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The majority of people show persistent poor performance in reasoning about "stock-flow problems" in the laboratory. An important example is the failure to understand the relationship between the "stock" of CO 2 in the atmosphere, the "inflow" via anthropogenic CO 2 emissions, and the "outflow" via natural CO 2 absorption. This study addresses potential causes of reasoning failures in the CO 2 accumulation problem and reports two experiments involving a simple re-framing of the task as managing an analogous financial (rather than CO 2 ) budget. In Experiment 1 a financial version of the task that required participants to think in terms of controlling debt demonstrated significant improvements compared to a standard CO 2 accumulation problem. Experiment 2, in which participants were invited to think about managing savings, suggested that this improvement was fortuitous and coincidental rather than due to a fundamental change in understanding the stock-flow relationships. The role of graphical information in aiding or abetting stock-flow reasoning was also explored in both experiments, with the results suggesting that graphs do not always assist understanding. The potential for leveraging the kind of reasoning exhibited in such tasks in an effort to change people's willingness to reduce CO 2 emissions is briefly discussed.

How does the process of information transmission affect the cultural or linguistic products that emerge? This question is often studied experimentally and computationally via iterated learning, a procedure in which participants learn from previous participants in a chain. Iterated learning is a powerful tool because, when all participants share the same priors, the stationary distributions of the iterated learning chains reveal those priors. In many situations, however, it is unreasonable to assume that all participants share the same prior beliefs. We present four simulation studies and one experiment demonstrating that when the population of learners is heterogeneous, the behavior of an iterated learning chain can be unpredictable and is often systematically distorted by the learners with the most extreme biases. This results in group-level outcomes that reflect neither the behavior of any individuals within the population nor the overall population average. We discuss implications for the use of iterated learning as a methodological tool as well as for the processes that might have shaped cultural and linguistic evolution in the real world.

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