Boundedly rational expected utility theory

Navarro-Martínez, Daniel; Loomes, Graham; Isoni, Andrea; Butler, David; Chrifi‐Alaoui, Larbi

doi:10.1007/s11166-018-9293-3

Cited by 17 publications

(8 citation statements)

References 80 publications

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“…In allowing stochasticity to play a central role in choice, these models are naturally able to capture a large range of behavioral effects that currently lie outside the descriptive scope of deterministic models. Indeed, some of these models even try to explain key decision-making anomalies using only unsystematic noise rather than specific restrictions on value functions or probability weighting ( Bhatia, 2014 ; Navarro-Martinez, Loomes, Isoni, & Butler, 2014 ; also see, e.g., Ratcliff & Rouder, 1998 ). Moreover, experimental work has shown that these types of models outperform many of the deterministic utility models in terms of quantitative fit ( Rieskamp, 2008 ).…”

Section: Beyond Deterministic Modelsmentioning

confidence: 99%

Noisy preferences in risky choice: A cautionary note.

Bhatia

Loomes

2017

Psychological Review

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We examine the effects of multiple sources of noise in risky decision making. Noise in the parameters that characterize an individual’s preferences can combine with noise in the response process to distort observed choice proportions. Thus, underlying preferences that conform to expected value maximization can appear to show systematic risk aversion or risk seeking. Similarly, core preferences that are consistent with expected utility theory, when perturbed by such noise, can appear to display nonlinear probability weighting. For this reason, modal choices cannot be used simplistically to infer underlying preferences. Quantitative model fits that do not allow for both sorts of noise can lead to wrong conclusions.

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Section: Beyond Deterministic Modelsmentioning

confidence: 99%

Noisy preferences in risky choice: A cautionary note.

Bhatia

Loomes

2017

Psychological Review

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“…Thus, during deliberation, incremental mental effort drives the drifting confidence particle until it reaches a threshold and the process terminates with a choice. Another related idea has been proposed in the realm of risky choice (Navarro-Martinez et al, 2018). Here, evidence samples based on probabilistic utility functions serve as input into a t-test calculation (mean / standard error of the mean) and the decision maker stops deliberating when the t-score reaches a target confidence level.…”

Section: Discussionmentioning

confidence: 99%

“…Both perspectives share the notion that by investing deliberation time, one should expect to achieve higher confidence than would be achieved by choosing prematurely (see also (Chaiken et al, 1989)). A related idea has been proposed in the realm of risky choice (Navarro-Martinez et al, 2018). Here, evidence samples based on probabilistic utility functions serve as input into a t-test calculation (mean / standard error of the mean) and the decision-maker stops deliberating when the t-score reaches a target confidence level.…”

Section: Discussionmentioning

confidence: 99%

Evidence or Confidence: What is really monitored during a decision?

Lee

Pezzulo

2021

Preprint

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Assessing one's confidence in one's choices is of paramount importance to making adaptive decisions, and it is thus no surprise that humans excel in this ability. However, standard models of decision-making, such as the drift-diffusion model (DDM), treat confidence assessment as a post-hoc or parallel process that does not directly influence the choice -- the latter depends only on accumulated evidence. Here, we pursue the alternative hypothesis that what is accumulated during a decision is confidence (that the to-be selected option is the best) rather than raw evidence. Accumulating confidence has the appealing consequence that the decision threshold corresponds to a desired level of confidence for the choice, and that confidence improvements can be traded off against the resources required to secure them. We show that most previous findings on perceptual and value-based decisions traditionally interpreted from an evidence-accumulation perspective can be explained more parsimoniously from our novel confidence-driven perspective. Furthermore, we show that our novel confidence-driven DDM (cDDM) naturally generalizes to any number of decisions -- which is notoriously extemporaneous using traditional DDM or related models. Finally, we discuss future empirical evidence that could be useful in adjudicating between these alternatives.

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“…This study builds on recent emerging research into decision making in AEC sector to support improved delivery of project benefits [33,34]. This research is cast in the perspective of utility theory, an MCDM technique for defining utility of decision making.…”

Section: Methodsmentioning

confidence: 99%

A Utilitarian Decision—Making Approach for Front End Design—A Systematic Literature Review

2020

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The complexity of construction processes often means interaction between various stakeholders, activities and tasks in order to deliver the expected outcomes. The intensity and dynamics of front-end design (FED) mean decision techniques and methods are important in supporting projects benefits delivery more importantly those based on utility of decision making. This paper explores a new utilitarian decision-making approach based on a systematic literature review of FED decision making. It presents the state of the art in design decision making concepts and analysis of tools over the last 10 years (2009–2019). From a total of 111 peer-reviewed journal papers, fifteen decision-making techniques are identified as dominant in design decision making, broadly grouped in four major categories as explanatory/rational, Multi Criteria Decision Making techniques (MCDM), Hybrid and Visual methods. The review finds that the most applied of the MCDM is Quality Function Deployment (QFD); while among the rational/explanatory techniques is set-based design (SBD). While there is limited application of Multi Attribute Utility Theory (MAUT) in decision making, the paper finds that the robust consistency and structured approach better captures the intricate dynamics of FED; including modelling of the subjectivity, interdependences and uncertainty in design discourse.

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Boundedly rational expected utility theory

Cited by 17 publications

References 80 publications

Noisy preferences in risky choice: A cautionary note.

Noisy preferences in risky choice: A cautionary note.

Evidence or Confidence: What is really monitored during a decision?

A Utilitarian Decision—Making Approach for Front End Design—A Systematic Literature Review

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