The Experts below are selected from a list of 157083 Experts worldwide ranked by ideXlab platform

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, Ellenwien Augustijn, Yaseen T Mustafa, Tatiana Filatova
    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 Agent-Based 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 Agent-Based 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 Agent-Based Model are discussed. We present a spatial disease Agent-Based 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.

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, Ellenwien Augustijn, Yaseen T Mustafa, Tatiana Filatova
    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 Agent-Based 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 Agent-Based 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 Agent-Based Model are discussed. We present a spatial disease Agent-Based 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.

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

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

Marco A Janssen - One of the best experts on this subject based on the ideXlab platform.

  • targeting and timing promotional activities an Agent Based Model for the takeoff of new products
    Journal of Business Research, 2007
    Co-Authors: Sebastiano A Delre, Wander Jager, Tammo H A Bijmolt, Marco A Janssen
    Abstract:

    Many marketing efforts focus on promotional activities that support the launch of new products. Promotional strategies may play a crucial role in the early stages of the product life cycle, and determine to a large extent the diffusion of a new product. This paper proposes an Agent-Based Model to simulate the efficacy of different promotional strategies that support the launch of a product. The article in particular concentrates on the targeting and the timing of the promotions. The results of the simulation experiments indicate that promotional activities highly affect diffusion dynamics. The findings indicate that: (1) the absence of promotional support and/or a wrong timing of the promotions may lead to a failure of product diffusion; (2) the optimal targeting strategy is to address distant, small and cohesive groups of consumers; and (3) the optimal timing of a promotion differs between durable categories (white goods, such as kitchens and laundry machines, versus brown goods, such as TVs and CDs players). These results contribute to the planning and the management of promotional strategies supporting new product launches.

Miao Wang - One of the best experts on this subject based on the ideXlab platform.

  • an Agent Based Model for risk Based flood incident management
    Natural Hazards, 2011
    Co-Authors: Richard Dawson, Roger Peppe, Miao Wang
    Abstract:

    Effective flood incident management (FIM) requires successful operation of complex, interacting human and technological systems. A dynamic Agent-Based Model of FIM processes has been developed to provide new insights which can be used for policy analysis and other practical applications. The Model integrates remotely sensed information on topography, buildings and road networks with empirical survey data to fit characteristics of specific communities. The multiAgent simulation has been coupled with a hydrodynamic Model to estimate the vulnerability of individuals to flooding under different storm surge conditions, defence breach scenarios, flood warning times and evacuation strategies. A case study in the coastal town of Towyn in the United Kingdom has demonstrated the capacity of the Model to analyse the risks of flooding to people, support flood emergency planning and appraise the benefits of flood incident management measures. Copyright Springer Science+Business Media B.V. 2011

  • An Agent-Based Model for risk-Based flood incident management
    Natural Hazards, 2011
    Co-Authors: Richard J. Dawson, Roger Peppe, Miao Wang
    Abstract:

    Effective flood incident management (FIM) requires successful operation of complex, interacting human and technological systems. A dynamic Agent-Based Model of FIM processes has been developed to provide new insights which can be used for policy analysis and other practical applications. The Model integrates remotely sensed information on topography, buildings and road networks with empirical survey data to fit characteristics of specific communities. The multiAgent simulation has been coupled with a hydrodynamic Model to estimate the vulnerability of individuals to flooding under different storm surge conditions, defence breach scenarios, flood warning times and evacuation strategies. A case study in the coastal town of Towyn in the United Kingdom has demonstrated the capacity of the Model to analyse the risks of flooding to people, support flood emergency planning and appraise the benefits of flood incident management measures.