|
on Cognitive and Behavioural Economics |
|
Issue of 2016‒09‒11
five papers chosen by Marco Novarese Università degli Studi del Piemonte Orientale |
| By: | Gonzalez Jimenez, Victor (Tilburg University, Center For Economic Research) |
| Abstract: | This paper studies the effect of incentive schemes incorporating status classes on workers’ performance. I focus on performance comparisons between similarly skilled workers that belong to different status classes. A theoretical framework predicts that, under certain conditions, low ability workers attain high performance when they are assigned to a high rather than a low status class, and that high ability workers achieve high performance irrespective of the received status. These predictions are tested in a laboratory setting, where subjects are randomly assigned to a high status or a low status condition and constant performance feedback is provided. The experimental data support both predictions: low ability subjects assigned to the high status condition outperform their low status counterparts by 0.53 standard deviations in a cognitively challenging task, and high ability subjects display high performance outcomes in both status classes. Moreover, I explore the subjects’ beliefs about performance as a mechanism to explain these results. I find that low ability subjects assigned to the high status exhibit performance targets that were as high as those elicited by high ability participants. This suggests that these workers used status to believe that they were good performers, and performed accordingly. |
| Keywords: | performance; beliefs; experiments; cognition |
| JEL: | D03 C91 D84 M54 Z13 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiucen:25ded0a5-f9c2-48d9-befe-52c011798c21&r=cbe |
| By: | Breaban, Adriana (Tilburg University, Center For Economic Research); van de Kuilen, Gijs (Tilburg University, Center For Economic Research); Noussair, Charles (Tilburg University, Center For Economic Research) |
| Abstract: | We report an experiment to consider the emotional correlates of prudent decision making. In the experiment, we present subjects with lotteries and measure their emotional response with facial recognition software. They then make binary choices between risky lotteries that distinguish prudent from imprudent individuals. They also perform tasks to measure their cognitive ability and a number of personality characteristics. We find that a more negative emotional state correlates with greater prudence. Higher cognitive ability and less conscientiousness is also associated with greater prudence. |
| Keywords: | emotions; prudence; personality; cognitive ability |
| JEL: | C91 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiucen:9a01a5ab-e03d-49eb-9cd7-4d7f878d8737&r=cbe |
| By: | Andreas Hefti; Steve Heinke; Frédéric Schneider |
| Abstract: | We propose that heterogeneous asset trading behavior is the result of two distinct, non-convertible mental dimensions: analytical (“quantitative”) capability and mentalizing (“perspective-taking”) capability. We develop a framework of mental capabilities that yields testable predictions about individual trading behavior, revenue distribution and aggregate outcomes. The two-dimensional structure of mental capabilities predicts the existence of four mental types with distinguishable trading patterns and revenues. Individuals will trade most successfully if and only if they have both capabilities. On the other hand, subjects who can mentalize well but have poor analytical capability will suffer the largest losses. As a consequence, being able in just one dimension does not assure trading success. We test these implications in a laboratory environment, where we first independently elicit subjects’ capabilities in both dimensions and then conduct a standard asset market experiment. We find that individual trading gains and patterns are consistent with our theoretical predictions. Our results suggest that two mental dimensions are necessary to encompass the complex heterogeneous behaviors in asset markets; a one-dimensional measure of mental capability will lead to biased conclusions. The findings have potential implications for financial institutions, which can use the measures to select successful traders, or for policy-makers, helping them to prevent the formation of asset bubbles. Finally, our conceptual framework and the empirical screening method could be applied to explain heterogeneous behavior in other games. |
| Keywords: | Asset markets, heterogeneity, mental capabilities |
| JEL: | G02 C92 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:zur:econwp:234&r=cbe |
| By: | Anufriev, M. (University of Technology, Sydney); Bao, T. (University of Groningen); Tuinstra, J. (University of Amsterdam) |
| Abstract: | We run a laboratory experiment to study how people switch between several profitable alternatives, framed as mutual funds, in order to provide a microfoundation for so-called heterogeneous agent models. The participants in our experiment have to choose repeatedly between two, three or four experimental funds. The time series of fund returns are exogenously generated prior to the experiment and participants are paid for each period according to the return of the fund they choose. For most cases participants' decisions can be successfully described by a discrete choice switching model, often applied in heterogeneous agent models, provided that a predisposition towards one of the funds is included. The estimated intensity of choice parameter of the discrete choice model depends on the structure of the fund returns. In particular, it increases with correlation between past and future returns. This suggests people do not myopically chase past returns, but are more likely to do so when past returns are more predictive of future returns, a feature that is absent in the standard heterogeneous agent models. |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:ams:ndfwpp:15-09&r=cbe |
| By: | Hommes, C.H. (University of Amsterdam); Makarewicz, T.A. (University of Amsterdam); Massaro, D. (University of Amsterdam); Smits, T. (SEO Economic Research) |
| Abstract: | In order to understand heterogeneous behaviour amongst agents, empirical data from Learning-to-Forecast (LtF) experiments can be used to construct learning models. This paper follows up on Assenza et al. (2013) by using a genetic algorithms (GA) model to replicate the results from their LtF experiment. In this GA model individuals optimise an adaptive, a trend following and an anchor coefficient in a population of general prediction heuristics. We replicate experimental treatments in a New-Keynesian environment with increasing complexity and use Monte Carlo simulations to investigate how well the model explains the experimental data. We find that the model is able to replicate the three different types of behaviour in the treatments using one GA model. The research furthermore shows that heterogeneous behaviour can be explained by an adaptive, anchor and trend extrapolating component and therewith contributes to the existing literature in the way that GA can be used to explain heterogeneous behaviour in LtF experiments with different types of complexity. |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:ams:ndfwpp:15-01&r=cbe |