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Integrating individual and social learning: accuracy and evolutionary viability

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Abstract

Much of what we know, we know thanks to our interactions with others. There is a variety of ways in which we learn from others. We sometimes simply adopt the viewpoints of those we regard as experts, but we also sometimes change our viewpoints in more subtle ways based on the viewpoints of people we regard as our peers. Both forms of social learning have been receiving increasing attention. However, studies investigating how best to combine them, and how to combine the two with individual forms of learning, are still few and far between. This paper looks at ways to integrate various forms of social learning with learning at an individual level within a broadly Bayesian framework. Using agent-based models, we compare the different ways in terms of accuracy of belief states as well as in terms of evolutionary viability. The outcomes of our simulations suggest that agents are best off spending most of their time engaging in social learning, reserving only a limited amount of time for individual learning.

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Data sharing not applicable to this article as no data sets were generated or analyzed during the current study.

Notes

  1. Agents are referred to as “it,” given that we will only be dealing with computational agents.

  2. In the machine learning literature, Hume’s problem has been deepened in the form of Wolpert’s (1996) no-free-lunch theorem (see also Wolpert and Macready 1997), which roughly says that every possible prediction method has the same expected success averaged over all possible worlds. Schurz (2019, Sect. 9.3) proposes a novel solution to the no-free-lunch challenge based on the universal optimality of meta-induction.

  3. Note that, here, all agents always have the same \(\epsilon \) value. So, \(\epsilon \) without the subscript i is to be read as “for all i, \(\epsilon _i = \ldots \)”; similarly for \(\alpha \) and \(\lambda \) further on.

  4. Note that for Carnapians this was true from the beginning. One could also consider the kind of situation in which for them social updating is done strictly on the opinions of the Carnapians within their BCI, but in that kind of situation it does not make sense to ask whether there might be any advantage for Carnapians to be members of a community that also includes meta-inductivists or meritocrats.

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Acknowledgements

We are greatly indebted to three anonymous referees for valuable comments on a previous version of this paper.

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Authors and Affiliations

  1. IHPST/CNRS/Panthéon–Sorbonne University, Paris, France

    Igor Douven

  2. Department of Philosophy, Heinrich Heine University, Düsseldorf, Germany

    Gerhard Schurz

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  1. Igor Douven
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  2. Gerhard Schurz
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Correspondence to Igor Douven.

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The code for the simulations and analyses reported in this paper was written in the scientific computing language Julia (Bezanson et al. 2017). Interested readers can download the code from this repository: https://github.com/IgorDouven/Integrating-Individual-and-Social-Learning.

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Douven, I., Schurz, G. Integrating individual and social learning: accuracy and evolutionary viability. Comput Math Organ Theory 30, 32–74 (2024). https://doi.org/10.1007/s10588-022-09372-1

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