Searching for the Reference Point

Baillon, Aurélien; Bleichrodt, Han; Spinu, Vitalie

doi:10.1287/mnsc.2018.3224

Cited by 122 publications

(72 citation statements)

References 92 publications

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“…In what follows we assume that all variables are chosen such that final wealth and final longevity levels always remain strictly positive. Let 1 and 2 2 A constant initial endowment induces a change in reference point if subjects use the expected value or the maximum of the minimum outcomes of a lottery as a point of reference, as has recently been found; in particular, Baillon et al (2017) observe that the majority of subjects use the status quo or the maximum of the minimum outcomes as their reference points in a high stakes experiment. As can be seen in Figure 1, the choice between the prospect A and B amounts to whether the individual prefers to aggregate or disaggregate the fixed wealth reductions across equally likely states of nature.…”

Section: Univariate Risk Preferencesmentioning

confidence: 79%

Measuring multivariate risk preferences in the health domain

Attema

l’Haridon

Kuilen

2019

Journal of Health Economics

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 Multivariate risk preferences are theoretically important to explain healthrelated behavior.  However, empirical evidence on multivariate risk preferences is very scarce for health outcomes.  We measure higher order risk preferences for health and wealth by means of the risk apportionment technique.  We observe strong evidence for risk aversion and prudence for health and wealth in the gain domain, but more neutrality in the loss domain.  In the multivariate setting, we find correlation aversion and cross-prudence for gains, and correlation seeking for losses.  We conclude that there is clear evidence that multivariate risk preferences are sign-dependent.

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Section: Univariate Risk Preferencesmentioning

confidence: 79%

Measuring multivariate risk preferences in the health domain

Attema

l’Haridon

Kuilen

2019

Journal of Health Economics

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“…We prove by definition. ∀γ 1 , γ 2 ∈ R, γ 1 < γ 2 , and R =x + bγ,x ∈ R, according to (7), A s R (γ 1 ) < A s R (γ 2 ). To calculate U s R (γ), all possible outcomes u(γ) = x + bγ shift the same amount b|γ 1 − γ 2 | as R does, hence the contributions to U s R (γ) remain the same as both the weighing and V [u(γ)] remain the same, therefore U s R (γ 1 ) = U s R (γ 2 ), hence p s R (γ 1 ) > p s R (γ 2 ).…”

Section: Appendix: Proofs Of Propertiesmentioning

confidence: 99%

Cumulative Prospect Theory Based Dynamic Pricing for Shared Mobility on Demand Services

Guan

Annaswamy

Tseng

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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Cumulative Prospect Theory (CPT) is a modeling tool widely used in behavioral economics and cognitive psychology that captures subjective decision making of individuals under risk or uncertainty. In this paper, we propose a dynamic pricing strategy for Shared Mobility on Demand Services (SMoDSs) using a passenger behavioral model based on CPT. This dynamic pricing strategy together with dynamic routing via a constrained optimization algorithm that we have developed earlier, provide a complete solution customized for SMoDS of multi-passenger transportation. The basic principles of CPT and the derivation of the passenger behavioral model in the SMoDS context are described in detail. The implications of CPT on dynamic pricing of the SMoDS are delineated using computational experiments involving passenger preferences. These implications include interpretation of the classic fourfold pattern of risk attitudes, strong risk aversion over mixed prospects, and behavioral preferences of self reference. Overall, it is argued that the use of the CPT framework corresponds to a crucial building block in designing socio-technical systems by allowing quantification of subjective decision making under risk or uncertainty that is perceived to be otherwise qualitative.our earlier work in [13] and [5], and offer a solution based on Cumulative Prospect Theory for determining dynamic tariffs.The results of [13] correspond to designing dynamic routes for passengers who request the SMoDS, based upon the requested pickup, drop-off locations, and a pre-specified bound on the walking distance by each passenger. An Alternating Minimization (AltMin) based algorithm was presented that optimizes a relevant time cost. The SMoDS server then offered pickup and drop-off locations as well as walking, waiting and riding times to each passenger derived via the AltMin algorithm. The notion of Transactive Control in [5] was introduced to enable the SMoDS to offer a dynamic tariff to the passenger which can serve as an incentive for the decision on the offer. A passenger behavioral model based on Utility Theory [22] was derived, with the utility of the passenger being a function of both travel times and tariff. The resulting socio-technical model that combines the passenger behavioral model and the optimization of dynamic routes was used to derive a desired probability of acceptance that led to the average estimated waiting time of passengers on the SMoDS platform being regulated around a desired value. The derivation of the actual dynamic tariffs was however not addressed and assumed to be such that the desired probability of acceptance from each passenger was realized.The results mentioned above have two deficiencies. The first is that the passenger behavioral model is significantly more complex than that considered in [5]. Strategic decision making, adjustments based upon framing effect, loss aversion, and probability distortion are several key features related to subjective decision making of individuals when facing uncertainty, which makes classic ...

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“…This leads to an overestimation of risk aversion if reference-dependence is not taken into account. In a recent investigation of the determinants of reference points, Baillon, Bleichrodt, and Spinu (2015) showed that about 31% of subjects fix on such 'max-min' reference points.…”

Section: Reference Dependence and Preference Modeling Under Ptmentioning

confidence: 99%

Violence and Risk Preference: Experimental Evidence from Afghanistan: Comment

Vieider

2018

American Economic Review

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In this comment on Callen et al. (2014), I revisit recent evidence uncovering a “preference for certainty” in violation of dominant normative and descriptive theories of decision-making under risk. I show that the empirical findings are potentially confounded by systematic noise. I then develop choice lists that allow me to disentangle these different explanations. Experimental results obtained with these lists reject explanations based on a preference for certainty in favor of explanations based on random choice. From a theoretical point of view, the levels of risk aversion detected in the choice list involving certainty can be accounted for by prospect theory through reference dependence activated by salient outcomes. (JEL C91, D12, D74, D81, O12, O17)

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Searching for the Reference Point

Cited by 122 publications

References 92 publications

Measuring multivariate risk preferences in the health domain

Measuring multivariate risk preferences in the health domain

Cumulative Prospect Theory Based Dynamic Pricing for Shared Mobility on Demand Services

Violence and Risk Preference: Experimental Evidence from Afghanistan: Comment

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