AgentBased Model to investigate the impacts of different CET designs in order to find the most appropriate one for China. The proposed bottom-up Model includes all main economic Agents in a general equilibrium framework. The simulation results indicate that (1) CET would effectively reduce carbon emissions, with a certain negative impact on the economy, (2) as for allowance allocation, the grandfathering rule is relatively moderate, while the benchmarking rule is more aggressive, (3) as for the carbon price, when the price level in the secondary CET market is regulated to be around RMB 40 per metric ton, a satisfactory emission mitigation effect can be obtained, (4) the penalty rate is suggested to be carefully designed to balance the economy development and mitigation effect, and (5) subsidy policy for energy technology improvement can effectively reduce carbon emissions without an additional negative impact on the economy. The results also indicate that the proposed novel Model is a promising tool for CET policy making and analyses.

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