Research in dynamic decision-making is mostly laboratory-based and uses computer simulation microworld tools (i.e., Decision Making Games, DMGames).
Microworlds become the laboratory analogues for real-life situations and help DDM investigators to study decision-making by compressing time and space while maintaining experimental control.
The DMGames compress the most important elements of the real-world problems they represent and are important tools for collecting human actions DMGames have helped investigate a variety of factors, such as cognitive ability, type of feedback, timing of feedback, strategies used while making decisions, and knowledge acquisition while performing DDM tasks.
[7] In similar DDM studies participants acting as doctors in an emergency room allowed their patients to die while they kept waiting for results of test that were actually non-diagnostic.
[8][9] An interesting insight into decisions from experience in DDM is that mostly the learning is implicit, and despite people's improvement of performance with repeated trials they are unable to verbalize the strategy they followed to do so.
Similarly, Lovett and Anderson[12] have shown how people use production rules or strategies of the if – then type in the building-sticks task which is an isomorph of Lurchins' waterjug problem.
As an example, Gibson et al.[15] has shown that a connectionist neural network machine learning model does a good job to explain human behavior in the Berry and Broadbent's Sugar Production Factory task[clarification needed].
[3] The theory has been extended to two different paradigms of dynamic tasks, called sampling and repeated-choice, by Cleotilde Gonzalez and Varun Dutt.
[16] Gonzalez and Dutt [16] have shown that in these dynamic tasks, IBLT provides the best explanation of human behavior and performs better than many other competing models and approaches.
According to IBLT, individuals rely on their accumulated experience to make decisions by retrieving past solutions to similar situations stored in memory.
[17] These instances have a very concrete structure defined by three distinct parts which include the situation, decision, and utility (or SDU): In addition to a predefined structure of an instance, IBLT relies on the global, high-level decision making process, consisting of five stages: recognition, judgment, choice, execution, and feedback.
In situations that are typical and where inss can be retrieved, evaluation of the utility of the similar instances takes place until a necessity level is crossed.
[16] Necessity is typically determined by the decision maker's “aspiration level,” similar to Simon and March's satisficing strategy.
Growing evidence in DDM indicates that adults share a robust problem in understanding some of the basic building blocks of simple dynamic systems, including stocks, inflows, and outflows.
Many adults have shown a failure to interpret a basic principle of dynamics: a stock (or accumulation) rises (or falls) when the inflow exceeds (or is less than) the outflow.
Some studies have failed to find evidence of a link between cognitive abilities as measured by intelligence tests and performance on DDM tasks.
Under demanding conditions of workload, low ability participants do not show improvement in performance in either training or test trials.
Evidence shows that low ability participants use more heuristics particularly when the task demands faster trials or time pressure and this happens both during training and test conditions.
[28] Also, owing to their greater experience, such motorists tend to perform a more effective and efficient search for hazards cues than their not so experienced counterparts.
Thus, experience on different DDM tasks makes a decision maker more situational aware with higher levels of perceptual and comprehension skills.