Learning and transfer in dynamic decision environments

An important aspect of learning is the ability to transfer knowledge to new contexts. However, in dynamic decision tasks, such as bargaining, firefighting, and process control, where decision makers must make repeated decisions under time pressure and outcome feedback may relate to any of a number of decisions, such transfer has proven elusive. This paper proposes a two-stage connectionist model which hypothesizes that decision makers learn to identify categories of evidence requiring similar decisions as they perform in dynamic environments. The model suggests conditions under which decision makers will be able to use this ability to help them in novel situations. These predictions are compared against those of a one-stage decision model that does not learn evidence categories, as is common in many current theories of repeated decision making. Both models' predictions are then tested against the performance of decision makers in an Internet bargaining task. Both models correctly predict aspects of decision makers' learning under different interventions. The two-stage model provides closer fits to decision maker performance in a new, related bargaining task and accounts for important features of higher-performing decision makers' learning. Although frequently omitted in recent accounts of repeated decision making, the processes of evidence category formation described by the two-stage model appear critical in understanding the extent to which decision makers learn from feedback in dynamic tasks. Faison (Bud) Gibson is an Assistant Professor at College of Business, Eastern Michigan University. He has extensive experience developing and empirically testing models of decision behavior in dynamic decision environments.     

Introduction to session 4

Applied Biochemistry and Biotechnology -     

Rapid column extraction method for actinides and strontium in fish and other animal tissue samples

The analysis of actinides and radiostrontium in animal tissue samples is very important for environmental monitoring. There is a need to measure actinide isotopes and strontium with very low detection limits in animal tissue samples, including fish, deer, hogs, beef and shellfish. A new, rapid separation method has been developed that allows the measurement of plutonium, neptunium, uranium, americium, curium and strontium isotopes in large animal tissue samples (100–200 g) with high chemical recoveries and effective removal of matrix interferences. This method uses stacked TEVA Resin®, TRU Resin® and DGA Resin® cartridges from Eichrom Technologies (Darien, IL, USA) that allows the rapid separation of plutonium (Pu), neptunium (Np), uranium (U), americium (Am), and curium (Cm) using a single multi-stage column combined with alphaspectrometry. Strontium is collected on Sr Resin® from Eichrom Technologies (Darien, IL, USA). After acid digestion and furnace heating of the animal tissue samples, the actinides and ^89/90Sr are separated using column extraction chromatography. This method has been shown to be effective over a wide range of animal tissue matrices. Vacuum box cartridge technology with rapid flow rates is used to minimize sample preparation time.     

Supporting Learning in Evolving Dynamic Environments

In dynamic decision environments such as direct sales, customer support, and electronically mediated bargaining, decision makers execute sequences of interdependent decisions under time pressure. Past decision support systems have focused on substituting for decision makers' cognitive deficits by relieving them of the need to explicitly account for sequential dependencies. However, these systems themselves are fragile to change and, further, do not enhance decision makers' own adaptive capacities. This study presents an alternative strategy that defines information systems requirements in terms of enhancing decision makers' adaptation. In so doing, the study introduces a simulation model of how decision makers learn patterns of sequential dependency. When a system was used to manage workflows in a way predicted by the model to enhance learning, decision makers in a bargaining experiment learned underlying patterns of sequential dependencythat helped them adapt to new situations. This result is rare if not unique in the study of dynamic decision environments. It indicates that a shift, away from substituting for short-term deficits and toward enhancing pattern learning, can substantially improve the effectiveness of decision support in dynamic environments. Based on the specific findings in this study, this shift has important implications for designing information system workflows and potential future applications in interface design.     

Microbial solubilization of a preoxidized subbituminous coal

Preliminary characterization of a microbial coal solubilization product has been performed. Submerged cultures ofPaecilomyces TLi orCandida ML13 were grown in defined minimal media containing preoxidized Wyodak subbituminous coal. Culture supernatants contained high-molecular-weight, acid-precipitable material that was separated by gel permeation chromatography (GPC) in aqueous and polar organic solvents. Organic GPC also separated a low-molecular-weight (<2700 daltons) fraction that was converted to higher-molecular-weight material upon acidification. Elemental analysis of the acid-precipitated material indicated an increased oxygen content as a result of the biological treatment. The biosolubilized product may undergo further microbial modification.     

