How to ask twenty questions and win: Machine learning tools for assessing preferences from small samples of willingness-to-pay prices

Sokratous, Konstantina; Fitch, Anderson; Kvam, Peter D.

doi:10.1016/j.jocm.2023.100418

Cited by 7 publications

(9 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…particle filtering (Djuric et al, 2003); density estimation (Minka, 2013)); inverse binomial sampling (van Opheusden et al, 2020) may prove to be more robust, but frequently require more advanced mathematical knowledge and model case-based adaptations, or are more computationally expensive; indeed, some of them may not be usable or tractable in our type of data and models where there are sequential dependencies between trials Acerbi and Ma, 2017; van Opheusden et al, 2020. ANN-based methods such as ours or others’ Radev, Mertens, et al, 2020; Radev, Voss, et al, 2020; Sokratous et al, 2023, on the other hand, offers a more straightforward and time-efficient path to both parameter estimation and model identification. Developing more accessible and robust methods is critical for advances in computational modeling and cognitive science, and the rising popularity of deep learning puts neural networks forward as useful tools for this purpose.…”

Section: Discussionmentioning

confidence: 99%

“…Chen et al, 2020; Fearnhead and Prangle, 2012; Jiang et al, 2017; Lavin et al, 2021; Radev, Mertens, et al, 2020; Radev, Voss, et al, 2020). This innovative approach serves to amortize the computational cost of simulation-based inference, opening new frontiers in terms of scalability and performance (Boelts et al, 2022; Fengler et al, 2021; Ghaderi-Kangavari et al, 2023; Radev, Mertens, et al, 2020; Radev, Voss, et al, 2020; Radev et al, 2021; Schmitt et al, 2021; Sokratous et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial neural networks for model identification and parameter estimation in computational cognitive models

Rmus,

Pan,

Xia

et al. 2023

Preprint

View full text Add to dashboard Cite

Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the lack of tools required to relate them to data. We propose to fill this gap using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. Our results show that we can adequately perform both parameter estimation and model identification using our new ANN approach, including for models that cannot be fit using traditional likelihood-based methods. Our new ANN approach will greatly broaden the class of cognitive models researchers can quantitatively consider.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial neural networks for model identification and parameter estimation in computational cognitive models

Rmus,

Pan,

Xia

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…To overcome these challenges, we employ a novel method that uses deep neural networks to automate the process of parameter estimation. This method was presented in detail by Sokratous et al (2023) and based on a similar approach as the work of Radev, Mertens, Voss, & Köthe (2020); Radev, Mertens, Voss, Ardizzone, & Köthe (2020). An outline of this approach is shown in Figure 3.…”

Section: Estimation Approachmentioning

confidence: 99%

“…In the appendix, we detail the structure of the deep learning algorithm for model fitting (Algorithmic Model). For alternative training sets and architectures on the pricing model, we invite readers to consult the original paper by Sokratous et al (2023).…”

Section: Estimation Approachmentioning

confidence: 99%

Time flips and price dips: Modeling preference reversals between intertemporal choice and pricing

Sokratous,

Kvam

2024

Preprint

View full text Add to dashboard Cite

In this paper, we first evaluated several predictions made by apricing accumulation model in the context of intertemporal value, such as positive skew in the distribution of price responses for payoffs with long delays, more negative skew for shorter delays, and longer response times for higher payoffs. Next, we applied this model to better understand why people favor smaller-sooner options in choice but assign higher bids to larger-later options on a pricing task. We show that intertemporal choice-price preference reversals can be explained by an anchoring process that is present in pricing but not in choice. This approach is then extended to the domain of delayed losses, where we show a clear pattern of loss aversion relative to the gain domain. Because there are no probabilities, this effect cannot be explained by risk aversion; instead, loss aversion manifests in shallower discounting rates in choice and stronger anchoring effects in pricing for delayed monetary losses relative to monetary gains.

show abstract

“…These methods enable automated (or semi-automated) construction of summary statistics, minimizing the effect the choice of summary statistics may have on the accuracy of parameter estimation [38,[40][41][42][43][44]. This innovative approach serves to amortize the computational cost of simulation-based inference, opening new frontiers in terms of scalability and performance [40,41,[45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks for model identification and parameter estimation in computational cognitive models

Rmus,

Pan,

Xia

et al. 2024

PLoS Comput Biol

View full text Add to dashboard Cite

Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate.

show abstract

How to ask twenty questions and win: Machine learning tools for assessing preferences from small samples of willingness-to-pay prices

Cited by 7 publications

References 67 publications

Artificial neural networks for model identification and parameter estimation in computational cognitive models

Artificial neural networks for model identification and parameter estimation in computational cognitive models

Time flips and price dips: Modeling preference reversals between intertemporal choice and pricing

Artificial neural networks for model identification and parameter estimation in computational cognitive models

Contact Info

Product

Resources

About