Sensitivity analysis of agent-based models: a new protocol

Abstract

Agent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such critiques, we propose a systematic approach to conducting sensitivity analyses of ABMs. Our approach deals with a feature that can complicate sensitivity analyses: most ABMs include important non-parametric elements, while most sensitivity analysis methods are designed for parametric elements only. The approach moves from charting out the elements of an ABM through identifying the goal of the sensitivity analysis to specifying a method for the analysis. We focus on four common goals of sensitivity analysis: determining whether results are robust, which elements have the greatest impact on outcomes, how elements interact to shape outcomes, and which direction outcomes move when elements change. For the first three goals, we suggest a combination of randomized finite change indices calculation through a factorial design. For direction of change, we propose a modification of individual conditional expectation (ICE) plots to account for the stochastic nature of the ABM response. We illustrate our approach using the Garbage Can Model, a classic ABM that examines how organizations make decisions.

Evaluating Policies to Decrease the Risk of Introducing SARS-CoV-2 Infections to Nursing Home Facilities

“…Finally, like all simulation studies, this analysis is subject to the inherent biases and limitations related to the sources used to parameterize the model. A formal probabilistic sensitivity analysis was not performed as methodological guidance for how to conduct PSA for agent-based models is still in development (Borgonovo et al, 2022; Ten Broeke et al, 2016). Therefore, parameters with greater uncertainty such as the probability of visiting a nursing home while experiencing mild symptoms of COVID-19 or the number of nursing home residents attended to by a single healthcare worker may not be fully accounted for within the main analyses.…”

Analyze the Behavioral Foundation of Stylized Facts Using Agent-Based Simulation and STGP Algorithm

“…Although the EMH assumes that the market is composed of agents with rational expectations and thus explains the absence of frequent auto-correlation, it fails to explain other capital market anomalies (Higachi et al, 2018). Dieci and He (2018) state that "economic and financial theories are changing the paradigm transfer from the investor with rational expectations to the investor with bounded rational and heterogeneous expectations" (Higachi et al, 2018;Borgonovo et al, 2022;Alfarano et al, 2005).…”

“…The agent-based models are particularly suitable for explaining bounded rationality, the adaptiveness of interacting agents (Dhesi et al, 2021), and outof-equilibrium phenomena (Wang et al, 2018;Lin, 2018). The most important feature of the agent-based model is that the phenomena occur at the population level rather than at the individual level (Borgonovo et al, 2022). They are complementary to the dynamic stochastic general equilibrium modeling in macroeconomics.…”

“…Independent and heterogeneity of agents' behavior are the key features of our agent-based model. Under such settings, the researcher can control and manipulate the specific information, and observe how investors make decisions; so the designated space provides a special condition to experiment with the existence of herding behavior (Borgonovo et al, 2022;Manahov & Hudson, 2014). Furthermore, the evolution of trading strategies (micro-level) and the co-evolution of agents that results in the market dynamics (macro level) lead to the self-organizing system (Witkam, 2013).…”

The emergence of discrimination due to miscategorization

“…For robustness analysis, we follow the framework ofBorgonovo et al (2022). Following this framework, we change imitation strategies, parameters, and an update mechanism one by one, and observe the correlation between the cost of fighting and gender discrimination from Figure11a.…”