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

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  • Timothy van Gelder, John Wilcox (2024): Narrative Abduction, In: SSRN Electronic Journal, doi:10.2139/ssrn.4790721
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  • 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
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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
  • 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
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  • 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
  • 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
  • Enide Maegherman, Karl Ask, Robert Horselenberg, Peter J. van Koppen (2020): Test of the analysis of competing hypotheses in legal <scp>decision‐making</scp>, In: Applied Cognitive Psychology 1(35), doi:10.1002/acp.3738
  • 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, 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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  • 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, Gilly Leshed, Dan Cosley, Susan R. Fussell (2014): Effects of implicit sharing in collaborative analysis, In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, doi:10.1145/2556288.2557229
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