No abstract

Authority and power permeate political, social, and economic life, but empirical knowledge about the motivational origins and consequences of authority is limited. We study the motivation and incentive effects of authority experimentally in an authority-delegation game. Individuals often retain authority even when its delegation is in their material interest—suggesting that authority has nonpecuniary consequences for utility. Authority also leads to overprovision of effort by the controlling parties, while a large percentage of subordinates underprovide effort despite pecuniary incentives to the contrary. Authority thus has important motivational consequences that exacerbate the inefficiencies arising from suboptimal delegation choices. (JEL C92, D23, D82)

The paper investigates whether the methods chosen for representing uncertain geographic information aids or impairs decision-making in the context of wildfire hazard. Through a series of three human subject experiments, utilizing 180 subjects and employing increasingly di cult tasks, this research evaluates the e↵ect of five di↵erent visualizations and a text based representation on decisionmaking under uncertainty. Our quantitative experiments focus specifically on the task of decision-making under uncertainty, rather than the task of reading levels of uncertainty from the map. To guard against the potential for generosity and risk seeking in decision-making under uncertainty, the experimental design uses performance-based incentives. The experiments showed that the choice of representation makes little di↵erence to performance in cases where subjects are allowed the time and focus to consider their decisions. However, with the increasing di culty of time pressure, subjects performed best using a spectral color hue-based representation, rather than more carefully designed cartographic representations. Text-based and simplified boundary encodings were amongst the worst performers. The results have implications for the performance of decision-making under uncertainty using static maps, especially in the stressful environments surrounding an emergency.

No abstract

A common approach to improving probabilistic forecasts is to identify and leverage the forecasts from experts in the crowd based on forecasters' performance on prior questions with known outcomes. However, such information is often unavailable to decision-makers on many forecasting problems, and thus it can be difficult to identify and leverage expertise. In the current paper, we propose a novel algorithm for aggregating probabilistic forecasts using forecasters' meta-predictions about what other forecasters will predict. We test the performance of an extremised version of our algorithm against current forecasting approaches in the literature and show that our algorithm significantly outperforms all other approaches on a large collection of 500 binary decision problems varying in five levels of difficulty. The success of our algorithm demonstrates the potential of using meta-predictions to leverage latent expertise in environments where forecasters' expertise cannot otherwise be easily identified.