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Article Excerpt
INTRODUCTION
Dynamic decision making (DDM) comprises a series of multiple, interdependent decisions that are made in real time in a continuously changing autonomous environment (Brehmer, 1990; Edwards, 1962). For example, air traffic control (ATC) requires controllers to make multiple decisions regarding how to allocate space to best accommodate the arrivals and departures of airplanes. That the assignment of a landing lane to an incoming airplane precludes the use of that lane by other airplanes, reflects the interdependency of decisions that characterizes DDM tasks. Furthermore, environmental parameters such as arrivals, departures, and weather are exogenous during ATC (i.e., they are beyond the influence of the controller). Finally, incoming airplanes need to be assigned to a landing lane at the correct moment in real time. Thus ATC provides a prime real-world example of DDM.
In actuality, most real-life decisions involve DDM, although they vary in the time allowed for decision making and in the number of chances that one receives to practice them. For example, in an effort to become tenured, professors make multiple and interrelated decisions regarding how to best allocate their time to tasks such as manuscript preparation, grant writing, and teaching. Throughout this process, many exogenous factors such as teaching load and reviewers affect the professors' decision-making processes, and the time at which decisions are made is critical for the accomplishment of the professors' motivating goal.
Time constraints have been defined as the difference between the amount of available time and the amount of time required to resolve a decision task (Benson & Beach, 1996; Rastegary & Landy, 1993). According to this definition, time constraints are relative to the pace of change in the decision environment. For example, professors in pursuit of tenure have much more time to make decisions than do air traffic controllers trying to help airplanes land safely. When placed under time constraints, decision makers encounter multiple decisions per unit time. Time constraints and the number of task executions are factors that influence individuals' ability to acquire control of a dynamic decision system (Kerstholt & Raaijmakers, 1997). Intuitively, one might expect that the more times individuals practice a task the better their task performance will be, but the interaction of time constraints and practice and the influence of these factors on performance have not been studied. This study addressed this research question in a DDM environment by examining both the relationship between practice trials and time constraints and the possible cognitive strategies utilized by individuals with different cognitive abilities.
INFLUENCE OF TIME AND COGNITIVE ABILITIES ON DDM
Research has shown that time constraints have a negative effect on the ability of individuals to make decisions effectively (Maule & Edland, 1997; Svenson & Maule, 1993). Most of this research, however, has emphasized traditional, static decision-making tasks and has focused almost exclusively on the effects of time pressure on performance. Researchers also have primarily investigated one-time decisions rather than the series of decisions that characterize DDM environments (Kerstholt & Raaijmakers, 1997).
Although most research conducted to date has evaluated the factor of time constraints on individuals performing static decision-making tasks, there is no reason to expect this effect to be anything other than detrimental to the ability of individuals to perform dynamic tasks as well. Because many DDM situations are extremely complex, the amount of information that an individual must process before making a decision can be very taxing on the decision maker if time is limited (i.e., if the decision maker is working under a deadline or within a higher-paced environment). As in static situations, individuals asked to perform dynamic tasks may exercise "satisficing"--thus limiting the amount of information they must process before making a decision--or may simply reduce their efforts at processing to perform the task as quickly as possible. In any case, time constraints would be expected to induce lower performance.
It is more difficult to predict what sort of effect time constraints might have on learning. One might reasonably hypothesize that the ability to deal with time constraints effectively improves with practice. If this is the case, the completion of enough practice trials should help individuals to achieve control over a system and therefore should improve overall task performance. Hence, with sufficient practice, individuals under severe time constraints should reach performance levels similar to those of individuals under low time constraints. In summary, this study was designed to test the hypothesis that high time constraints attenuate dynamic task performance and that practice can moderate this negative effect.
Human cognitive capacity is another variable that is frequently mentioned in the literature but rarely investigated in DDM studies. Because human cognition is limited, it should modulate the effects of time pressure on learning in DDM situations. In complex and dynamic systems, the need to process larger amounts of information increases as the amount of available time decreases. Because dynamic situations change over time, decision makers must process new situations continuously and be able to retain information while concurrently processing incoming variables. Researchers commonly define fluid intelligence (Gf) as the ability to solve novel problems and adapt to new situations (Engle, Tuholski, Laughlin, & Conway, 1999). Working memory and Gf are related through what is commonly referred to as controlled attention, or the number of elements on which an individual can focus his or her attention at a particular time. The study presented here examined the possible effects of human cognitive capacity and its relationship to time constraints. Because human capacity is limited, it was hypothesized that the detrimental effects of time constraints would be substantial and would correlate with a measure of cognitive capacity.
This study also investigated the effects of time constraints on the strategies that individuals use to deal with a dynamic system. In static situations with time constraints, decision makers speed up their information processing and reduce the amount of time they spend searching for predecision information (Edland & Svenson, 1993). Kerstholt (1994)reported that during DDM there is an acceleration in information processing as time becomes more limited. She also found that dynamic decision makers employ judgment-oriented...
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