Dataset Information

Collaborative reasoning in the context of group competition.

ABSTRACT: A key skill in collaborative problem-solving is to communicate and evaluate reasons for proposals to arrive at the decision benefiting all group members. Although it is well-documented that collaborative contexts facilitate young children's reasoning, less is known about whether competition with other groups contributes to children's collaborative reasoning. We investigated whether between-group competition facilitates children's within-group collaborative reasoning, regarding their production of reasons and their use of transacts, communicative acts that operate on one another's proposals and reasoning. We presented 5- and 7-year-old peer dyads with two collaborative problem-solving tasks (decorating a zoo and a dollhouse). In one task, children competed against another group (the competitive condition); whereas in the other task, they did not (non-competitive condition). Our results suggest that children's sensitivity to group competition as reflected in their reasoning changed depending on the task. When they decorated a house, they produced more transacts in the competitive condition than in the non-competitive condition; whereas when they decorated a zoo, this pattern was reversed. Thus, our results highlight that group competition did not influence children's collaborative reasoning consistently across different contexts.

SUBMITTER: Domberg A

PROVIDER: S-EPMC7864449 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Collaborative reasoning in the context of group competition.

Domberg Andreas A Tomasello Michael M Köymen Bahar B

PloS one 20210205 2

A key skill in collaborative problem-solving is to communicate and evaluate reasons for proposals to arrive at the decision benefiting all group members. Although it is well-documented that collaborative contexts facilitate young children's reasoning, less is known about whether competition with other groups contributes to children's collaborative reasoning. We investigated whether between-group competition facilitates children's within-group collaborative reasoning, regarding their production o ...[more]

PMID: 33544768

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Project description:US intelligence analysts must weigh up relevant evidence to assess the probability of their conclusions, and express this reasoning clearly in written reports for decision-makers. Typically, they work alone with no special analytic tools, and sometimes succumb to common probabilistic and causal reasoning errors. So, the US government funded a major research program (CREATE) for four large academic teams to develop new structured, collaborative, software-based methods that might achieve better results. Our team's method (BARD) is the first to combine two key techniques: constructing causal Bayesian network models (BNs) to represent analyst knowledge, and small-group collaboration via the Delphi technique. BARD also incorporates compressed, high-quality online training allowing novices to use it, and checklist-inspired report templates with a rudimentary AI tool for generating text explanations from analysts' BNs. In two prior experiments, our team showed BARD's BN-building assists probabilistic reasoning when used by individuals, with a large effect (Glass' ? 0.8) (Cruz et al., 2020), and even minimal Delphi-style interactions improve the BN structures individuals produce, with medium to very large effects (Glass' ? 0.5-1.3) (Bolger et al., 2020). This experiment is the critical test of BARD as an integrated system and possible alternative to business-as-usual for intelligence analysis. Participants were asked to solve three probabilistic reasoning problems spread over 5 weeks, developed by our team to test both quantitative accuracy and susceptibility to tempting qualitative fallacies. Our 256 participants were randomly assigned to form 25 teams of 6-9 using BARD and 58 individuals using Google Suite and (if desired) the best pen-and-paper techniques. For each problem, BARD outperformed this control with very large to huge effects (Glass' ? 1.4-2.2), greatly exceeding CREATE's initial target. We conclude that, for suitable problems, BARD already offers significant advantages over both business-as-usual and existing BN software. Our effect sizes also suggest BARD's BN-building and collaboration combined beneficially and cumulatively, although implementation differences decreased performances compared to Cruz et al. (2020), so interaction may have contributed. BARD has enormous potential for further development and testing of specific components and on more complex problems, and many potential applications beyond intelligence analysis.

Dataset Information

Collaborative reasoning in the context of group competition.

Publications

Collaborative reasoning in the context of group competition.

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