| Dynamic Decision Making Laboratory | ||||
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RESEARCH |
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Selected Publications Facilities, Methods, & DMGames
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All our research revolves around the topics of learning and decisions from experience. Our work is characterized by the study of repeated and interdependent decisions, that allow us to examine learning and the use of experience in making decisions. We address questions such as: How does experience influence our decisions? What kinds of experiences would produce better decisions and better adaptation? How does experience transfer to new situations? Our research has multiple practical applications, most prominently on training. We use multiple research methods including laboratory experiments and cognitive modeling. For example, we collect behavioral data using complex, dynamic simulations (called DMGames) that involves extended practice to understand how experience develops, changes, and transfers. Also, we create ACT-R cognitive computational models that reproduce human behavior. Current Projects (Sorted Chronologically)
Richard Lounsbery Foundation (2007-2008). PeaceMaker-Based Research for Decision Making and Diplomacy. In collaboration with Kiron Skinner, Associate Professor of Social and Decision Sciences and History & Laurie Eisenberg, Associate Teaching Professor of History at Carnegie Mellon University. Argonne National Laboratory (2008). Determinants of Public Confidence in Government to Prevent Terrorism. In collaboration with Ignacio Martinez-Moyano and Michael Samsa of Argonne National Labs. U.S. Army Research Laboratory (2001-2006). Cognitive Process Modeling and Measurement in Dynamic Decision Making. In collaboration with Mica Endsley, President of SA Technologies. Office of Naval Research, Multidisciplinary University Research Initiative (2001-2006). Cognitive, Biological and Computational Analyses of Automaticity in Complex Cognition. In collaboration with Marcel Just, D.O. Hebb Professor of Psychology and Co-Director of Center for Cognitive Brain Imaging (CCBI) at Carnegie Mellon University, Walter Schneider, Professor of Psychology at the University of Pittsburgh, & Poornima Madhavan, Assistant Professor of Psychology at the Old Dominion University Office of Naval Research, Small Business Innovation Research (2004). Automated Communication Analysis for Interactive Situation Awareness Assessment. In collaboration with Mica Endsley, President of SA Technologies & Cheryl Bolstad, Senior Research Associate at SA Technologies. Institute for Complex Engineered Systems, Carnegie Mellon University, PITA program (2005-2006). Learning from the Past: Improving Estimation of Future Construction Projects. In collaboration with Burcu Akinci, Associate Professor of Civil and Environmental Engineering at Carnegie Mellon University. Carnegie Mellon University Berkman Faculty Development Fund (2001). Perception and Attention Effects on Learning Dynamic Decision Making Tasks. National Institute of Mental Health, training grant (2002-2006). Training in Combined Computational and Behavioral Approaches to Cognition. In collaboration with Lynne Reder, Professor of Psychology and Director of Memory Lab at Carnegie Mellon University. Facilities, Methods, & DMGames The Dynamic Decision Making Laboratory has two separate lab facilities. Our main lab facility is located at 4609 Winthrop Street, 1st floor suite. Winthrop Street intersects with Craig Street and is within walking distance of the main CMU campus. The DDMLab is a network of six Windows machines for data collection. All machines are equipped with a number of devices, graphics, sound cards, and high resolution displays. Our other lab space is on campus in Baker Hall A55A, in the basement of Baker Hall. Our Baker lab consists of a similar set up to our Winthrop lab.  We employ a wide range of research methods including laboratory experiments, computational modeling, eye-tracking, and fMRI. We use the fMRI scanner available to us through the CCBI in the Brain Imaging Research Center, established in 2002. Our main laboratory's research tools are interactive computer simulations of DDM problems. We design and develop these simulations, called Decision Making Games (DMGames), to facilitate the study of learning in dynamic tasks. In the past years, we have created DMGames in many diverse contexts. Below you will find some examples of DMGames for dynamic resource allocation, medical diagnosis, supply chain management, and generic dynamic control of accumulations. DMGames represent a compromise between the experimental control of the laboratory and the realism from real-life decision making (Gonzalez, Vanyukov, & Martin, 2005) and are a powerful tool to study DDM. 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. Dynamic Climate Change Simulator (DCCS) The DCCS was inspired by generic dynamic stocks and flows tasks, and based on a simplified and adapted climate model. The DCCS interface epresents a single stock or accumulation of CO2 in the form of an orange-color liquid in a tank. Deforestation and fossil fuel CO2 emissions, are represented by a pipe connected to the tank, that increase the level of CO2 stock; and CO2 absorptions, also represented as a pipe on the right of the tank, which decreases the level of CO2 stock. The absorptions are outside the direct control of the participant. The absorptions depend on the change in concentration of the CO2 stock and the rate of CO2 transfer parameter derived from the models in the previous section. Available for download at the Wellspring Technology Gateway. Click on the image below to download a video of DCCS in action.
