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 CO2 in the atmosphere, the “inflow” via anthropogenic CO2 emissions, and the “outflow” via natural CO2 absorption. This study addresses potential causes of reasoning failures in the CO2 accumulation problem and reports two experiments involving a simple re-framing of the task as managing an analogous financial 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 CO2 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 CO2 emissions is briefly discussed. (shrink)

We argue that standard experiments supporting the existence of do not represent many cooperative situations outside the laboratory. More representative experiments that incorporate rather than wealth also do not provide evidence for the impact of strong reciprocity on cooperation in contemporary real-life situations or in evolutionary history, supporting the main conclusions of the target article.

Bayesian accounts are currently popular in the field of inductive reasoning. This commentary briefly reviews the limitations of one such account, the Rational Model (Anderson 1991b), in explaining how inferences are made about objects whose category membership is uncertain. These shortcomings are symptomatic of what Jones & Love (J&L) refer to as Bayesian approaches.

We focus on two issues: (1) an unusual, counterintuitive prediction that quantum probability (QP) theory appears to make regarding multiple sequential judgments, and (2) the extent to which QP is an appropriate and comprehensive benchmark for assessing judgment. These issues highlight how QP theory can fall prey to the same problems of arbitrariness that Pothos & Busemeyer (P&B) discuss as plaguing other models.

Mitchell et al. present a lucid and provocative challenge to the claim that links between mental representations are formed automatically. However, the propositional approach they offer requires clearer specification, especially with regard to how propositions and memories interact. A definition of a system would also clarify the debate, as might an alternative technique for assessing task.

In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the (...) names of the cities, but they were able to collect different kinds of cues for both response alternatives (e.g., “Does this city have a university?”) before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, ) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take–the–best or the Weighted Additive heuristics. (shrink)