Formal Models of Differential Framing Effects in Decision Making Under Risk

An intriguing finding in the decision-making literature is that, when people have to choose between sure and risky options of equal expected value, they typically take more risks when decisions are framed as losses instead of gains (Tversky &amp; Kahneman, 1981). This framing effect is robust and has important implications for health, finance, and politics. However, theoretical debate exists on the origins of this effect. Moreover, pronounced task-related, individual, and developmental differences exist in the magnitude of the effect. These two issues—theoretical debate and differential framing effects— can be solved together, as an adequate theory of the framing effect should both describe the effect itself and describe differences therein. Therefore, we compare four theories on their capacity to describe differential framing effects: cumulative prospect theory (CPT), fuzzy trace theory (FTT), dual process theory, and a hybrid theory (HT) incorporating elements from lexicographic theory and fuzzy trace theory. First, in a theoretical analysis and empirical review, we build on recent advances in the fields of decision making, brain– behavior relationships, and cognitive development. Second, in an empirical study, we directly compare these theories by using a new experimental task and new analytic approach in which we use hierarchical Bayesian model-based mixture analysis of theories. Taken together, results indicate that differential framing effects are best described by the notion that the majority of decision makers decide according to the hybrid theory, and a sizable minority according to cumulative prospect theory and fuzzy trace theory. We discuss implications of these results for our understanding of the framing effect, and for decision making in general.</p

Bayesian inference for psychology. Part II:Example applications with JASP

Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away