One Theory - Many Formalizations: Testing Different Code Implementations of the Theory of Planned Behaviour in Energy Agent-Based Models
One Theory - Many Formalizations: Testing Different Code Implementations of the Theory of Planned Behaviour in Energy Agent-Based Models
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一种理论 - 多种形式化：测试基于能量代理的模型中计划行为理论的不同代码实现
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
T. Filatova
中科院分区：
文献类型：
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作者：
Hannah Muelder;T. Filatova
随着基于代理的建模越来越受欢迎，对底层建模假设透明度的需求也在增长。行为规则指导代理人的决策，学习，互动和可能的变化，这些应该依赖于坚实的理论和经验的基础。这一领域已经足够成熟，我们需要超越仅仅报告我们基于什么社会理论制定这些规则。许多社会科学理论与各种抽象的结构，如态度，感知，规范或意图。这些概念相当主观，并且在正式的模型代码中操作它们时仍然可以进行解释。越来越多的人担心，建模者如何在定量ABM中解释定性社会科学理论可能会因情况而异。然而，对这些差异的正式测试很少，也缺乏系统的方法来分析任何可能的分歧。我们的论文解决了这一差距，探索的后果变化形式化的一个社会科学理论的模拟结果的代理为基础的模型的同类。我们进行了模拟，以测试四种类型的差异的影响：在一个理论中的特定方程及其序列的模型架构，在影响代理决策的因素，在这些潜在的不同因素的表示，最后在模型中使用的数据的底层分布。我们说明了这些差异的紧急结果，使用基于代理的模型，这是开发来研究家庭的太阳能电池板投资决策的区域影响的例子。计划行为理论是用来定义个体行为规则的最常见的社会科学理论之一。我们的研究结果表明，定性和定量的差异，模拟结果，即使代理的决策规则是基于相同的理论和数据。本文概述了一些关键的方法学的影响，为今后的发展，基于代理的建模。
As agent-based modelling gains popularity, the demand for transparency in underlying modelling assumptions grows. Behavioural rules guiding agents' decisions, learning, interactions and possible changes in these should rely on solid theoretical and empirical grounds. This field has matured enough to reach the point at which we need to go beyond just reporting what social theory we base these rules upon. Many social science theories operate with various abstract constructs such as attitudes, perceptions, norms or intentions. These concepts are rather subjective and remain open to interpretation when operationalizing them in a formal model code. There is a growing concern that how modellers interpret qualitative social science theories in quantitative ABMs may differ from case to case. Yet, formal tests of these differences are scarce, and a systematic approach to analyse any possible disagreements is lacking. Our paper addresses this gap by exploring the consequences of variations in formalizations of one social science theory on the simulation outcomes of agent-based models of the same class. We ran simulations to test the impact of four types of differences: in model architecture concerning specific equations and their sequence within one theory, in factors affecting agents' decisions, in the representation of these potentially different factors, and finally in the underlying distribution of data used in a model. We illustrate emergent outcomes of these differences using the example of an agent-based model, which is developed to study regional impacts of households' solar panel investment decisions. The Theory of Planned Behaviour was applied as one of the most common social science theories used to define behavioural rules of individual agents. Our findings demonstrate qualitative and quantitative differences in the simulation outcomes, even when agents' decision rules are based on the same theory and data. The paper outlines a number of critical methodological implications for future developments in agent-based modelling.