Journal Article

The CACHE Study: Group Effects in Computer-supported Collaborative Analysis

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Date

2008, 2008

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Source

Computer Supported Cooperative Work (CSCW): Vol. 17, No. 4

Publisher

Springer

Abstract

The present experiment investigates effects of group composition in computer-supported collaborative intelligence analysis. Human cognition, though highly adaptive, is also quite limited, leading to systematic errors and limitations in performance – that is, biases . We experimentally investigated the impact of group composition on an individual’s bias, by composing groups that differ in whether their members initial beliefs are diverse (heterogeneous group) or similar (homogeneous group). We study three-member, distributed, computer-supported teams in heterogeneous, homogeneous, and solo (or nominal) groups. We measured bias in final judgment, and also in the selection and evaluation of the evidence that contributed to the final beliefs. The distributed teams collaborated via CACHE-A, a web-based software environment that supports a collaborative version of Analysis of Competing Hypotheses (or ACH, a method used by intelligence analysts). Individuals in Heterogeneous Groups showed no net process cost, relative to noninteracting individuals. Both heterogeneous and solo (noninteracting) groups debiased strongly, given a stream of balanced evidence. In contrast, individuals in Homogenous Groups did worst, accentuating their initial bias rather than debiasing. We offer suggestions about how CACHE-A supports collaborative analysis, and how experimental investigation in this research area can contribute to design of CSCW systems.

Description

Convertino, Gregorio; Billman, Dorrit; Pirolli, Peter; Massar, J. P.; Shrager, Jeff (2008): The CACHE Study: Group Effects in Computer-supported Collaborative Analysis. Computer Supported Cooperative Work (CSCW): Vol. 17, No. 4. DOI: 10.1007/s10606-008-9080-9. Springer. PISSN: 1573-7551. pp. 353-393

Keywords

CACHE, collaboration, CSCW, group bias, group decision-making, intelligence analysis

Citation

DOI

10.1007/s10606-008-9080-9

URI

URI

http://dx.doi.org/10.1007/s10606-008-9080-9
 https://dl.eusset.eu/handle/20.500.12015/4009

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JCSCW Vol. 17 (2008)

