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Theorists in management and organizational science rarely use computational modeling to support theoretical development or refinement, particularly at the micro level of analysis. This article argues that organizational scholars, who strive to understand dynamic behavior in a complex context, are particularly in need of the support computational models offer. Moreover, organizational scholars can build on (a) the plethora of informal theories extant in the literature and (b) the computational architectures and model building platforms developed in recent years. To increase the number of organizational scholars building and evaluating computational models, the article provides a tutorial in model building and simulation. Specifically, a new computational model is built and assessed. Surprising realizations emerge in the process. There is also an extensive section on model evaluation involving empirical observations.

This chapter is designed help motivate the current readers' interest in computational modeling as the authors provide a description of the myriad of values computational modeling can bring to our science. Readers are also given a brief history of computational modeling as it relates to the field of I-O psychology. This is followed by a more complete description of the goals for the book, as the authors describe the learning objectives for various levels of computational model afficionados, from the scholarly consumers to the computational model creators. Finally, an overview of the chapters is provided.

"This collection provides a primer to the process and promise of computational modeling for industrial-organizational psychologists. With contributions by global experts in the field, the book is designed to expand readers' appreciation for computational modeling via chapters focused on key modeling achievements in domains relevant to industrial-organizational psychology, including decision-making in organizations, diversity and inclusion, learning and training, leadership, and teams"--

Effective risk communication during the COVID-19 pandemic is critical for encouraging appropriate public health behaviors. One way that the public is informed about COVID-19 numbers is through reports of daily new cases. However, presenting daily cases has the potential to lead to a dynamic reasoning bias that stems from intuitive misunderstandings of accumulation. Previous work in system dynamics shows that even highly educated individuals with training in science and math misunderstand basic concepts of accumulation. In the context of COVID-19, relying on the single cue of daily new cases can lead to relaxed attitudes about the risk of COVID-19 when daily new cases begin to decline. This situation is at the very point when risk is highest because even though daily new cases have declined, the active number of cases are highest because they have been accumulating over time. In an experiment with young adults from the USA and Canada (N = 551), we confirm that individuals fail to understand accumulation regarding COVID-19, have less concern regarding COVID-19, and decrease endorsement for public health measures as new cases decline but when active cases are at the highest point. Moreover, we experimentally manipulate different dynamic data visualizations and show that presenting data highlighting active cases and minimizing new cases led to increased concern and increased endorsement for COVID-19 health measures compared to a control condition highlighting daily cases. These results hold regardless of country, political affiliation, and individual differences in decision making. This study has implications for communicating the risks of contracting COVID-19 and future public health issues.