Formal dynamic and computational models are a useful method of conceptualizing complex and dynamic phenomena in psychology because they allow researchers to understand how a representation of a phenomenon behaves as a complex system over time. Despite the clear advantages, organizational psychologists seem to shy away from using formal dynamic and computational models. This chapter focuses on how formal dynamic and computational model approaches can advance our understanding of psychological contract dynamics. The authors first discuss the advantages of formal dynamic and computational models, and how using them can address the existing issues in the psychological contract literature. Then they focus on different levels of analysis and different types of dynamic and computational models that are relevant to psychological contract research. They end this chapter with an overview of useful books, journals, articles, and websites for researchers who are interested in building their own formal dynamic or computational model.
Justin Weinhardt, Yannick Griep and Joanna Sosnowska
Jason L. Harman, Justin Weinhardt and John-Luke McCord
In this chapter, we provide a general overview of recent advances in the study of dynamic decision making. Decisions in organizations happen over time, are influenced by, and subsequently influence other decisions that are made. Until recently, there has been a lack of emphasis on dynamics in decision making in organizational research. Newer analytic and computational methodologies along with new experimental paradigms have begun to change the research landscape. We begin with the basic building blocks of dynamic decision-making problems—dynamic systems. We then review work on goal pursuit and outline the current knowledge about how temporal dynamics influence motivation, effort, and goal pursuit. The final sections review the last 15 years of research on decisions from experience and temporal discounting. Traditional decision-making research treats decisions as one-time events where an individual is given information about possible outcomes beforehand. Decisions from experience is a newer view of decision making, where the type of decisions being modeled are choices made repeatedly over time with the outcome of choices influencing future decisions. The description–experience gap refers to differences in observed choice behavior between these two paradigms. Finally, we review recent work on temporal discounting.