A statistical test for the optimality of deliberative time allocation

Bhui, Rahul

doi:10.3758/s13423-018-1555-1

Cited by 15 publications

(9 citation statements)

References 91 publications

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“…Our results additionally complement a large set of work which has studied how humans and animals decide how long to deliberate prior to a choice (Ratcliff, 1978). This work has largely studied how long to deliberate in sensory decision-making (Drugowitsch et al, 2012;Gold & Shadlen, 2002) and value-based decision making (Bhui, 2019b;Lee & Daunizeau, 2021;Tajima et al, 2016) problems and has found that humans and animals can intelligently balance decision accuracy with the cost of spending time in these domains (although see (Bhui, 2019a;Oud et al, 2016)).…”

Section: Discussionsupporting

confidence: 53%

Time spent thinking in online chess reflects the value of computation

Russek¹,

Acosta-Kane²,

Opheusden³

et al. 2022

Preprint

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Although artificial intelligence systems can now outperform humans in a variety of domains, they still lag behind in the ability to arrive at good solutions to problems using limited resources. Recent proposals have suggested that the key to this cognitive efficiency is intelligent selection of the situations in which computational resources are spent. We tested this hypothesis in the domain of complex planning by analysing how humans managed time available for thinking in over 12 million online chess matches. We found that players spent more time thinking in board positions where planning was more beneficial. This effect was greater in stronger players, and additionally strengthened by considering only the information available to the player at the time of choice. Finally, we found the qualitative shape of this relationship could be captured by rationally trading off the benefits of improving board position with the costs of spending time. This provides evidence that human efficiency is supported by intelligent selection of when to apply computation.

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Section: Discussionsupporting

confidence: 53%

Time spent thinking in online chess reflects the value of computation

Russek¹,

Acosta-Kane²,

Opheusden³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We are not the …rst authors to study choice in a setting where material outcomes depend on imperfectly perceived objects with objectively measurable properties. For instance, researchers have made payments to subjects as a function of judgments involving the relative quantity of dots (Caplin and Dean, 2015;Dutilh and Rieskamp, 2016), the dominant direction of moving dots (Bhui, 2019a;2019b), the number of ‡ickering dots (Oud et al, 2016), a dynamic display of dots (Zeigenfuse, Pleskac, and Liu, 2014), the heights of bars of dynamic size (Tsetsos et al, 2016), and the area occupied by objects of various sizes (Polanía, Krajbich, Grueschow, and Ru¤, 2014). 5 To our knowledge, Du¤y, Gussman, and Smith (2019) is the only other paper that describes a choice experiment where suboptimal choices are perfectly observable because utility Saito, and Tserenjigmid (2018), Koida (2018), Kovach and Tserenjigmid (2018), Caplin, Dean, and Leahy (2019), Cattaneo, Ma, Masatlioglu, and Suleymanov (2019), Conte and Hey (2019), and Natenzon (2019).…”

Section: Choice Involving Imperfectly Perceived Objectsmentioning

confidence: 99%

An Economist and a Psychologist Form a Line: What Can Imperfect Perception of Length Tell Us About Stochastic Choice?

Duffy¹,

Smith

2020

SSRN Journal

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Standard choice experiments are hampered by the fact that utility is either unknown or imperfectly measured by experimenters. As a consequence, the inferences available to researchers are limited. By contrast, we design a choice experiment where the objects are valued according to only a single attribute with a continuous measure and we can observe the true preferences of subjects. Subjects have an imperfect perception of the choice objects but can improve the precision of their perception with cognitive e¤ort. Subjects are given a choice set involving several lines of various lengths and are told to select one of them. They strive to select the longest line because they are paid an amount that increases with the length of their choice. Our design allows us to observe the search history, the response times, and make unambiguous conclusions about the optimality of choices. We …nd a negative relationship between the demanding nature of the choice problems and the likelihood that subjects select the optimal lines. We also …nd a positive relationship between the demanding nature of the choice problems and the response times. However, we …nd evidence that suboptimal choices are associated with longer response times than are optimal choices. This result appears to be consistent with Fudenberg, Strack, and Strzalecki (2018). Additionally, our experimental design permits a multinomial discrete choice analysis. Our results suggest that the errors in our data are better described as having a Gumbel distribution rather than a normal distribution. We also observe e¤ects consistent with memory decay and attention. Finally, we …nd evidence that choices in our experiment exhibit the independence from irrelevant alternatives (IIA) property.

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“…Notwithstanding these limitations, recent research has shown that web-based experiments yield reliable results, comparable to those obtained with lab-based experiments for reaction time tasks ([ 34 ] and references therein). Furthermore, our within-subjects study and analyses reduce the risk of conclusions driven by between-participant variability [ 35 ].…”

Section: Methodsmentioning

confidence: 58%

Magnitude-sensitive reaction times reveal non-linear time costs in multi-alternative decision-making

et al. 2022

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Optimality analysis of value-based decisions in binary and multi-alternative choice settings predicts that reaction times should be sensitive only to differences in stimulus magnitudes, but not to overall absolute stimulus magnitude. Yet experimental work in the binary case has shown magnitude sensitive reaction times, and theory shows that this can be explained by switching from linear to multiplicative time costs, but also by nonlinear subjective utility. Thus disentangling explanations for observed magnitude sensitive reaction times is difficult. Here for the first time we extend the theoretical analysis of geometric time-discounting to ternary choices, and present novel experimental evidence for magnitude-sensitivity in such decisions, in both humans and slime moulds. We consider the optimal policies for all possible combinations of linear and geometric time costs, and linear and nonlinear utility; interestingly, geometric discounting emerges as the predominant explanation for magnitude sensitivity.

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A statistical test for the optimality of deliberative time allocation

Cited by 15 publications

References 91 publications

Time spent thinking in online chess reflects the value of computation

Time spent thinking in online chess reflects the value of computation

An Economist and a Psychologist Form a Line: What Can Imperfect Perception of Length Tell Us About Stochastic Choice?

Magnitude-sensitive reaction times reveal non-linear time costs in multi-alternative decision-making

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