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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Article
  • Peer Reviewed
Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 35 (2013)
    Cover page: Social Learning in Complex Networks: The Role of Building Blocks and Environmental Change
    • Article
    • Peer Reviewed
    Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 37 (2015)
    Policy decisions on political, economic, legal, and health issues are often made by groups that rarely exceed 40 members and are typically much smaller. Given that wisdom is usually attributed to large crowds, should committees be larger? Using computer simulations and mathematical analyses we show that group accuracy, averaged across the range of difficulty that would be encountered in real-world tasks, is often maximized for moderate-sized groups. The result holds whenever the accuracy in easy tasks is above chance more than the accuracy in difficult tasks is below chance and the easy tasks are encountered more often. Note that for the result to hold, it is not necessary to assume selective sampling of group members according to their individual accuracy. A smaller committee that would produce more accurate decisions in our model can simply be selected randomly out of a larger group of experts.
      Cover page: Wisdom of Randomly Assembled Small Crowds
      • Article
      • Peer Reviewed
      Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 37 (2015)

      From choosing a book to picking a restaurant, most choices people encounter are about “matters of taste” and thus no universal, objective criterion about the options’ quality exists. Tapping into the knowledge of individuals with similar tastes who have already experienced and evaluated options—as harnessed by recommender system algorithms—helps people select options that they will enjoy. Although recommender systems are available in some domains, for most everyday decisions there is neither an algorithm nor “big data” at hand. We mapped recommender system algorithms to models of human judgment and decision making about “matters of fact” and then recast the latter as social recommender strategies for “matters of taste”. This allowed us to investigate how people can leverage the experiences of other individuals to make better decisions when no machine recommender systems are available. Using computer simulations on a widely used data set from the recommender systems literature, we show that experienced individuals can benefit from relying on only the opinions of seemingly similar people. Inexperienced individuals, in contrast, are often well-advised to pick the mainstream option (i.e., the one with the highest average evaluation) even if there are interindividual differences in taste; this is because reliable estimation of similarity requires considerable experience

        Cover page: You're special, but it doesn't matter if you're a greenhorn: Social recommender strategies for mere mortals
        • Article
        • Peer Reviewed
        Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 38 (2016)
        In groups and organizations, agents use both individual and so-cial learning to solve problems. The balance between these twoactivities can lead collectives to very different levels of perfor-mance. We model collective search as a combination of simplelearning strategies to conduct the first large-scale comparativestudy, across fifteen challenging environments and two differ-ent network structures. In line with previous findings in thesocial learning literature, collectives using a hybrid of individ-ual and social learning perform much better than specialistsusing only one or the other. Importantly, we find that collec-tive performance varies considerably across different task en-vironments, and that different types of network structures canbe superior, depending on the environment. These results sug-gest that recent contradictions in the social learning literaturemay be due to methodological differences between two sepa-rate research traditions, studying disjoint sets of environmentsthat lead to divergent findings.
          Cover page: Collective search on rugged landscapes:A cross-environmental analysis
          • Article
          • Peer Reviewed
          UC Merced Previously Published Works (2023)
          We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.
            Cover page: Beyond collective intelligence: Collective adaptation
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