Dynamic decision-making (DDM) is interdependent decision-making that takes place in an environment that changes over time either due to the previous actions of the decision maker or due to events that are outside of the control of the decision maker. In this sense, dynamic decisions, unlike simple and conventional one-time decisions, are typically more complex and occur in real-time and involve observing the extent to which people are able to use their experience to control a particular complex system, including the types of experience that lead to better decisions over time.

Dynamic decision making research uses computer simulations which are laboratory analogues for real-life situations. These computer simulations are also called “microworlds” and are used to examine people's behavior in simulated real world settings where people typically try to control a complex system where later decisions are affected by earlier decisions. The following differentiate DDM research from more classical forms of decision making research of the past:

Also, the use of microworlds as a tool to investigate DDM not only provides experimental control to DDM researchers but also makes the DDM field contemporary unlike the classical decision making research which is very old.

Examples of dynamic decision making situations include managing climate change, factory production and inventory, air traffic control, firefighting, and driving a car, military command and control in a battle field. Research in DDM has focused on investigating the extent to which decision makers use their experience to control a particular system; the factors that underlie the acquisition and use of experience in making decisions; and the type of experiences that lead to better decisions in dynamic tasks.

The primary characteristics of dynamic decision environments are dynamics, complexity, opaqueness, and dynamic complexity. The dynamics of the environments refers to the dependence of the system's state on its state at an earlier time. Dynamics in the system could be driven by positive feedback (self-amplifying loops) or negative feedback (self-correcting loops), examples of which could be the accrual of interest in a saving bank account or the assuage of hunger due to eating respectively.

Complexity largely refers to the number of interacting or interconnected elements within a system that can make it difficult to predict the behavior of the system. But the definition of complexity could still have problems as system components can vary in terms of how many components there are in the system, number of relationships between them, and the nature of those relationships. Complexity may also be a function of the decision maker's ability.

Opaqueness refers to the physical invisibility of some aspects of a dynamic system and it might also be dependent upon a decision maker's ability to acquire knowledge of the components of the system.

Dynamic complexity refers to the decision maker's ability to control the system using the feedback the decision maker receives from the system. Diehl and Sterman have further broken down dynamic complexity into three components. The opaqueness present in the system might cause unintended side-effects. There might be non-linear relationships between components of a system and feedback delays between actions taken and their outcomes. The dynamic complexity of a system might eventually make it hard for the decision makers to understand and control the system.