Split-second Decision-Making in the Field: Response Times in Mobile Advertising

Chiong, Khai Xiang; Shum, Matthew; Webb, Ryan; Chen, Richard

doi:10.2139/ssrn.3289386

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…Hawkins, Forstmann, Wagenmakers, Ratcliff, and Brown (2015) estimate collapsing boundaries in a parametric class, allowing for a random nondecision time at the start. Chiong, Shum, Webb, and Chen (2018) estimate a version of DDM with constant boundaries but random starting point of the signal accumulation process; Ratcliff (2002) estimates a similar model where other parameters are made random. Baldassi, Cerreia-Vioglio, Maccheroni, and Marinacci (2018) partially characterize DDM with constant boundary.…”

Section: Introductionmentioning

confidence: 99%

Testing the Drift-Diffusion Model

Fudenberg¹,

Newey²,

Strack³

et al. 2019

Preprint

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Alós-Ferrer, Fehr, and Netzer (2018) and Echenique and Saito (2017) study models where response time is a deterministic function of the utility difference. Che and Mierendorff (2016); Hebert and Woodford (2016); Liang and Mu (2019); Liang, Mu, and Syrgkanis (2019); Woodford (2014); Zhong (2019) study dynamic costly optimal information acquisition. The Stochastic Choice FunctionLet X be the universe of alternatives (actions) and T = R + be time. For every pair of objects {x, y} the analyst observes pairwise stochastic choices and decision times. In the limit as the sample size grows large, the analyst will have access to the joint distribution over which object is chosen and at which time a choice is made. We denote by F xy (t) the probability that the agent makes a choice by time t, and let p xy (t) the probability that the agent picks x conditional on stopping at time t. Throughout, we restrict attention to cases where F has full support and no atoms at time 0, so that F (0) = 0, and we assume that F is strictly increasing with lim t→∞ F (t) = 1. These restrictions imply the agent never stops immediately, that there is a positive probability of stopping in every time interval, and that the agent always eventually stops. We call (p xy , F xy ), the stochastic choice function.An immediate restriction on the stochastic choice function is that the choices of the agent are unaffected by which object we consider to be the first and which object we consider to be the second. This is formally equivalent to p xy (t) ≡ 1 − p yx (t) for all t and F xy ≡ F yx for all x, y ∈ X.Without loss of generality we only consider stochastic choice functions which satisfy this restriction. We also assume that each option is chosen with positive probability 0 < p xy (t) < 1 for all t.

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Section: Introductionmentioning

confidence: 99%

Testing the Drift-Diffusion Model

Fudenberg¹,

Newey²,

Strack³

et al. 2019

Preprint

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show abstract

“…For an example of how components of value can be estimated from observable attributes in the DDM framework, seeChiong et al (2018).3 In many applications, an "initial point" Z i,0 can be specified to allow for any priors that decision makers might have.…”

mentioning

confidence: 99%

Estimating the dynamic role of attention via random utility

Smith

Krajbich

Webb

2019

J. Econ. Sci. Assoc.

Self Cite

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When making decisions, people tend to look back and forth between the alternatives until they eventually make a choice. Eye-tracking research has established that these shifts in attention are strongly linked to choice outcomes. A predominant framework for understanding the dynamics of the choice process, and thus the effects of attention, is sequential sampling of information. However, existing methods for estimating the attention parameters in these models are computationally costly and overly flexible, and yield estimates with unknown precision and bias. Here we propose an estimation method that relies on a link between sequential sampling models and random utility models (RUM). This method uses familiar econometric tools (i.e., logistic regression) and yields estimates that appear to be unbiased and relatively precise compared to existing methods, in a small fraction of the computation time.The RUM thus appears to be a useful tool for estimating the effects of attention on choice.

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Testing the drift-diffusion model

Fudenberg

Newey

Strack

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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The drift-diffusion model (DDM) is a model of sequential sampling with diffusion signals, where the decision maker accumulates evidence until the process hits either an upper or lower stopping boundary and then stops and chooses the alternative that corresponds to that boundary. In perceptual tasks, the drift of the process is related to which choice is objectively correct, whereas in consumption tasks, the drift is related to the relative appeal of the alternatives. The simplest version of the DDM assumes that the stopping boundaries are constant over time. More recently, a number of papers have used nonconstant boundaries to better fit the data. This paper provides a statistical test for DDMs with general, nonconstant boundaries. As a by-product, we show that the drift and the boundary are uniquely identified. We use our condition to nonparametrically estimate the drift and the boundary and construct a test statistic based on finite samples.

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Split-second Decision-Making in the Field: Response Times in Mobile Advertising

Cited by 4 publications

References 40 publications

Testing the Drift-Diffusion Model

Testing the Drift-Diffusion Model

Estimating the dynamic role of attention via random utility

Testing the drift-diffusion model

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