Choice Overload and Height Ranking of Menus in Partially-Ordered Sets

Basili, Marcello; Vannucci, Stefano

doi:10.3390/e17117584

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…[35]. Indeed, it is advisable to avoid assigning fewer subjective evaluation tasks to mitigate cognitive errors [4,64] As suggested by Axiom 1, the τx similarity between two incomplete rankings can be equivalently calculated by simply dropping the alternatives unranked by either ranking (i.e., by projecting them to the subset of objects evaluated by both). While this may seem to remove the incomplete data from the researcher's view when comparing two incomplete rankings, we emphasize that the consensus ranking problem (see (1) or ( 2)) involves accruing the comparisons between the candidate solution (always a complete ranking) and each input ranking (which may be incomplete or complete).…”

Section: Derivation Of New Correlation Coefficient and Its Axiomatic ...mentioning

confidence: 99%

A New Correlation Coefficient for Aggregating Non-strict and Incomplete Rankings

Yoo,

Escobedo,

Skolfield

2018

Preprint

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We introduce a correlation coefficient that is designed to deal with a variety of ranking formats including those containing non-strict (i.e., withties) and incomplete (i.e., unknown) preferences. The correlation coefficient is designed to enforce a neutral treatment of incompleteness whereby no assumptions are made about individual preferences involving unranked objects. The new measure, which can be regarded as a generalization of the seminal Kendall tau correlation coefficient, is proven to satisfy a set of metric-like axioms and to be equivalent to a recently developed ranking distance function associated with Kemeny aggregation. In an effort to further unify and enhance both robust ranking methodologies, this work proves the equivalence of an additional distance and correlation-coefficient pairing in the space of non-strict incomplete rankings. These connections induce new exact optimization methodologies: a specialized branch and bound algorithm and an exact integer programming formulation. Moreover, the bridging of these complementary theories reinforces the singular suitability of the featured correlation coefficient to solve the general consensus ranking problem. The latter premise is bolstered by an accompanying set of experiments on random instances, which are generated via a herein developed sampling technique connected with the classic Mallows distribution of ranking data.Associated experiments with the branch and bound algorithm demonstrate that, as data becomes noisier, the featured correlation coefficient yields relatively fewer alternative optimal solutions and that the aggregate rankings tend to be closer to an underlying ground truth shared by a majority.

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Section: Derivation Of New Correlation Coefficient and Its Axiomatic ...mentioning

confidence: 99%

A New Correlation Coefficient for Aggregating Non-strict and Incomplete Rankings

Yoo,

Escobedo,

Skolfield

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Research has attempted to identify the optimal number of options for consumers, and it has been found that a choice set between 8 and 15 options is preferred (Chernev et al, 2015; Sharma & Nair, 2017). Large choice sets may initially be appealing; however, if cognitive load is present, the motivation to make a selection is reduced (Basili & Vannucci, 2015). Studies conducted in the areas of charitable giving and travel souvenirs indicated that choice overload did not occur (Lindkvist & Luke, 2022; Sthapit, 2018).…”

Section: Introductionmentioning

confidence: 99%

Choice overload interferes with early processing and necessitates late compensation: Evidence from electroencephalogram

Hu,

Meng,

2024

Eur J of Neuroscience

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Having a multitude of choices can be advantageous, yet an abundance of options can be detrimental to the decision‐making process. Based on existing research, the present study combined electroencephalogram and self‐reported methodologies to investigate the neural mechanisms underlying the phenomenon of choice overload. Behavioural data suggested that an increase in the number of options led to negative evaluations and avoidance of choice tendencies, even in the absence of time pressure. Event‐related potential results indicated that the large choice set interfered with the early visual process, as evidenced by the small P1 amplitude, and failed to attract more attentional resources in the early stage, as evidenced by the small amplitude of P2 and N2. However, the LPC amplitude was increased in the late stage, suggesting greater investment of attentional resources and higher emotional arousal. Multivariate pattern analysis revealed that the difference between small and large choice set began at around 120 ms, and the early and late stages were characterised by opposite activation patterns. This suggested that too many options interfered with early processing and necessitate continued processing at a later stage. In summary, both behavioural and event‐related potential (ERP) results confirm the choice overload effect, and it was observed that individuals tend to subjectively exaggerate the choice overload effect.

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“…Research has attempted to identify the optimal number of options for consumers, and it has been found that a choice set between 8-15 options is preferred (Chernev et al, 2015;Sharma & Nair, 2017). Large choice sets may initially be appealing, however, if cognitive load is present, the motivation to make a selection is reduced (Basili & Vannucci, 2015). Studies conducted in the areas of charitable giving and travel souvenirs indicated that choice overload did not occur (Lindkvist & Luke, 2022;Sthapit, 2018).…”

Section: Introductionmentioning

confidence: 99%

Choice overload interferes early processing and necessitates late compensation: evidence from electroencephalogram

Hu,

Meng,

2023

Preprint

View full text Add to dashboard Cite

Having a multitude of choices can be advantageous, yet an abundance of options can be detrimental to the decision-making process. Based on existing research, the present study combined electroencephalogram and self-reported methodologies to investigate the neural mechanisms underlying the phenomenon of choice overload. Behavioral data suggested that an increase in the number of options led to negative evaluations and avoidance of choice tendencies, even in the absence of time pressure. Event-related potential results indicated that the large choice set interfered with the early visual process, as evidenced by the small P1 amplitude, and failed to attract more attentional resources in the early stage, as evidenced by the small amplitude of P2 and N2. However, the LPC amplitude was increased in the late stage, suggesting greater investment of attentional resources and higher emotional arousal. Multivariate pattern analysis revealed that the difference between small and large choice set began at around 120ms and the early and late stages were characterized by opposite activation patterns. In conclusion, this study suggested that too many options interfered with early processing and necessitate continued processing at a later stage.

show abstract

Choice Overload and Height Ranking of Menus in Partially-Ordered Sets

Cited by 5 publications

References 34 publications

A New Correlation Coefficient for Aggregating Non-strict and Incomplete Rankings

A New Correlation Coefficient for Aggregating Non-strict and Incomplete Rankings

Choice overload interferes with early processing and necessitates late compensation: Evidence from electroencephalogram

Choice overload interferes early processing and necessitates late compensation: evidence from electroencephalogram

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