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Current Projects Facilities, Methods, & DMGames
Selected Publications
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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 look into 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) and involving extended practice to understand how experience develops, changes, and transfers. Also, we create ACT-R cognitive computational models that reproduce human behavior. PeaceMaker-Based Research for Decision Making & Diplomacy Funding Source: PeaceMaker-Based Research for Decision Making & Diplomacy. Richard Lounsbery Foundation. Project Researchers: Cleotilde Gonzalez (PI) & Lisa Czlonka (Research Associate) External Collaborators: Kiron Skinner (Co-PI), Associate Professor of Social and Decision Sciences, History, Carnegie Mellon University & Laurie Eisenberg, Associate Teaching Professor of History, Carnegie Mellon University Using PeaceMaker, a tool developed by ImpactGames, we study world peace using a game-theoretic approach. We collect behavioral data from a large diversity of individuals with different political affiliations, personality and leadership backgrounds, and religious beliefs. A game theoretic approach helps us study war as a problem in which equilibrium must be reached, and decision making strategies must be used to attain peace. Determinants of Public Confidence in Government to Prevent Terrorism Funding Source: Argonne National Labs Project Researchers: Cleotilde Gonzalez (PI), Varun Dutt (Doctoral Student), & Lisa Czlonka (Research Associate) External Collaborators: Ignacio Martinez-Moyano , Argonne National Labs & Michael Samsa , Argonne National Labs The allocation of resources to prevent terrorist attacks can be guided by our understanding of how much citizens value their confidence in government. In this research, we investigate how the impact of a terrorist attack and characteristics of the general population would influence their confidence in government. We collect data on the level of confidence in government before and after participants watch a mockup video of a terrorist event in the USA. We also collect information on the emotional content and the personality characteristics that could influence the confidence in government. Training Decision Making Skills Funding Source: Training Knowledge and Skills for the Networked Battlefield. Army Research Office, Multidisciplinary University Research Inititative. Project Researchers: Cleotilde Gonzalez (Co-PI) External Collaborators: Alice Healy (PI), Professor of Psychology, University of Colorado at Boulder & Lyle Bourne (Co-PI), Professor of Psychology Emeritus and Faculty Fellow of Institute of Cognitive Science, University of Colorado at Boulder Our goal in this project is to create ACT-R computational models that will be used as predictive tools for the different effects resulting from the application of empirically-based training principles. Currently are investigating the speed-accuracy trade-offs resulting from fatigue in a task with extended practice.
Funding Source: Cognitive Process Modeling and Measurement in Dynamic Decision Making. Sponsored by the U.S. Army Research Laboratory. Project Researchers: Cleotilde Gonzalez (PI) & Lelyn Saner (Post-Doctoral Fellow) External Collaborator: Mica Endsley (Co-PI), President, SA technologies We study Situation Awareness (SA) in military scenarios. A focus of this project is to better understand how SA develops with practice and changes as learning occurs. Under this project we conduct behavioral studies using dynamic decision making simulations and generate computational representations of SA. |
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The Basic Building Blocks of Dynamic Systems Funding Source: Hypothesis Generation and Feedback in Dynamic Decision Making. National Science Foundation, Human and Social Dynamics Priority Area. Project Researchers: Cleotilde Gonzalez (PI), Angela Brunstein (Post-Doctoral Fellow), Polina Vanyukov (Doctoral Student), & Varun Dutt (Doctoral Student) External Collaborators: John Sterman , Jay W. Forrester Professor of Management and Director of MIT System Dynamics Group , Massachusetts Institute of Technology & Matt Cronin , Assistant Professor of Management, George Mason University Stock and flows— resources that accumulate or deplete and the flows that alter them— are ubiquitous, and understanding them is fundamental in business, our personal life, and society. Our work shows that many find stocks and flows unintuitive and rely on incorrect assumptions to solve these problems. This project aims at determining why people find the basic stock and flows difficult. Specifically, we study how experience may help people learn to detect the correct cause-effect relationships. Hypothesis Generation & Feedback Funding Source: Hypothesis Generation and Feedback in Dynamic Decision Making. National Science Foundation, Human and Social Dynamics Priority Area. Project Researchers: Cleotilde Gonzalez (PI) & Colleen Vrbin (Research Associate) External Collaborators: Rickey Thomas (Co-PI), Assistant Professor of Cognitive Psychology, University of Oklahoma & Robert Hamm (Co-PI), Professor of Family and Preventive Medicine and Director of Clinical Decision Making Program, University of Oklahoma Health Sciences Center This research will improve our theoretical understanding of the dynamics of human behavior through laboratory studies using artificial and realistic task domains and through computational cognitive modeling. This research contributes directly to understanding the dynamics of two very basic mechanisms of decision making – how people come to generate hypotheses from cues while those cues and the situation evolve over time and how feedback of different kinds changes individuals’ dynamic decision making behavior. Learning & Adaptation in Complex Decision Making Situations Project Researchers: Cleotilde Gonzalez & Alex Persoskie (Doctoral Student) The Instance-Based Learning Theory (IBLT) (Gonzalez, Lerch, & Lebiere, 2003) proposes that in dynamic environments learning occurs through the accumulation and refinement of decision instances and that decision making is based on the retrieval of solutions stored in similar memory instances. Automaticity is a phenomenon strongly related to learning. It is acquired with extended and consistent practice. A tool frequently used in our lab to study automaticity is the luggage screening task, a computer simulation testing both memory and visual detection. The Transportation Safety Administration (TSA) provided the initial x-ray object images, which we then compiled into consolidated bags and pre-tested for difficulty level. The task is a precise representation of the work environment of an airport screener. Another phenomenon is the bullwhip effect, a decision making phenomenon occuring in real world supply chains. The difficulty is magnified as one moves up the supply chain from retailer to wholesaler to factory, resulting in a costly oscillating pattern of backorders and inventories. Extensive economic research has identified structural causes and possible solutions. From a psychological perspective, we show how particular decision making processes interact with a complex decision environment to enhance and attenuate the bullwhip effect. Past experimental results suggest practical ways to facilitate successful supply chain management learning. Most experiments involve management of a supply chain through our Beer Game simulation.
