Summary
This proposal outlines an agenda that aims to improve our understanding of economies with inattentive agents. Attention to detail, not only to current news, but also to how the world works in general, is central to how we interact with the environment.
In the first part of the agenda, we will study how agents come up with the simplified mental models they use in their decision-making. The aim is to provide a new alternative to rational expectations. We will address the question of endogenous model uncertainty by sidestepping the largely statistical nature of previous work. Our agents learn about a model directly, i.e., all information on the details of the correct model is readily available. The envisioned implications can speak to issues such as the expectations formation and formation of narratives, polarization of opinions, and demand for public policy.
In the second part, we will study how a government optimally intervenes in markets if it finds it costly to get the necessary information. On one hand, a government does not possess the local information of decentralized markets. On the other, markets on their own often generate suboptimal social outcomes. We will explore what information the government should collect, how to use it for regulation, and when instead it should leave markets unaffected.
In the third part, we will leverage recent theories of attention allocation and use uniquely detailed data on attention and treatment choices by hospital personnel (including physicians and nurses). This will allow us to explore in more detail than before what theories describe realistic choices well. Moreover, we will eventually aim at a very practical goal: how to help clinicians decrease their cognitive load and improve medical choices.
In the first part of the agenda, we will study how agents come up with the simplified mental models they use in their decision-making. The aim is to provide a new alternative to rational expectations. We will address the question of endogenous model uncertainty by sidestepping the largely statistical nature of previous work. Our agents learn about a model directly, i.e., all information on the details of the correct model is readily available. The envisioned implications can speak to issues such as the expectations formation and formation of narratives, polarization of opinions, and demand for public policy.
In the second part, we will study how a government optimally intervenes in markets if it finds it costly to get the necessary information. On one hand, a government does not possess the local information of decentralized markets. On the other, markets on their own often generate suboptimal social outcomes. We will explore what information the government should collect, how to use it for regulation, and when instead it should leave markets unaffected.
In the third part, we will leverage recent theories of attention allocation and use uniquely detailed data on attention and treatment choices by hospital personnel (including physicians and nurses). This will allow us to explore in more detail than before what theories describe realistic choices well. Moreover, we will eventually aim at a very practical goal: how to help clinicians decrease their cognitive load and improve medical choices.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101002898 |
| Start date: | 01-04-2021 |
| End date: | 31-03-2026 |
| Total budget - Public funding: | 1 162 663,75 Euro - 1 162 663,00 Euro |
Cordis data
Original description
This proposal outlines an agenda that aims to improve our understanding of economies with inattentive agents. Attention to detail, not only to current news, but also to how the world works in general, is central to how we interact with the environment.In the first part of the agenda, we will study how agents come up with the simplified mental models they use in their decision-making. The aim is to provide a new alternative to rational expectations. We will address the question of endogenous model uncertainty by sidestepping the largely statistical nature of previous work. Our agents learn about a model directly, i.e., all information on the details of the correct model is readily available. The envisioned implications can speak to issues such as the expectations formation and formation of narratives, polarization of opinions, and demand for public policy.
In the second part, we will study how a government optimally intervenes in markets if it finds it costly to get the necessary information. On one hand, a government does not possess the local information of decentralized markets. On the other, markets on their own often generate suboptimal social outcomes. We will explore what information the government should collect, how to use it for regulation, and when instead it should leave markets unaffected.
In the third part, we will leverage recent theories of attention allocation and use uniquely detailed data on attention and treatment choices by hospital personnel (including physicians and nurses). This will allow us to explore in more detail than before what theories describe realistic choices well. Moreover, we will eventually aim at a very practical goal: how to help clinicians decrease their cognitive load and improve medical choices.
Status
SIGNEDCall topic
ERC-2020-COGUpdate Date
27-04-2024
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