Abstract
Most research on transmission biases in cultural evolution has treated different biases as distinct strategies. Here I present a model that combines both frequency dependent bias (including conformist bias) and payoff bias in a single decision-making calculus and show that such an integrated learning strategy may be superior to relying on either bias alone. Natural selection may operate on humans’ relative dependence on frequency and payoff information, but both are likely to contribute to the spread of variants with high payoffs. Importantly, the magnitude of conformist bias affects the evolutionary dynamics, and I show that an intermediate level of conformity may be most adaptive and may spontaneously evolve as it resists the invasion of low-payoff variants yet enables the fixation of high-payoff variants in the population.
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The graphical representations of all equations are created using python 3.7. The final agent-based simulation for the evolution of epistemic weights (shown in Fig. 3) is created using Julia 1.5.0. All codes are available at https://github.com/kevintoy/epistemic_weight_evo.
Notes
For example, suppose X1/X2, Y1/Y2, and Z1/Z2 denote different components of the same technology; for human populations to progressively achieve higher payoff, evolution needs to figure out the superior variant for each component and “lock onto it.” This process will be modeled later with an agent-based simulation approach.
These assumptions will also be relevant in the agent-based simulation discussed below.
The evolutionary dynamics shown in Fig. 1 resemble that of anticonformist transmission, though no conformity-related bias has been introduced. This is because the payoff component causes the population to evolve toward \(\frac{{b}_{1}}{\left({b}_{1}+{b}_{2}\right)}\), and thus the rare variant can increase in frequency.
Again, this result stems from the construction of Eq. 1 and may not hold for other formulations.
There has also been some discussion of negative D values (i.e., anticonformity). For example, see Denton et al. (2020).
Due to the way conformist bias is constructed here (as a single coefficient with maximum \(D=1\)), a variant sampled in \(\frac{{n}_{1}}{n}\) models, where \({n}_{1}>{n}_{2}\), can be adopted with a probability of at most \(\frac{{n}_{1}+1}{n}\). Thus, as \(n\) becomes large, the effect of conformity becomes weaker.
Realistically speaking, the time interval at which the new variant appears involves some random component, but in the present simulation a fixed interval is used because it does not qualitatively change the nature of the simulation and allows for better visual inspection of evolutionary trends.
Note that fitness was only incorporated in the agent-based simulation and not the first part of the model.
Though such cost is not incorporated in the present model.
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Acknowledgments
I thank Dr. Joseph Henrich for his continued encouragement and support for this project, and Peter Park and two anonymous reviewers for their very constructive feedback on earlier drafts of this manuscript.
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Ze Hong was supported by a grant from the John Templeton Foundation and the Issachar Fund.
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Hong, Z. Combining Conformist and Payoff Bias in Cultural Evolution. Hum Nat 33, 463–484 (2022). https://doi.org/10.1007/s12110-022-09435-x
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DOI: https://doi.org/10.1007/s12110-022-09435-x