Overcoming Individual Limitations Through Distributed Computation: Rational Information Accumulation in Multigenerational Populations

• Published in: Topics in cognitive science Vol. 14; no. 3; pp. 550 - 573
• Main Authors: Hardy, Mathew D., Krafft, Peaks M., Thompson, Bill, Griffiths, Thomas L.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.07.2022
• Subjects: Bayes Theorem; Bayesian inference; Collective intelligence; Cultural evolution; Distributed computation; Group rationality; Humans; Intelligence; Knowledge; Rationality; Social learning; Collective intelligence; Distributed computation; Social learning; Cultural evolution; Group rationality; Bayesian inference
• ISSN: 1756-8757; 1756-8765; 1756-8765
• DOI: 10.1111/tops.12596

Many of the computational problems people face are difficult to solve under the limited time and cognitive resources available to them. Overcoming these limitations through social interaction is one of the most distinctive features of human intelligence. In this paper, we show that information accumulation in multigenerational social networks can be produced by a form of distributed Bayesian inference that allows individuals to benefit from the experience of previous generations while expending little cognitive effort. In doing so, we provide a criterion for assessing the rationality of a population that extends traditional analyses of the rationality of individuals. We tested the predictions of this analysis in two highly controlled behavioral experiments where the social transmission structure closely matched the assumptions of our model. Participants made decisions on simple categorization tasks that relied on and contributed to accumulated knowledge. Success required these microsocieties to accumulate information distributed across people and time. Our findings illustrate how in certain settings, distributed computation at the group level can pool information and resources, allowing limited individuals to perform effectively on complex tasks. Blurb: Many of the problems people face are difficult to solve under the limited time and resources available to them. We show that individuals can overcome these limitations by following a simple social learning heuristic that yields distributed Bayesian inference at the population level. We test our model in two large behavioral experiments, comparing observed knowledge accumulation with the Bayesian ideal in multigenerational microsocieties.