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Learning heuristics in preference elicitation tasks: insights from behavioural, computational and neurobiological investigations

Objective

Although decision theory assumes that when making a choice, individuals attribute values to available options, compare those values and select the option with the highest, the succession of choices faced during classical preference elicitation tasks might trigger the emergence of additional heuristics, implemented to perform those tasks in a fast, yet adaptive manner. This project aim at pioneering the isolation of such heuristic development, in a dynamical framework where both the task features and the agents’ own preferences are learned from previous trials, and influence subsequent behavior. This framework suggests that agents’ preferences will depend on the choice sequence, thus vary according to predictable patterns through different instantiations of the same task. Thereby, it captures a new component of individual preferences, which we refer to as task-related preferences. Combining behavioral experiments, computational modelling and functional brain imaging, we propose to reveal and measure the behavioral variance accounted by the task-related preferences, to model their emergence during the task, and to incorporate them in a coherent neuro-cognitive model of decision-making. Overall, this project will contribute to 1) refine current neurocognitive and economic models of decision-making, 2) train a promising cognitive neuroscientist to tackle human decision issues relevant to social sciences, with advanced quantitative economic/computational tools, and 3) foster fruitful cross-talks between scholars from economics, psychology, and neuroscience at the host institution. The scientific contribution seems particularly important given that preferences are one of the current conceptual cornerstones used to understand our society at the micro- and macroeconomic level, to guide and assess public policies aiming to maximize people’s well-being, to characterize normal and pathological behaviors, and to unravel the neurobiological mechanisms underlying decision-making.

Fields of science (EuroSciVoc)

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

Multi-annual funding programmes that define the EU’s priorities for research and innovation.

Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) H2020-MSCA-IF-2014

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Coordinator

UNIVERSITEIT VAN AMSTERDAM
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 165 598,80
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 165 598,80
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