Collective Intelligence as Social Community’s Competence and Social Learning of Reality

AbstractWhy groups of individuals sometimes exhibit collective ‘wisdom’ and other times maladaptive ‘herding’ is an enduring conundrum. Here we show that this apparent conflict is regulated by the social learning strategies deployed. We examined the patterns of human social learning through an interactive online experiment with 699 participants, varying both task uncertainty and group size, then used hierarchical Bayesian model-ftting to identify the individual learning strategies exhibited by participants. Challenging tasks elicit greater conformity amongst individuals, with rates of copying increasing with group size, leading to high probabilities of herding amongst large groups confronted with uncertainty. Conversely, the reduced social learning of small groups, and the greater probability that social information would be accurate for less-challenging tasks, generated ‘wisdom of the crowd’ effects in other circumstances. Our model-based approach provides evidence that the likelihood of collective intelligence versus herding can be predicted, resolving a longstanding puzzle in the literature.

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Cognitive diversity promotes social learning - but only when cognitive differences are obscured

10.31235/osf.io/vyrxz ◽

2021 ◽

Author(s):

Douglas Guilbeault ◽

Austin van Loon ◽

Katharina Lix ◽

Amir Goldberg ◽

Sameer Srivastava

Keyword(s):

Social Learning ◽

Collective Intelligence ◽

Social Identities ◽

Cognitive Diversity ◽

Experimental Paradigm ◽

Complex Interplay ◽

Cognitive Differences ◽

Similarities And Differences ◽

Creativity And Innovation ◽

Focal Issue

Cognitive diversity is often assumed to catalyze creativity and innovation by promoting social learning. Yet the learning benefits of cognitive diversity often fail to materialize. Why does cognitive diversity promote social learning in some contexts but not in others? We propose that the answer partly lies in the complex interplay between cognitive diversity and cognitive homophily: The likelihood of individuals learning from one another, and thus changing their views about a substantive issue, depends crucially on whether they are aware of the cognitive similarities and differences that exist between them. When social identities and cognitive associations about concepts related to a focal issue are obscured, we theorize that cognitive diversity will promote social learning by exposing people to novel ideas. When cognitive diversity is instead made salient, we anticipate that a cognitive homophily response is activated that extinguishes cognitive diversity’s learning benefits---even when social identity cues and other categorical distinctions are suppressed. To evaluate these ideas, we introduce a novel experimental paradigm and report the results of four pre-registered studies (N=1,325) that lend support to our theory. We discuss implications for research on social influence, collective intelligence, and cognitive diversity in groups.

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Social learning and partisan bias in the interpretation of climate trends

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1722664115 ◽

2018 ◽

Vol 115 (39) ◽

pp. 9714-9719 ◽

Cited By ~ 27

Author(s):

Douglas Guilbeault ◽

Joshua Becker ◽

Damon Centola

Keyword(s):

Social Networks ◽

Social Learning ◽

Communication Networks ◽

Collective Intelligence ◽

Motivated Reasoning ◽

Climate Data ◽

Climate Trends ◽

Partisan Bias ◽

Belief Polarization ◽

Amazon's Mechanical Turk

Vital scientific communications are frequently misinterpreted by the lay public as a result of motivated reasoning, where people misconstrue data to fit their political and psychological biases. In the case of climate change, some people have been found to systematically misinterpret climate data in ways that conflict with the intended message of climate scientists. While prior studies have attempted to reduce motivated reasoning through bipartisan communication networks, these networks have also been found to exacerbate bias. Popular theories hold that bipartisan networks amplify bias by exposing people to opposing beliefs. These theories are in tension with collective intelligence research, which shows that exchanging beliefs in social networks can facilitate social learning, thereby improving individual and group judgments. However, prior experiments in collective intelligence have relied almost exclusively on neutral questions that do not engage motivated reasoning. Using Amazon’s Mechanical Turk, we conducted an online experiment to test how bipartisan social networks can influence subjects’ interpretation of climate communications from NASA. Here, we show that exposure to opposing beliefs in structured bipartisan social networks substantially improved the accuracy of judgments among both conservatives and liberals, eliminating belief polarization. However, we also find that social learning can be reduced, and belief polarization maintained, as a result of partisan priming. We find that increasing the salience of partisanship during communication, both through exposure to the logos of political parties and through exposure to the political identities of network peers, can significantly reduce social learning.

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A cognitive approach to cumulative technological culture is useful and necessary but only if it also applies to other species

Behavioral and Brain Sciences ◽

10.1017/s0140525x2000014x ◽

2020 ◽

Vol 43 ◽

Author(s):

Thibaud Gruber

Keyword(s):

Social Learning ◽

Cognitive Processes ◽

Learning Processes ◽

Cognitive Approach ◽

Technological Culture ◽

Social Learning Processes

Abstract The debate on cumulative technological culture (CTC) is dominated by social-learning discussions, at the expense of other cognitive processes, leading to flawed circular arguments. I welcome the authors' approach to decouple CTC from social-learning processes without minimizing their impact. Yet, this model will only be informative to understand the evolution of CTC if tested in other cultural species.

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