System Dynamics Based Technology for Decision Support

System Dynamics Based Technology for Decision Support

Hassan Qudrat-Ullah (York University, Canada)
DOI: 10.4018/978-1-60566-026-4.ch581
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Abstract

Managers face problems that are increasingly complex and dynamic. Decision support systems (DSS) are designed to assist them make better decisions. However, the empirical evidence concerning the impact of DSS on improved decision making and learning in dynamic tasks is equivocal at best (Klabbers, 2003; Sharda, Steve, Barr, & McDonnell, 1988; Sterman, 2000; Todd & Benbasat, 1999). Over four decades of dynamic decision making; studies have resulted in a general conclusion on why people perform poorly in dynamic tasks. In dynamic tasks, where a number of decisions are required rather than a single decision, decisions are interdependent, and the decision-making environment changes as a result of the decisions or autonomously or both (Edwards, 1962), most often the poor performance is attributed to subjects’ misperceptions of feedback. That is, people perform poorly because they ignore time delays between their “actions and the consequences” (Sterman, 2000) and are insensitive to the feedback structure of the task system (Diehl & Sterman, 1995). Decision maker’s mental models about the task are often inadequate and flawed (Kerstholt & Raaijmakers, 1997; Romme, 2004). In this paper we argue that system dynamics based interactive learning environments (ILEs) could provide effective decision support for dynamic tasks by reducing the misperceptions of feedback.
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Background

Dynamic Decision Making

Dynamic decision-making situations differ from those traditionally studied in static decision theory in at least three ways: (1) a number of decisions are required rather than a single decision, (2) decisions are interdependent, and (3) the environment changes, either as a result of decisions made or independently of them or both (Edwards, 1962). Recent research in system dynamics has characterized such tasks by feedback processes, time delays, and nonlinearities in the relationships between decision task variables (Romme, 2004). Driving a car, managing a firm, and controlling money supply are all dynamic tasks (Diehl & Sterman, 1995) In these tasks, contrary to static tasks such as lottery-type gambling, locating a park on a city map, and counting money, multiple and interactive decisions are made over several periods, whereby these decisions change the environment, giving rise to new information and leading to new decisions (Forrester, 1961; Sterman, 2000).

ILE

We use ILEs as a term sufficiently general to include microworlds, management flight simulators, DSS, learning laboratories, and any other computer simulation-based environment—the domain of these terms is all forms of action whose general goal is the facilitation of dynamic decision making. Based on the on-going work in the system dynamics discipline (Moxnes, 2004; Otto & Struben, 2004; Qudrat-Ullah, in press; Sterman, 2002), this conception of ILE embodies learning as the main purpose of an ILE. Under this definition of ILE, learning goals are made explicit to the decision makers. A computer simulation model is built to represent adequately the domain or issue under study with which the decision makers can experience and induce real world-like responses (Qudrat-Ullah, 2005). Human intervention refers to active keying in of the decisions by the decision makers into the computer simulation model via the interface of an ILE.

Key Terms in this Chapter

Feedback: It is a process whereby an input variable is fed back by the output variable. For example, an increased (or decreased) customer base leads to an increase (or decrease) in sales from word of mouth which then is fed back to the customer base, increasingly (or decreasingly).

Diagnostic Information: Information that helps to understand why a particular consequence happened or did not happen.

Behavior of System: The patterns of performance of the variable(s) of the system over time.

Simulation Model: A simplified, computer, simulation-based construction (model) of some real world phenomenon (or the problem task).

Mental Model: A mental model is the collection of concepts and relationships about the image of real world things we carry in our heads. For example, one does not have a house or a city or a gadget in his/her head but a mental model about these items.

Nonlinearity: A nonlinearity exists between a cause (decision) and effect (consequence), if effect is not proportional to cause.

Time Delays: Often the decisions and their consequences are not closely related in time. For instance, the response of gasoline sales to the changes in price involves time delays. If prices go up only after a while may sales drop.

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