Adsorption of Sr by immobilized microorganisms

Wastewaters from numerous industrial and laboratory operations can contain toxic or undesirable components such as metal ions, which must be removed before discharge to surface waters. Adsorption processes that have high removal efficiencies are attractive methods for removing such contaminants. For economic operations, it is desirable to have an adsorbent that is selective for the metal contaminant of interest, has high capacity for the contaminant, has rapid adsorption kinetics, can be economically produced, and can be regenerated to a concentrated waste product or decomposed to a low-volume waste. Selected microorganisms are potentially useful adsorbents for these applications because they can be inexpensive, have high selectivities, and have high capacities for adsorption of many heavy metals, which are often problems in a variety of industries.     

Is It Better to Forget? Stimulus-Response, Prediction, and the Weight of Past Experience in a Fast-Paced Bargaining Task

Decision makers in dynamic environments such as air traffic control, firefighting, and call center operations adapt in real-time using outcome feedback. Understanding this adaptation is important for influencing and improving the decisions made. Recently, stimulus-response (S-R) learning models have been proposed as explanations for decision makers' adaptation. S-R models hypothesize that decision makers choose an action option based on their anticipation of its success. Decision makers learn by accumulating evidence over action options and combining that evidence with prior expectations. This study examines a standard S-R model and a simple variation of this model, in which past experience may receive an extremely low weight, as explanations for decision makers' adaptation in an evolving Internet-based bargaining environment. In Experiment 1, decision makers are taught to predict behavior in a bargaining task that follows rules that may be the opposite of, congruent to, or unrelated to a second task in which they must choose the deal terms they will offer. Both models provide a good account of the prediction task. However, only the second model, in which decision makers heavily discount all but the most recent past experience, provides a good account of subsequent behavior in the second task. To test whether Experiment 1 artificially related choice behavior and prediction, a second experiment examines both models' predictions concerning the effects of bargaining experience on subsequent prediction. In this study, decision models where long-term experience plays a dominating role do not appear to provide adequate explanations of decision makers' adaptation to their opponent's changing response behavior.     

The sugar production factory—A dynamic decision task

This article describes the Sugar Production Factor and its structural equivalent, the Personal Interaction task. These are two simple, individual dynamic decision-making tasks in which subjects make interdependent decisions to reach a goal, and receive feedback on the outcome of their efforts along the way. An important result from human learning experiments using these two tasks and their variants is that subjects reliably improve their ability to reach the goal over a moderate number of training trials (40–90) but do not show consistent improvement in other measures of task knowledge. These other measures focus on subjects' ability to accurately predict the task environment's response to their actions and subjects' ability to produce useful heuristics. This pattern of results runs counter to the idea that decision makers' performance in dynamic decision tasks depends critically on the predictive accuracy their internal models of the task environment. Variants of both tasks have been used to manipulate this pattern of results and explore more deeply the nature of the internal models that subjects form of the task environment. These variants are discussed in the context of other relevant findings in the dynamic decision making literature.     

Production of coal-solubilizing activity byPaecilomyces Sp. during submerged growth in defined liquid media

Paecilomyces TLi, a fungus isolated from coal, had previously been shown to transform solid coal into a water-miscible liquid during surface growth on coal or on a complex, solid microbiological medium. Coal solubilization has now been demonstrated in submerged cultures grown in defined liquid minimal media. Activity under these conditions is less than that observed in surface culture, and is affected by both nitrogen and carbon nutrition. Coal solubilization occurs at low pH (≤ 4) but may be associated with pH increases during the assay period. Spectroscopic data suggest a role for alkaline catalysis in coal solubilization by this organism.