An accumulation of preference: Two alternative dynamic models for understanding transport choices

Hancock, Thomas O.; Hess, Stephane; Marley, A. A. J.; Choudhury, Charisma F.

doi:10.1016/j.trb.2021.04.001

Cited by 19 publications

(11 citation statements)

References 78 publications

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“…We used a DFT model with an external threshold and scaling parameters, as set out by Hancock et al. (2021). In an external threshold model, the alternative with the highest preference is chosen after an estimated total number of preference‐updating steps 8 .…”

Section: Methodsmentioning

confidence: 99%

“…The scale‐invariance of this DFT model ensures that the comparison of parameter ratios to other models is valid; ratios of the scaling parameters represent the relative importance (RI) of attributes. For individual

i

in choice scenario

t

(omitting

i,t

subscripts for readability), this is specified as (Hancock et al., 2021):

{P}_{\tau }=S\,\cdot \,{P}_{\tau -1}+{V}_{\tau }

{P}_{0}={\left[{\alpha }_{1},\text{\ldots },{\alpha }_{j},\text{\ldots },{\alpha }_{J}\right]}^{\prime }

S={I}_{J}-{\phi }_{2}\,\cdot \,\mathit{exp}\left(-{\phi }_{1}\,\cdot \,{D}^{2}\right)

{{D}_{jm}}^{2}=\sum\limits _{k=1}^{K}{({\beta }_{k}\cdot ({x}_{ijtk}-{x}_{imtk}))}^{2}:\,\mathrm{entries}\hspace*{.5em}\mathrm{of}\hspace*{.5em}D

...

…”

Section: Methodsmentioning

confidence: 99%

“…Previous applications of DFT estimated weights

{w}_{k}

for each attribute. Here, these are fixed to

{w}_{k}=1/K\,\forall \,k

, which may be a reasonable assumption in experimental settings, such as DCEs (Hancock et al., 2021). Instead, each attribute value in matrix

M

is multiplied by scaling factors in matrix

B

, a diagonal matrix with scaling coefficients

\beta

on the diagonal (which results in DFT being scale‐invariant, see Hancock et al.…”

Section: Methodsmentioning

confidence: 99%

“…In a comparison of DFT to RUM and RRM for a DCE of transport mode choice, DFT improved model fit and out‐of‐sample forecasts (Hancock et al., 2018). Further, a scale‐invariant DFT allows for better incorporation of deterministic interactions, and outperformed RUM models (Hancock et al., 2021).…”

Section: Decision Field Theory For Health Economicsmentioning

confidence: 99%

“…Evidence from behavioral economics and eye‐tracking data in health‐based DCEs shows the presence of behavioral anomalies and context effects (Ryan et al., 2018), which DFT may better explain (Roe et al., 2001). In transport economics, comparisons showed DFT outperformed RUM and RRM in traditional DCEs (Hancock et al., 2018; Hancock et al., 2021). Thus, in some cases, DFT may better explain health choice behaviors than existing paradigms.…”

Section: Introductionmentioning

confidence: 99%

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Can decision field theory enhance our understanding of health‐based choices? Evidence from risky health behaviors

et al. 2023

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Discrete choice models are almost exclusively estimated assuming random utility maximization (RUM) is the decision rule applied by individuals. Recent studies indicate alternative behavioral assumptions may be more appropriate in health. Decision field theory (DFT) is a psychological theory of decision‐making, which has shown promise in transport research. This study introduces DFT to health economics, empirically comparing it to RUM and random regret minimization (RRM) in risky health settings, namely tobacco and vaccine choices. Model fit, parameter ratios, choice shares, and elasticities are compared between RUM, RRM and DFT. Test statistics for model differences are derived using bootstrap methods. Decision rule heterogeneity is investigated using latent class models, including novel latent class DFT models. Tobacco and vaccine choice data are better explained with DFT than with RUM or RRM. Parameter ratios, choice shares and elasticities differ significantly between models. Mixed results are found for the presence of decision rule heterogeneity. We conclude that DFT shows promise as a behavioral assumption that underpins the estimation of discrete choice models in health economics. The significant differences demonstrate that care should be taken when choosing a decision rule, but further evidence is needed for generalizability beyond risky health choices.

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Section: Methodsmentioning

confidence: 99%