|
on Cognitive and Behavioural Economics |
|
Issue of 2025–02–24
five papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Erica Ordali; Chiara Rapallini |
| Abstract: | Decades of research have assumed the stability of risk preferences across domains and ages. However, recent evidence has shown that it might not be the case since variations in the level of risks taken are, in fact, observable. Economics and Psychology literature investigated such issues, providing mixed evidence regarding age changes. This paper provides the first exhaustive meta-analytical review of the economic and psychology literature results regarding the association between aging and financial risk attitudes. We find differences in the effect mainly due to the methods used for measuring risk preferences. In particular, we find that the positive association between risk aversion and age is verified for survey data and lotteries, while psychological tasks underline the role played by the learning process and, ultimately, that cognitive abilities and health status may affect preferences. The meta-regression on effect sizes derived from studies based on surveys shows that cognitive abilities and healthstatus explain a significant part of the heterogeneity of this sample of studies. |
| Keywords: | Ageing, financial risk-taking, meta-analysis, survey data, lottery, task |
| JEL: | J1 D91 D81 D01 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:frz:wpaper:wp2024_27.rdf |
| By: | Marco Caliendo (Director of Research at the Institute for the Study of Labor (IZA) in Bonn and affiliated with DIW Berlin and IAB - Director of Research at the Institute for the Study of Labor (IZA) in Bonn and affiliated with DIW Berlin and IAB); Deborah Cobb-Clark; Juliana Silva-Goncalves; Arne Uhlendorff (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We conduct a laboratory experiment to study how locus of control operates through people's preferences and beliefs to influence their decisions. Using the principal–agent setting of the delegation game, we test four key channels that conceptually link locus of control to decision-making: (i) preference for agency, (ii) optimism and (iii) confidence regarding the return to effort, and (iv) illusion of control. Knowing the return and cost of stated effort, principals either retain or delegate the right to make an investment decision that generates payoffs for themselves and their agents. Extending the game to the context in which the return to stated effort is unknown allows us to explicitly study the relationship between locus of control and beliefs about the return to effort. We find that internal locus of control is linked to the preference for agency, an effect that is driven by women. We find no evidence that locus of control influences optimism and confidence about the return to stated effort, or that it operates through an illusion of control. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:halshs-04793394 |
| By: | Paul Grass (University of Bonn); Philipp Schirmer (University of Bonn); Malin Siemers (University of Bonn) |
| Abstract: | People often form mental models based on incomplete information, revising them as new relevant data becomes available. In this paper, we experimentally investigate how individuals update their models when data on predictive variables are gradually revealed. We find that people's models tend to be `sticky, ' as their final models remain strongly influenced by earlier models formed using a subset of variables. Guided by a simple framework highlighting the role of attention in shaping model revisions, we document that only participants who exert lower cognitive effort during the revising stage, relative to the initial model formation stage - as proxied by time spent - exhibit significant model stickiness. Additionally, subjects' final models are strongly predicted by their reasoning type - their self-described approach to extracting models from multidimensional data. While model stickiness varies across reasoning types, effort allocation across stages remains a strong predictor of stickiness even when accounting for reasoning. |
| Keywords: | Mental models, learning dynamics, attention, mental representation, bounded rationality |
| JEL: | D83 D91 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:355 |
| By: | Claudia Soucek (Heinrich-Heine-University Duesseldorf - Institute for Health Services Research and Health Economics, Moorenstr. 5, 40225 Duesseldorf, Germany); Tommaso Reggiani (Cardiff Business School, Cardiff University, Cardiff, United Kingdom; Masaryk University, Brno, Czechia; IZA, Bonn, Germany); Nadja Kairies-Schwarz (Heinrich-Heine-University Duesseldorf - Institute for Health Services Research and Health Economics, Moorenstr. 5, 40225 Duesseldorf, Germany) |
| Abstract: | Background. In hospitals, decisions are often made under time pressure. There is, however, little evidence on how time pressure affects the quality of treatment and the documentation behavior of physicians. Setting. We implemented a controlled laboratory experiment with a healthcare framing in which international medical students in the Czech Republic treated patients in the role of hospital physicians. We varied the presence of time pressure and a documentation task. Results. We observed worse treatment quality when individuals were faced with a combination of a documentation task and time pressure. In line with the concept of the speed-accuracy trade-off, we showed that quality changes are likely driven by less accuracy. Finally, we showed that while documentation quality was relatively high overall, time pressure significantly lowered the latter leading to a higher hypothetical profit loss for the hospital. Conclusions. Our results suggest that policy reforms aimed at increasing staffing and promoting novel technologies that facilitate physicians' treatment decisions and support their documentation work in the hospital sector might be promising means of improving the treatment quality and reducing inefficiencies potentially caused by documentation errors. |
| Keywords: | physician incentives; work motivation; time pressure; laboratory experiment |
| JEL: | C91 I11 M50 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:mub:wpaper:2025-01 |
| By: | Feng, Mengli |
| Abstract: | Predictive coding (PC) is a theory in cognitive/computational neuroscience which explains cortical functions with a hierarchical process of minimising prediction errors. It provides a neuronal scheme for implementing Bayesian inference in the brain to recover the hidden state of the world from sensory input (passive inference) and to select actions to reach the goals the agent has (active inference). Since its discovery, predictive coding has been found to be a unifying theory explaining more and more cognitive functions, including perception, attention, and action planning. In this literature thesis, I review and discuss how PC can be used also as a powerful tool to understand working memory (WM), an essential function for executive control. % Giving a brief introduction to working memory and current PC frameworks, I start with an overview of how WM might fit within predictive coding frameworks. Specifically, I try to explore how PC frameworks help with explaining the following questions: 1. how is WM maintained and updated? 2. What is the relationship between attention and WM and how do they interact? 3. why does WM have limited capacity? and 4. why is WM hierarchical? By treating WM coding as part of the state inference process, we can explain WM maintenance as the stage where the state variables remain the same when there is no new evidence. WM updates, on the other hand, correspond to belief updating when new evidence arises. Since there is a trade-off between prediction complexity and accuracy during state inference, the limited capacity of WM may be an emergent property to ensure a certain level of accuracy. In a process of active inference, attention helps the agent to select actions that reduce uncertainties about the world where selected actions give rise to observations that are used to update WM. This delineates the roles of WM and attention and clarifies the mechanism of their interactions. Finally, hierarchical PC can account for the hierarchical representation of working memory in the brain where each level of WM corresponds to each level of inferred states. Based on the reviewed literature, I summarised three important ingredients for modelling WM which are temporal depth, goals and hierarchy. Future work on modelling would be to clarify whether WM is a separable component in PC, which variable WM is actually represented in PC and where in the hierarchy WM is generated and maintained. In summary, through the lens of variational Bayesian inference, WM can be assessed in the process of evidence accumulation simulated in a deep hierarchical predictive coding model. With action selection incorporated, this naturally explains WM as an emergent property of goal-directed behaviour, manifested by hierarchical inference of the brain through the minimization of expected free energy. Modelling WM in PC frameworks provides alternative explanations to some long-standing questions about WM and may help with resolving the conflicts between WM theories, for example, the ones that propose either persistent or sparse neuronal activity during WM. It may also help with developing computational tools to improve treatments for brain disorders such as schizophrenia and facilitate artificial intelligence in coping with a world full of uncertainties. |
| Date: | 2023–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:osf:thesis:sqfr9_v1 |