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Validation of Agent-Based Simulation through Human Computation: An Example of Crowd Simulation

机译：通过人工计算验证基于代理的仿真：人群仿真的示例

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Agent-based modeling as a methodology for understanding natural phenomena is becoming increasingly popular in many disciplines of scientific research. Validation is still a significant problem for agent based modelers and while various validation methodologies have been proposed, none have been widely adopted. Data plays a key role in the validation of any simulation system, typically large amounts of observable real world data are necessary to compare with model outputs. However, the complex nature of the studied natural systems will often make data collection difficult. This is certainly true for crowd and egress simulation, where data is limited and difficult to collect. In this paper we propose a new technique for validation of agent-based models, particularly those which relate to human behavior. This methodology adopts ideas from the field of Human Computation as a means of collecting large amounts of contextual behavioral data. The key principle is to use games as a means of framing behavioral questions to try and capture people's nat ural and instinctive decisions. We outline some key design challenges for such games and present one example game in the form of Escape. Escape is an egress based game where people are tasked to escape from rooms inhabited by other people. We show some preliminary studies which high light some interesting applications of the game in addressing validation of behavioral based crowd and egress simulation.
机译：基于代理的建模作为一种了解自然现象的方法在许多科学研究领域中正变得越来越流行。对于基于代理的建模者，验证仍然是一个重要的问题，尽管已经提出了各种验证方法，但尚未广泛采用。数据在任何仿真系统的验证中都起着关键作用，通常需要大量可观察的真实世界数据才能与模型输出进行比较。但是，所研究的自然系统的复杂性质通常会使数据收集变得困难。对于数据有限且难以收集的人群和出口模拟，这确实是正确的。在本文中，我们提出了一种新的技术来验证基于代理的模型，尤其是那些与人类行为有关的模型。这种方法采用了人类计算领域的思想作为收集大量上下文行为数据的手段。关键原则是将游戏用作解决行为问题的一种方法，以尝试捕捉人们的自然和本能决策。我们概述了此类游戏的一些主要设计挑战，并以Escape的形式展示了一个示例游戏。逃生游戏是一款基于出口的游戏，人们被要求从其他人居住的房间逃脱。我们展示了一些初步研究，这些研究突出了游戏在解决基于行为的人群和出口模拟的验证方面的一些有趣应用。

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