1
1.0
Feb 11, 2022
02/22
by
Alex Jamieson; Maarten Speekenbrink
data
eye 1
favorite 0
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The default stance of much of the regret literature is that, overall, regret is a functional emotion- it leads decision makers to adaptively switch to a behaviour more likely to lead to better outcomes, compared to the previously adopted behaviour (Epstude & Roese, 2008; O’Connor et al., 2014; Zeelenberg, 1999). However, regret may not always be functional. Random variation in the environment means uncertainty is inherent to decisional outcomes. It follows that the optimal decision at the...
Source: https://osf.io/nstr3/
3
3.0
Jun 29, 2018
06/18
by
Eric Schulz; Maarten Speekenbrink; Björn Meder
texts
eye 3
favorite 0
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How can heuristic strategies emerge from smaller building blocks? We propose Approximate Bayesian Computation as a computational solution to this problem. As a first proof of concept, we demonstrate how a heuristic decision strategy such as Take The Best (TTB) can be learned from smaller, probabilistically updated building blocks. Based on a self-reinforcing sampling scheme, different building blocks are combined and, over time, tree-like non-compensatory heuristics emerge. This new algorithm,...
Topics: Applications, Statistics
Source: http://arxiv.org/abs/1605.01598
1
1.0
Sep 7, 2021
09/21
by
Hrvoje Stojic; Eric Schulz; Pantelis P. Analytis; Maarten Speekenbrink
data
eye 1
favorite 0
comment 0
This project repository contains all the files related to preregistration of "It’s new, but is it good? How generalization and uncertainty guide the exploration of novel options" by Hrvoje Stojić, Eric Schulz, Pantelis P. Analytis and Maarten Speekenbrink.
Source: https://osf.io/tg5kc/
1
1.0
Sep 9, 2021
09/21
by
Hrvoje Stojic; Eric Schulz; Pantelis P. Analytis; Maarten Speekenbrink
data
eye 1
favorite 0
comment 0
This project repository contains all the files related to preregistration of "It’s new, but is it good? How generalization and uncertainty guide the exploration of novel options" by Hrvoje Stojić, Eric Schulz, Pantelis P. Analytis and Maarten Speekenbrink.
Source: https://osf.io/upj76/
1
1.0
Aug 27, 2021
08/21
by
Hrvoje Stojic; Eric Schulz; Pantelis P. Analytis; Maarten Speekenbrink
data
eye 1
favorite 0
comment 0
This project repository contains all the files related to preregistration of "It’s new, but is it good? How generalization and uncertainty guide the exploration of novel options" by Hrvoje Stojić, Eric Schulz, Pantelis P. Analytis and Maarten Speekenbrink.
Source: https://osf.io/37ayn/
1
1.0
Aug 21, 2021
08/21
by
Hrvoje Stojic; Eric Schulz; Pantelis P. Analytis; Maarten Speekenbrink
data
eye 1
favorite 0
comment 0
This project repository contains all the files related to preregistration of "It’s new, but is it good? How generalization and uncertainty guide the exploration of novel options" by Hrvoje Stojić, Eric Schulz, Pantelis P. Analytis and Maarten Speekenbrink.
Source: https://osf.io/h5uqr/
1
1.0
Aug 28, 2021
08/21
by
Adam Harris; Maarten Speekenbrink; Fi N Blower; Sophie A Rodgers
data
eye 1
favorite 0
comment 0
Source: https://osf.io/9b5ws/
1
1.0
Sep 12, 2021
09/21
by
Adam Harris; Maarten Speekenbrink; Fi N Blower; Sophie A Rodgers
data
eye 1
favorite 0
comment 0
Source: https://osf.io/ahfp6/
1
1.0
Sep 7, 2021
09/21
by
Adam Harris; Maarten Speekenbrink; Fi N Blower; Sophie A Rodgers
data
eye 1
favorite 0
comment 0
Source: https://osf.io/mfau4/
1
1.0
Sep 11, 2021
09/21
by
Adam Harris; Maarten Speekenbrink; Fi N Blower; Sophie A Rodgers
data
eye 1
favorite 0
comment 0
Source: https://osf.io/jrvqd/
1
1.0
Sep 2, 2021
09/21
by
Adam Harris; Maarten Speekenbrink; Fi N Blower; Sophie A Rodgers
data
eye 1
favorite 0
comment 0
Source: https://osf.io/v24a3/
1
1.0
Sep 10, 2021
09/21
by
Adam Harris; Maarten Speekenbrink; Fi N Blower; Sophie A Rodgers
data
eye 1
favorite 0
comment 0
Source: https://osf.io/upgy9/
7
7.0
Jun 29, 2018
06/18
by
Eric Schulz; Quentin J. M. Huys; Dominik R. Bach; Maarten Speekenbrink; Andreas Krause
texts
eye 7
favorite 0
comment 0
Exploration-exploitation of functions, that is learning and optimizing a mapping between inputs and expected outputs, is ubiquitous to many real world situations. These situations sometimes require us to avoid certain outcomes at all cost, for example because they are poisonous, harmful, or otherwise dangerous. We test participants' behavior in scenarios in which they have to find the optimum of a function while at the same time avoid outputs below a certain threshold. In two experiments, we...
Topics: Learning, Machine Learning, Applications, Computing Research Repository, Statistics
Source: http://arxiv.org/abs/1602.01052
