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首页> 外文期刊>Current directions in psychological science: a journal of the American Psychological Society >Analytic Versus Computational Cognitive Models: Agent-Based Modeling as a Tool in Cognitive Sciences
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Analytic Versus Computational Cognitive Models: Agent-Based Modeling as a Tool in Cognitive Sciences

机译：分析与计算认知模型：基于代理的建模作为认知科学的工具

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Computational cognitive models typically focus on individual behavior in isolation. Models frequently employ closed-form solutions in which a state of the system can be computed if all parameters and functions are known. However, closed-form models are challenged when used to predict behaviors for dynamic, adaptive, and heterogeneous agents. Such systems are complex and typically cannot be predicted or explained by analytical solutions without application of significant simplifications. In addressing this problem, cognitive and social psychological sciences may profitably use agent-based models, which are widely employed to simulate complex systems. We show that these models can be used to explore how cognitive models scale in social networks to calibrate model parameters, to validate model predictions, and to engender model development. Agent-based models allow for controlled experiments of complex systems and can explore how changes in low-level parameters impact the behavior at a whole-system level. They can test predictions of cognitive models and may function as a bridge between individually and socially oriented models.
机译：计算认知模型通常侧重于单独的单独行为。模型经常采用封闭式解决方案，其中如果已知所有参数和功能，则可以计算系统的状态。然而，当用于预测动态，自适应和异质剂的行为时，封闭式模型受到挑战。这种系统是复杂的，通常不能通过分析解决方案预测或解释的，而无需施加显着的简化。在解决这个问题时，认知和社会心理学科学可以有利地利用基于代理的模型，这些模型被广泛用于模拟复杂系统。我们表明这些模型可用于探索认知模型在社交网络中的规模如何校准模型参数，以验证模型预测，并参考模型开发。基于代理的模型允许复杂系统的受控实验，并可以探讨低级参数的变化如何影响全系统级别的行为。他们可以测试认知模型的预测，并且可以用作单独和社会面向模型之间的桥梁。

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