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首页> 外文会议>Annual conference of the International Society of Exposure Science >A framework for the use of agent based modeling to simulate inter- and intra-individual variation in human behaviors
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A framework for the use of agent based modeling to simulate inter- and intra-individual variation in human behaviors

机译：使用基于代理的模型来模拟人类行为的个体间和个体内变化的框架

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Simulation of human behavior in exposure modeling is a complex task. Traditionally, inter-individual variation in human activity has been modeled by drawing from a pool of single day time-activity diaries such as the US EPA Consolidated Human Activity Database (CHAD). Here, an agent-based model (ABM) is used to simulate population distributions of longitudinal patterns of four macro activities (sleeping, eating, working, and commuting) in populations of adults over a period of one year. In this ABM, an individual is modeled as an agent whose movement through time and space is determined by a set of decision rules. The rules are based on the agent having time-varying "needs" that are satisfied by performing actions. Needs are modeled as increasing over time, and taking an action reduces the need. Need-satisfying actions include sleeping (meeting the need for rest), eating (meeting the need for food), and commuting/working (meeting the need for income). Every time an action is completed, the model determines the next action the agent will take based on the magnitude of each of the agent's needs at that point in time. Different activities advertise their ability to satisfy various needs of the agent (such as food to eat or sleeping in a bed or on a couch). The model then chooses the activity that satisfies the greatest of the agent's needs. When multiple actions could address a need, the model will choose the most effective of the actions (bed over the couch). In addition, multiple activities can be linked to a single decision (e.g., commuting must precede and follow working). An agent's needs and the rate at which the needs increase over time are varied across agents and are correlated with the agents' fixed personal attributes (e.g., age, gender, etc.) and household physical characteristics (distance between residence and work). Model parameters such as individuals' rates of need increases are informed using data from CHAD. The advantage of ABM is that, unlike CHAD, it can provide information on human activity over periods of time longer than one day. We will present predictions for a population of adults for the four activities and compare the model outputs to the CHAD data. In future work we propose to extend this "need-based" framework to model usage of consumer products. For example, each agent is assigned personal hygiene and home cleanliness needs which drive their use of personal care products and household cleaning supplies.
机译：在曝光建模中模拟人类行为是一项复杂的任务。传统上，人类活动之间的个体差异是通过收集单日时间活动日志（例如美国EPA综合人类活动数据库（CHAD））来建模的。在这里，基于代理的模型（ABM）用于模拟一年内成年人口中四种宏观活动（睡眠，进食，工作和通勤）的纵向模式的人口分布。在此ABM中，将个人建模为一个代理，其在时空中的移动由一组决策规则确定。规则基于代理具有随时间变化的“需求”，这些“需求”可以通过执行操作来满足。需求被建模为随着时间的推移而增长，采取行动可以减少需求。满足需求的行为包括睡觉（满足休息需求），进餐（满足食物需求）和通勤/工作（满足收入需求）。每次操作完成时，模型都会根据该时间点每个代理商的需求量来确定代理商将要采取的下一个措施。不同的活动表明他们有能力满足代理商的各种需求（例如吃饭或在床上或沙发上睡觉的食物）。然后，模型选择满足代理商最大需求的活动。当多个动作可以满足需求时，模型将选择最有效的动作（躺在沙发上）。另外，可以将多个活动链接到一个决定（例如，上下班必须在工作之前和之后）。代理商的需求和需求随时间增长的速度在代理商之间有所不同，并且与代理商的固定个人属性（例如年龄，性别等）和家庭身体特征（居住和工作之间的距离）相关。使用CHAD的数据来告知模型参数（例如个人的需求增加率）。 ABM的优点是，与CHAD不同，它可以在超过一天的时间内提供有关人类活动的信息。我们将针对这四项活动介绍成年人口的预测，并将模型输出与CHAD数据进行比较。在未来的工作中，我们建议扩展此“基于需求”的框架以对消费产品的使用进行建模。例如，每个代理商都被分配了个人卫生和家庭清洁需求，这推动了他们对个人护理产品和家庭清洁用品的使用。

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