Dynamic decision- making - Wikipedia. Dynamic decision- making (DDM) is interdependent decision- making that takes place in an environment that changes over time either due to the previous actions of the decision maker or due to events that are outside of the control of the decision maker. These computer simulations are also called “microworlds”. Research in DDM has focused on investigating the extent to which decision makers use their experience to control a particular system; the factors that underlie the acquisition and use of experience in making decisions; and the type of experiences that lead to better decisions in dynamic tasks. Characteristics of dynamic decision- making environments. The dynamics of the environments refers to the dependence of the system’s state on its state at an earlier time. Handbook of Applications of Chaos Theory. Applications of Extreme Value Theory in Dynamical Systems for the Analysis of Blood Pressure Data. Scott Mc Laughlin. Interconnected Dynamical Systems, Raymond A. DeCar/o and Richard Saeks 11. Transformer and Inductor Design Handbook: Second Edition, Revised and. Dynamic decision-making. Opaqueness refers to the physical invisibility of some aspects of a dynamic system and it might also be dependent upon a decision maker. Download and Read Sunbonnet Sue And Scottie At Play Designs In Redwork And Applique. Nonlinear dynamical systems for theory and research in ergonomics. Nonlinear dynamical systems for theory and research in. Handbook of Systems and Complexity. Dynamics in the system could be driven by positive feedback (self- amplifying loops) or negative feedback (self- correcting loops), examples of which could be the accrual of interest in a saving bank account or the assuage of hunger due to eating respectively. Complexity largely refers to the number of interacting or interconnected elements within a system that can make it difficult to predict the behavior of the system. But the definition of complexity could still have problems as system components can vary in terms of how many components there are in the system, number of relationships between them, and the nature of those relationships. Complexity may also be a function of the decision maker's ability. Opaqueness refers to the physical invisibility of some aspects of a dynamic system and it might also be dependent upon a decision maker’s ability to acquire knowledge of the components of the system. Dynamic complexity refers to the decision maker’s ability to control the system using the feedback the decision maker receives from the system. Diehl and Sterman. The opaqueness present in the system might cause unintended side- effects. There might be non- linear relationships between components of a system and feedback delays between actions taken and their outcomes. Simulation, Modeling, and Applied Cognitive Science PhD. The dynamic complexity of a system might eventually make it hard for the decision makers to understand and control the system. Microworlds in DDM research. Research in dynamic decision- making is mostly laboratory- based and uses computer simulation microworld tools (i. Decision Making Games, DMGames). The microworlds are also known by other names, including synthetic task environments, high fidelity simulations, interactive learning environments, virtual environments, and scaled worlds. 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. However, even though DMGames aim to represent the essential elements of real- world systems, they differ from the real- world task in various respects. Stakes might be higher in real- life tasks and expertise of the decision maker has often been acquired over a period of many years rather than minutes, hours or days as in DDM tasks. Thus, DDM differs in many respects from naturalistic decision- making (NDM). In DDM tasks people have been shown to perform below the optimal levels of performance, if an optimal could be ascertained or known. For example, in a forest firefighting simulation game, participants frequently allowed their headquarters to be burned down. One of the main research activities in DDM has been to investigate using microworlds simulations tools the extent to which people are able to learn to control a particular simulated system and investigating the factors that might explain the learning in DDM tasks. Strategy- Based Learning Theory. These rules specify the conditions under which a certain rule or strategy will apply. These rules are of the form if you recognize situation S, then carry out action/strategy A. For example, Anzai. The Anzai strategies did reasonably well to mimic the performance on the task by human participants. Similarly, Lovett and Anderson. There are basically two strategies to use in trying to solve this problem. The undershoot strategy is to take smaller sticks and build up to the target stick. The overshoot strategy is to take the stick longer than the goal and cut off pieces equal in length to the smaller stick until one reaches the target length. Lovett and Anderson arranged it so that only one strategy would work for a particular problem and gave subjects problems where one of the two strategies worked on a majority of the problems (and she counterbalanced over subjects which was the more successful strategy). Connectionism learning theory. Handbook the guggenheim museum collection 1900 1980. The connections between units, whose strength or weighing depend upon previous experience. Thus, the output of a given unit depends upon the output of the previous unit weighted by the strength of the connection. As an example, Gibson et al. According to IBLT, individuals rely on their accumulated experience to make decisions by retrieving past solutions to similar situations stored in memory. Thus, decision accuracy can only improve gradually and through interaction with similar situations. IBLT assumes that specific instances or experiences or exemplars are stored in the memory. In atypical situations (those that are not similar to anything encountered in the past), retrieval from memory is not possible and people would need to use a heuristic (which does not rely on memory) to make a decision. 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. But the necessity level might also be determined by external environmental factors like time constraints (as in the medical domain with doctors in an emergency room treating patients in a time critical situation). Once that necessity level is crossed, the decision involving the instance with the highest utility is made. The outcome of the decision, when received, is then used to update the utility of the instance that was used to make the decision in the first place (from expected to experienced). This generic decision making process is assumed to apply to any dynamic decision making situation, when decisions are made from experience. The computational representation of IBLT relies on several learning mechanisms proposed by a generic theory of cognition, ACT- R. Currently, there are many decision tasks that have been implemented in the IBLT that reproduces and explains human behavior accurately. There is a time delay built into the game between placing an order by a role and reception of the ordered cases of beer. If a role runs out of beer (i. This might lead people to overstock beer to satisfy any future unanticipated demands. Results, contrary to economic theory which predicts a long term stable equilibrium, show people ordering too much. This happens because the time delay between placing an order and receiving inventory makes people think that the inventory is running out as new orders come in, so they react and place larger orders. Once they build up the inventory and realize the incoming orders they drastically cut future orders which leads the beer industry experience oscillating patterns of over- ordering and under- ordering, that is, costly cycles of boom and bust. Similar examples on effects of feedback delay have been reported among fire fighters in a fire fighting game called NEWFIRE in the past where on account of task complexity and feedback delay between actions of firefighters and outcomes, led participants to frequently allow their headquarters to be burned down. Effects of proportional thinking in DDM Tasks. 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. This problem, termed Stock- Flow failure (SF Failure), has been shown to be persistent even in simple tasks, with well motivated participants, in familiar contexts and simplified information displays. The belief that the stock behaves like the flows is a common but wrong heuristic (named the “correlation heuristic. Larrick & Soll, 2. De Bock 2. 00. 2; Greer, 1. Van Dooren et al., 2. Van Dooren et al., 2. Verschaffel et al., 1. Individual Differences in DDM. Although individual differences exist and are often shown on DDM tasks, there has been a debate on whether these differences arise as a result of differences in cognitive abilities. Some studies have failed to find evidence of a link between cognitive abilities as measured by intelligence tests and performance on DDM tasks. But later studies contend that this lack is due to absence of reliable performance measures 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. This does not discount research in the laboratory but reveals the broad conception of the research underlying DDM. Under the DDM in the real world people are more interested in processes like goal setting, planning, perceptual and attention processes, forecasting, comprehension processes and many others including attending to feedback. The study of these processes brings DDM research closer to situation awareness and expertise. For example, it has been shown in DDM research that motorists who have more than 1.
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