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Referenced By

Number of citations to item: 35

  • Daniel M. Russell, Gregorio Convertino, Aniket Kittur, Peter Pirolli, Elizabeth Anne Watkins (2018): Sensemaking in a Senseless World, In: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, doi:10.1145/3170427.3170636
  • Mandeep K. Dhami, Ian K. Belton, David R. Mandel (2019): The “analysis of competing hypotheses” in intelligence analysis, In: Applied Cognitive Psychology 6(33), doi:10.1002/acp.3550
  • Anna Wu, Gregorio Convertino, Craig Ganoe, John M. Carroll, Xiaolong (Luke) Zhang (2013): Supporting collaborative sense-making in emergency management through geo-visualization, In: International Journal of Human-Computer Studies 1(71), doi:10.1016/j.ijhcs.2012.07.007
  • Daniel M. Russell, Laura Koesten, Aniket Kittur, Nitesh Goyal, Michael Xieyang Liu (2024): Sensemaking: What is it today?, In: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, doi:10.1145/3613905.3636322
  • Pradeep K. Murukannaiah, Anup K. Kalia, Pankaj R. Telangy, Munindar P. Singh (2015): Resolving goal conflicts via argumentation-based analysis of competing hypotheses, In: 2015 IEEE 23rd International Requirements Engineering Conference (RE), doi:10.1109/re.2015.7320418
  • Jennifer Stromer-Galley, Patricia Rossini, Kate Kenski, Brian McKernan, Benjamin Clegg, James Folkestad, Carsten Østerlund, Lael Schooler, Olga Boichak, Jordan Canzonetta, Rosa Mikeal Martey, Corey Pavlich, Eric Tsetsi, Nancy McCracken (2020): Flexible versus structured support for reasoning: enhancing analytical reasoning through a flexible analytic technique, In: Intelligence and National Security 2(36), doi:10.1080/02684527.2020.1841466
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  • Gregorio Convertino, Lichan Hong, Les Nelson, Peter Pirolli, Ed H. Chi (2009): Activity Awareness and Social Sensemaking 2.0: Design of a Task Force Workspace, In: Lecture Notes in Computer Science, doi:10.1007/978-3-642-02812-0_16
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  • Feng Li, Nian Liu, Wei Jiang, Xiaoqing Liu (2013): Argument placement recommendation and relevancy assessment in an intelligent argumentation system, In: 2013 International Conference on Collaboration Technologies and Systems (CTS), doi:10.1109/cts.2013.6567265
  • Mandeep K. Dhami, Ian K. Belton, Peter De Werd, Velichka Hadzhieva, Lars Wicke (2024): Effects of task structure and confirmation bias in alternative hypotheses evaluation, In: Cognitive Research: Principles and Implications 1(9), doi:10.1186/s41235-024-00560-y
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  • Jijie Huang, Changnian Lin, Haiming Zhou, Zhengqing Xu, Chunzhe Lin (2019): Research on key technologies of deduction of multinational power trading in the context of Global Energy Interconnection, In: Global Energy Interconnection 6(2), doi:10.1016/j.gloei.2020.01.010
  • Jeff Shrager, Dorrit Billman, Gregorio Convertino, J. P. Massar, Peter Pirolli (2010): Soccer Science and the Bayes Community: Exploring the Cognitive Implications of Modern Scientific Communication, In: Topics in Cognitive Science 1(2), doi:10.1111/j.1756-8765.2009.01049.x
  • Anna De Liddo, Ágnes Sándor, Simon Buckingham Shum (2011): Contested Collective Intelligence: Rationale, Technologies, and a Human-Machine Annotation Study, In: Computer Supported Cooperative Work (CSCW) 4-5(21), doi:10.1007/s10606-011-9155-x
  • Zhigang Xu, Qi Li, Xinhua Dong, Hongmu Han, Zhongzhen Yan, Haitao Wang (2022): Incentive-compatible Intelligence Collaboration Analysis Framework Based on Blockchain and Evolutionary Game, In: 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta), doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00288
  • Derek Sleeman, Laura Moss, Andy Aiken, Martin Hughes, John Kinsella, Malcolm Sim (2012): Detecting and resolving inconsistencies between domain experts’ different perspectives on (classification) tasks, In: Artificial Intelligence in Medicine 2(55), doi:10.1016/j.artmed.2012.03.001
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  • Gregorio Convertino, Helena M. Mentis, Aleksandra Slavkovic, Mary Beth Rosson, John M. Carroll (2011): Supporting common ground and awareness in emergency management planning, In: ACM Transactions on Computer-Human Interaction 4(18), doi:10.1145/2063231.2063236
  • Mark Klein (2015): A Critical Review of Crowd-Scale Online Deliberation Technologies, In: SSRN Electronic Journal, doi:10.2139/ssrn.2658811
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  • Nitesh Goyal, Susan R. Fussell (2016): Effects of Sensemaking Translucence on Distributed Collaborative Analysis, In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work &amp; Social Computing, doi:10.1145/2818048.2820071
  • ZhenJiang Lei, JiJie Huang, Zhao Li, Lei Wang, JiSheng Cui, Zhi Tang (2018): Research on Collaborative Technology in Distributed Virtual Reality System, In: Journal of Physics: Conference Series, doi:10.1088/1742-6596/960/1/012016
  • Jeremy Lee Pennington (2015): Probable Event Analysis (PEA): A Proposed Structured Analysis Methodology, In: SSRN Electronic Journal, doi:10.2139/ssrn.2655408
  • Peter Eachus, Ben Short, Alex W. Stedmon, Jennie Brown, Margaret Wilson, Lucy Lemanski (2013): A Collaborative Multi-source Intelligence Working Environment: A Systems Approach, In: Lecture Notes in Computer Science, doi:10.1007/978-3-642-39360-0_31
  • Utpal Bose (2015): Design and evaluation of a group support system supported process to resolve cognitive conflicts, In: Computers in Human Behavior, doi:10.1016/j.chb.2015.03.014
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