Project Researchers: Cleotilde Gonzalez & Wei Siong Neo (Doctoral Student) With the help of the Center for Cognitive Brain Imaging (CCBI) at Carnegie Mellon University, we have used functional MRI to study risk in simple choice problems. In Gonzalez, Dana, Koshino, and Just (2005), we report brain activity due to the choice of certain and risky options in positively and negatively framed problems. Our results indicate that differential brain activity is influenced by the greater emotional content of the negative frame compared to the positive frame, and in the risk frame influence is due to the greater uncertainty in the risky versus the certain choice. The main lesson from this study is that cognition and emotion don't work independently, but rather they clearly interact to determine choice.
U.S. Army Research Laboratory. (2001-2006) Cognitive Process Modeling and Measurement in Dynamic Decision Making. In collaboration with Mica Endsley, President, 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), Carnegie Mellon University, Walter Schneider, Professor of Psychology, University of Pittsburgh , & Poornima Madhavan, Assistant Professor of Psychology, 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, SA technologies & Cheryl Bolstad, Senior Research Associate, 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, 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, Carnegie Mellon University. Facilities, Methods, & DMGames 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. 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, suply 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. 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. Click on the image below to see a video of DSF in action. |
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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 see a video of WPP in action. |
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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 it 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 see a video of FIRECHIEF in action. |
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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 see a video of beer game in action. |
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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 see a video of MEDIC in action.
Selected Publications Gonzalez, C., & Thomas, R. (2008). Effects of automatic detection on dynamic decision making. Journal of Cognitive Engineering and Decision Making. Best, B. J., Gonzalez, C., Young, M. D., Healy, A. F., & Bourne, L. E., Jr. (2007). Modeling automaticity and strategy selection in dynamic visual detection. In Proceedings of the Sixteenth Conference on Behavior Representation in Modeling and Simulation (pp. 3-11). Orlando, FL: Simulation Interoperability Standards Organization. Gonzalez, C., & Vrbin, C. (2007). Learning in MEDIC: A dynamic simulation of medical diagnosis. In A. Holzinger (Ed.), Usability & HCI for Medicine and Health Care. 3rd Symposium of the Austrian Computer Society (Lecture Notes in Computer Science, vol. 4799), ISBN:978-3-540-76804-3. Kiziltas, S., Akinci, B., & Gonzalez, C. (2007). Understanding differences in information needs of expert and novice estimators from construction project histories. The 2007 ASCE Construction Research Congress, Grand Bahama Island, May 6-8. Young, M. D., Healy, A. F., Gonzalez, C., & Bourne, L. E., Jr. (2007). The effects of training difficulty on RADAR detection. Paper presented at the joint meeting of the Experimental Psychology Society and the Psychonomic Society, Edinburgh, Scotland, July 4-7. Gonzalez, C., Fu, W., Healy, A. F., Kole, J. A., & Bourne, L. E., Jr. (2006). ACT-R models of training data entry skills. In Proceedings of the Fifteenth Conference on Behavior Representation in Modeling and Simulation (pp. 101-109). Orlando, FL: Simulation Interoperability Standards Organization. Gonzalez, C., Juarez, O., Endsley, M., & Jones, D. (2006). Cognitive models of situation awareness: Automatic evaluation of situation awareness in graphic interfaces. In Proceedings of the Fifteenth Conference on Behavior Representation in Modeling and Simulation (pp. 45-54). Orlando, FL: Simulation Interoperability Standards Organization. Cronin, M., Gonzalez, C., & Sterman, J. D. (2006). Difficulties understanding system dynamics: A challenge to researchers, educators and citizens. In K. M. Weaver (Ed.), 2006 Academy of Management Annual Meeting (Available on CD-ROM). Briarcliff Manor, NY: Academy of Management. Gonzalez, C., Juarez, O., & Graham, J. (2004). Cognitive and computational models as tools to improve situation awareness. Proceedings of the 48th Annual Meeting of the Human Factors and Ergonomics Society. New Orleans, LA, September Juarez, O., & Gonzalez, C. (2004). Situation awareness of commanders: A cognitive model. Proceedings of the Conference on Behavior Representation in Modeling and Simulation. Arlington, VA, May. Martin, M. K., Gonzalez, C., & Lebiere, C. (2004). Learning to make decisions in dynamic environments: ACT-R plays the Beer Game. Proceedings of the 6th International Conference on Cognitive Modeling. Pittsburgh, PA, July–August. Gonzalez, C. (2003). Verbal protocols from novices and experts in dynamic decision making. Proceedings of the 47th Annual Meeting of the Human Factors and Ergonomics Society. Denver, CO, October. de Souza, C., Sanchez, A., Barbosa, S., & Gonzalez, C. (2003). Proceedings of the Latin American conference on human-computer interaction (ACM International Conference Proceedings Series, Vol. 46). New York: ACM Press. |
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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 lczlonka@andrew.cmu.edu. |
