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on Neuroeconomics |
| By: | Jonathan Benchimol (Bank of Israel); Lahcen Bounader (World Bank); Mario Dotta (Getulio Vargas Foundation) |
| Abstract: | Bounded rationality and limited attention significantly influence expectation formation and macroeconomic dynamics, yet empirical quantification of these behavioral phenomena remains challenging. This paper provides the first cross-country estimation of both micro- and macro-level attention parameters using a structurally identified behavioral New Keynesian model. Employing Bayesian techniques on harmonized data from 22 OECD countries (1996–2019) and ensuring robust parameter identification, we document substantial heterogeneity in behavioral inattention across countries. Our cognitive discounting estimates range from 0.76 to 0.98, with higher values indicating greater attention. We establish three key empirical regularities: (1) attention parameters are positively associated with macroeconomic volatility, supporting rational inattention theory; (2) surprise movements in key macroeconomic variables and online information-seeking behavior significantly influence attention allocation; and (3) institutional quality, particularly government effectiveness, is correlated with attention levels. These findings reveal that attention is both a behavioral and a structural phenomenon, responding to institutional factors and economic conditions. Our results provide an empirical foundation for calibrating country-specific models and yield important implications for the design and transmission of monetary policy under bounded rationality, showing that policy effectiveness may systematically vary with the macroeconomic environment. |
| Keywords: | Cognitive discounting, Myopia, Attention, Bayesian estimation, Behavioral macroeconomics |
| JEL: | E37 E52 E58 E70 E71 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:boi:wpaper:2025.09 |
| By: | Edmundo Molina-Perez (School of Government and Public Transformation, Tecnológico de Monterrey); Pedro Cortes (Tecnológico de Monterrey); Isaac Molina (Tecnológico de Monterrey); Fernanda Sobrino (Tecnológico de Monterrey); Mario Tellez (Tecnológico de Monterrey); Yessica Orozco (Tecnológico de Monterrey); Mitzi Castellón (Tecnológico de Monterrey); Steven Popper (Tecnológico de Monterrey); Luis Serra (Tecnológico de Monterrey) |
| Abstract: | Decision-making is a multifaceted cognitive process influenced by task complexity, information availability, individual cognitive strategies, and environmental settings. Yet, the neural mechanisms guiding everyday choices remain incompletely understood. This gap intensifies when integrating real-time aids, such as artificial intelligence tools (AIT), as cognitive decisionsupport especially for complex and ambiguous problems. This study explores the neural mechanisms of decision-making and examines how AIT influences these processes. Combining behavioral assessments and neurophysiological measurements, we investigate the dynamic interplay between human cognition and AIT through behavioral execution and electroencephalogram (EEG) activity. Experimental data from 54 participants suggest that in low-complexity decision-making, AIT is largely ignored in favor of heuristics. In high-complexity contexts, AIT positively influences decision-making outcomes while also increasing capacity for engagement with a challenging task as registered by EEG cortical activity. This suggests a non-linear effect of AIT in decision-making strategies highlighting its role as a complement to —rather than a replacement of—human cognitive processes. |
| Keywords: | artificial intelligence, decision-making, EEG, neuroeconomics, cognitive support tools |
| JEL: | C91 D83 D89 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:gnt:wpaper:8 |
| By: | Ajay K. Agrawal; Joshua S. Gans; Avi Goldfarb |
| Abstract: | Steve Jobs described computers as “bicycles for the mind, ” a tool that allowed people to dramatically leverage their capabilities. This paper presents a formal model of cognitive tools and technologies that enhance mental capabilities. We consider agents engaged in iterative task improvement, where cognitive tools are assumed to be substitutes for implementation skills and may or may not be complements to judgment, depending on their type. The ability to recognise opportunities to start or improve a process, which we term opportunity judgment, is shown to always complement cognitive tools. The ability to know which action to take in a given state, which we term payoff judgment, is not necessarily a complement to cognitive tools. Using these concepts, we can synthesise the empirical literature on the impact of computers and artificial intelligence (AI) on productivity and inequality. Specifically, while both computers and AI appear to increase productivity, computers have also contributed to increased inequality. Empirical work on the impact of AI on inequality has shown both increases and decreases, depending on the context. We also apply the model to understand how cognitive tools might influence incentives to automate processes and allocate decision-making authority within teams. |
| JEL: | D83 J24 L23 O33 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34034 |
| By: | Julie Achmirowicz (Ministère des armées, ENST - Ecole Nationale Supérieure des Télécommunications); Jean Langlois-Berthelot (Ministère des armées) |
| Abstract: | Développer une innovation p é d a g o g i q u e s u r l e s systèmes cognitifs et l'IA au sein du CEMS-T |
| Keywords: | Sciences cognitives, IA, Armée |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05062904 |