“To hope was to expect”: The impact of perspective taking and forecast type on wishful thinking

When forecasting future outcomes, people tend to believe that the outcomes they want to happen are also likely to happen. Despite numerous attempts, few systematic factors have been identified that consistently and robustly reduce wishful thinking (WT) effects. Using elections and sporting event outcomes as contexts, three experiments examined whether taking the perspective of a political rival or opposing fan reduced WT effects. We also examined whether making deliberative (vs. intuitive-based) forecasts was associated with lower WT effects. Online adult samples of U.S. citizens from Mechanical Turk and U.S. college students provided their preferences and forecasts for the U.S. presidential election (Experiments 1 and 2) and a sports competition outcome (Experiment 3). Critically, some participants received perspective taking prompts immediately before providing forecasts. First, results revealed reductions in WT effects when participants engaged in perspective taking. Interestingly, this effect only emerged when intuitive-based forecasts were made first (Experiment 3). Second, intuitive-based forecasts revealed stronger evidence of WT effects. Finally, we found that perspective taking and forming forecasts deliberately promoted a shift in focus away from preferences and toward a consideration of the relative strengths and weaknesses of the entities (i.e., candidates and teams). Theoretical implications for understanding WT effects and applied implications for developing interventions are discussed.     

Multiattribute Decision Making in Context: A Dynamic Neural Network Methodology

A theoretical structure for multiattribute decision making is presented, based on a dynamical system for interactions in a neural network incorporating affective and rational variables. This enables modeling of problems that elude two prevailing economic decision theories: subjective expected utility theory and prospect theory. The network is unlike some that fit economic data by choosing optimal weights or coefficients within a predetermined mathematical framework. Rather, the framework itself is based on principles used elsewhere to model many other cognitive and behavioral data, in a manner approximating how humans perform behavioral functions. Different, interconnected modules within the network encode (a) attributes of objects among which choices are made, (b) object categories, (c) and goals of the decision maker. An example is utilized to simulate the actual consumer choice between old and new versions of Coca-Cola. Potential applications are also discussed to market decisions involving negotiations between participants, such as international petroleum traders.