{"content":[{"text":"A Computational Model of Learning with Time Pressure in Dynamic \n Decision Tasks \n Faison P. Gibson \n Graduate School of Industrial Administration \n Carnegie Mellon University \n Pittsburgh, PA 15213 \n gibson+@cmu.edu \n http://www.gsia.cmu.edu/andrew/gibson/web/ \n Introduction and Theory","bbox":{"left":0.175294,"top":0.088944,"width":0.6490849999999999,"height":0.160314}},{"text":"In dynamic decision tasks such as managing a production process, decision makers attempt to control an \n evolving situation in real-time. This type of dynamic decision task can be distinguished from one-time \n decision tasks such as buying a house, by the presence of four elements: (1) The tasks require a series of \n decisions rather than one isolated decision; (2) The decisions are interdependent; (3) The environment \n changes both autonomously and as a result of decision makers' actions; (4) Decisions are goal-directed and \n made under time pressure, thereby reducing the decision-maker's opportunities to consider and explore \n options (Brehmer, 1990, 1992, 1995; Rapoport, 1975; Edwards, 1962).","bbox":{"left":0.147026,"top":0.263424,"width":0.698339,"height":0.103775}},{"text":"Hogarth (1981) has hypothesized that an important feature of many dynamic decision environments is that \n they provide decision makers with feedback on the outcomes of their actions. Decision makers may use this \n outcome feedback to adapt their decisions on-line as they perform the task. Recently, Davis and Kottemann \n (1995) have shown evidence that decision makers' performance improves in a dynamic task when they are \n provided with continuous and cumulative outcome feedback. Sengupta and Abdel-Hamid (1993) find \n similar results when they provide their subjects with a decision aid that immediately models the \n environment's long-term response to their actions.","bbox":{"left":0.147043,"top":0.382667,"width":0.704588,"height":0.10392600000000002}},{"text":"However, Sterman (1989a, 1989b, 1994; Paich & Sterman, 1993; Diehl & Sterman, 1995) provides and \n discusses evidence that subjects using outcome feedback alone fail to learn to control dynamic systems to \n the level that they could with simple linear heuristics. Furthermore, several researchers have obtained \n results supporting the general finding that subjects in dynamic tasks fail to use outcome feedback to adapt \n to the point where they can effectively cope with the demands of the decision environment, at least in the \n time-frame dictated by the experiment and with the information provided about the environment (Brehmer, \n 1990, 1992, 1995; Kleinmuntz, 1993; Kleinmuntz & Thomas, 1987; Richardson & Rohrbaugh, 1990). \n Attempts to improve subjects' performance by providing various decision aids in addition to outcome \n feedback have generated mixed results (e.g., Kleinmuntz & Thomas, 1987; Richardson & Rohrbaugh, \n 1990).","bbox":{"left":0.147059,"top":0.501909,"width":0.700307,"height":0.14757699999999996}},{"text":"The mixture of results described in the last two paragraphs suggests that, while central to improving \n performance, the process by which people adapt or learn learn on-line from outcome feedback in dynamic \n decision tasks is only partially understood. To begin to address this issue, we have developed a \n computational formulation of how people might learn in these situations.","bbox":{"left":0.147059,"top":0.664788,"width":0.6930989999999999,"height":0.06042700000000001}},{"text":"Our formulation builds on previous theoretical work in dynamic decision making by Brehmer (1990, 1992, \n 1995) and motor learning by Jordan and Rumelhart (1992; Jordan, 1992, in press; Jordan, Flash, & Arnon, \n 1994; Wolpert, Gharamani, & Jordan, 1995). It proposes two central assumptions about how decision \n makers learn on-line from outcome feedback in dynamic decision tasks. The first is that decision makers \n use outcome feedback to form two interdependent, internal submodels of the task as they participate in it. \n These two submodels represent knowledge, conditioned by environmental context, about: (1) How the \n decision maker's actions affect outcomes; (2) Which actions to take to achieve desired outcomes. Our \n formulation's second major assumption is that the acquisition of these two types of knowledge from \n outcome feedback can be simulated using on-line learning of parallel distributed processing (PDP) or \n neural network models. This second assumption is important because, among other reasons, it focuses the \n applicability of the model to situations where decision makers have little time for reflection.","bbox":{"left":0.147059,"top":0.740546,"width":0.699902,"height":0.16197499999999998}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}