DSF is a generic representation of the basic building blocks of every dynamic system: a single stock that represents accumulation; inflows, which increase the level of stock; and outflows, which decrease the level of stock. A user must maintain the stock at a particular level or, at least, within an acceptable range by contrarresting the effects of the environmental flows. Available for download at the Wellspring Technology Gateway. Click on the image below to download a video of DSF in action.
The Water Purification Plant (WPP) A detailed description of this task can be found in Gonzalez, Lerch, & Lebiere (2003). WPP is a resource allocation and scheduling task. It simulates a water distribution system with 23 tanks arranged in a tree structure and connected with pipes. The goal is to go through the purification process on time before the deadlines expire. Decision makers must manage a limited number of resources (only 5 of the 46 pumps available can be active at a given time) over time to accomplish this goal. With minor modifications, WPP has been used extensively to study automaticity development, stuation awareness, learning, and adaptation. In addition, we developed an ACT-R cognitive model that reproduces human learning in this task (Gonzalez, Lerch, & Lebiere, 2003). For a free download of the software, please visit Carnegie Mellon's Center for Technology Transfer and click on Social Sciences/Economics category. Click on the image below to download a video of WPP in action.
FIRECHIEF (developed by Mary Omodei & Alex Wearing) Firechief is a resource allocation task in which the participant's goal is to minimize the damage caused by the fire to landscape. There are a limited number of the two appliance types (helicopters and firetrucks) used to extinguish fires. The screen shot shows the fire having already consumed a certain percentage of the landscape (black areas of the screen) and continuing to spread (red dots on the screen). The score is the percentage of landscape that has not yet been consumed by fire. The simulation takes place in real-time and is highly dynamic, the environment changing autonomously as well as depending on the actions of the participant. Resources are limited as the appliances eventually run out of water and may need to be refilled, thus causing a delay in their usage. The player needs to learn how and where to concentrate resources in order to save as much of the landscape as possible. Click on the image to download a video of FIRECHIEF in action.
We have created a learning environment that provides users with an interactive experience of supply-chain management. The supply-chain consists of a single retailer who supplies beer to the consumers (simulated as an external demand function), a single wholesaler who supplies beer to the retailer, a distributor who supplies the wholesaler, and a factory that brews the beer (obtaining it from an inexhaustible external supply) and supplies the distributor. Beer game is used extensively to study the way decision makers perform when confronted by dynamic complexity (Sterman, 2004). We use the beer game to study learning and adaptation in DDM. In addition, we have developed an ACT-R cognitive model that reproduces initial data collected on human learning (Martin, Gonzalez, & Lebiere, 2004). Click on the image to download a video of beer game in action.
MEDIC is an interactive tool involving presentation of symptoms, generation of diagnoses, tests of diagnoses, treatments, and outcome feedback. The simulation begins with a patient complaining of symptoms. Each patient has a different initial health level, which continues to fluctuate downward until the patient has been treated or until the patient dies. Since each patient is randomly assigned one of many fictitious diseases, participants must test for the presence of symptoms, each having a different probability of being associated with one of the diseases. Each test returns with a definitive diagnosis (absent or present) after a predetermined time delay for the test to run. The participant provides an assessment of the probability of the presence of the disease. Then, participants can either conduct more tests or administer a treatment. Feedback comprises the actual disease present, the disease the participant believed was present, and a score that represents their accuracy throughout the task. Click on the image to download a video of MEDIC in action.
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Understanding
Conflict with a Socio-Cognitive Computational Approach
Training
Dynamic Decision Making in Mine Emergency Situations
Hypothesis Generation & Reasoning in Dynamic Cyber SA Decision Making
Training Decision Making Skills
Cognitive
Process Modeling and Measurement in Dynamic Decision Making
Hypothesis Generation & Feedback in Dynamic Decision Making
Learning & Adaptation in Complex Decision Making Situations
Neural Basis of Dynamic Decision Making
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The Dynamic Decision Making Laboratory is part of the Social and Decision Sciences Department, Carnegie Mellon University. For updates and comments, please email hauyuw@andrew.cmu.edu. |