When people get ill, they naturally want to restore health through medical interventions. Here I model a situation in which individuals can psychologically entertain multiple potential treatments at once: when illness occurs, individuals would attempt one treatment first, and if it fails to produce an observable effect within a particular time period, a second treatment is attempted, and the eventual recovery is attributed to the treatment that is temporally closer. This creates population dynamics wherein the therapeutic power of the superior/effective (...) medical treatments is misattributed to inferior/ineffective treatments. Through both analytic formulation and agent-based simulation, I show that the equilibrium frequencies of different treatment variants depend on their natural variability in the effect timing, the level of individual patience, and the number of cultural models sampled by the naive individual. Both ineffective and effective medical treatments may stably coexist in the population under a range of parameter settings. (shrink)

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. (shrink)