scholarly journals Individual Differences in Loss Aversion

2016 ◽  
Vol 42 (4) ◽  
pp. 471-484 ◽  
Author(s):  
Christopher J. Boyce ◽  
Alex M. Wood ◽  
Eamonn Ferguson
Keyword(s):  
Individual Differences ◽  
Loss Aversion

Individual Differences in Loss Aversion

2006 ◽  
Author(s):  
Yariv Cohen ◽  
Eric J. Johnson ◽  
Jayanth Narayanan ◽  
Elke Weber
Keyword(s):  
Individual Differences ◽  
Loss Aversion

scholarly journals Prospect Theory Reflects Selective Allocation of Attention

2017 ◽  
Author(s):  
Thorsten Pachur ◽  
Michael Schulte-Mecklenbeck
Keyword(s):  
Information Processing ◽  
Individual Differences ◽  
Loss Aversion ◽  
Weighting Function ◽  
Risky Choice ◽  
Process Models ◽  
Attention Allocation ◽  
Psychological Constructs ◽  
Probability Information ◽  
As If

There is a disconnect in the literature between analyses of risky choice based on cumulativeprospect theory (CPT) and work on predecisional information processing. One likely reason is that for expectation models (e.g., CPT) it is often assumed that people only behaved as if they conducted the computations leading to the predicted choice, and that the models are thus mute with regard to information processing. We suggest that key psychological constructs in CPT, such as loss aversion and outcome and probability sensitivity, can be interpreted in terms of attention allocation. In two experiments, we tested hypotheses about specific links between CPT parameters and attentional regularities. Experiment 1 used process tracing to monitor participants’ predecisional attention allocation to outcome and probability information. As hypothesized, individual differences in CPT’s loss-aversion, outcome-sensitivity, and probability-sensitivity parameters (estimated from participants’ choices) were systematically associated with individual differences in attention allocation to outcome and probability information. For instance, loss aversion was associated with the relative attention allocated to loss and gain outcomes, and a more strongly curved weighting function was associated with less attention allocated to probabilities. Experiment 2 manipulated participants’ attention to losses or gains, causing systematic differences in CPT’s loss aversion parameter. This result indicates that attention allocation can to some extent cause choice regularities that are captured by CPT. Our findings demonstrate an “as-if” model’s capacity to reflect characteristics of information processing. We suggest that the observed CPT–attention links can be harnessed to inform the development of process models of risky choice.

scholarly journals Losing my loss aversion: The effects of current and past environment on the relative sensitivity to losses and gains

2020 ◽  
Vol 27 (6) ◽  
pp. 1333-1340
Author(s):  
Tim Rakow ◽  
Nga Yiu Cheung ◽  
Camilla Restelli
Keyword(s):  
Individual Differences ◽  
Loss Aversion ◽  
Relative Sensitivity ◽  
Sampling Theory ◽  
Expected Value ◽  
Current Sensitivity ◽  
Consideration Set ◽  
Prior Context ◽  
Decision Environment ◽  
Previous Session

AbstractIt is often assumed that most people are loss averse, placing more weight on losses than commensurate gains; however, some research identifies variability in loss sensitivity that reflects features of the environment. We examined this plasticity in loss sensitivity by manipulating the size and distribution of possible outcomes in a set of mixed gambles, and assessing individual stability in loss sensitivity. In each of two sessions, participants made accept-reject decisions for 64 mixed-outcome gambles. Participants were randomly assigned to conditions defined by the relative range of losses and gains (wider range of losses vs. wider range of gains), and the currency-units at stake (‘pennies’ vs. ‘pounds’). Participants showed modest but non-trivial consistency in their sensitivity to losses; though loss sensitivity also varied substantially with our manipulations. When possible gains had greater range than possible losses, most participants were loss averse; however, when possible losses had the greater range, reverse loss aversion was the norm (i.e., more weight on gains than losses). This is consistent with decision-by-sampling theory, whereby an outcome’s rank within a consideration-set determines its value, but can also be explained by the gamble’s expected-value rank within the decision-set, or by adapting aspirations to the decision-environment. Loss aversion was also reduced in the second session of decisions when the stakes had been higher in the previous session. This illustrates the influence of prior context on current sensitivity to losses, and suggests a role for idiosyncratic experiences in the development of individual differences in loss sensitivity.

Cortical alpha asymmetry at central and posterior – but not anterior – sites is associated with individual differences in behavioural loss aversion

2018 ◽  
Vol 121 ◽  
pp. 206-212 ◽  
Author(s):  
Éilish Duke ◽  
Robert Schnuerch ◽  
Gesine Heeren ◽  
Martin Reuter ◽  
Christian Montag ◽  
...  
Keyword(s):  
Individual Differences ◽  
Loss Aversion ◽  
Alpha Asymmetry

scholarly journals Individual differences in loss aversion and preferences for skewed risks across adulthood.

2018 ◽  
Vol 33 (4) ◽  
pp. 654-659 ◽  
Author(s):  
Kendra L. Seaman ◽  
Mikella A. Green ◽  
Stephen Shu ◽  
Gregory R. Samanez-Larkin
Keyword(s):  
Individual Differences ◽  
Loss Aversion

scholarly journals The Functional and Structural Neural Basis of Individual Differences in Loss Aversion

2013 ◽  
Vol 33 (36) ◽  
pp. 14307-14317 ◽  
Author(s):  
N. Canessa ◽  
C. Crespi ◽  
M. Motterlini ◽  
G. Baud-Bovy ◽  
G. Chierchia ◽  
...  
Keyword(s):  
Individual Differences ◽  
Loss Aversion ◽  
Neural Basis

Individual Differences and the Nature of Loss Aversion

2005 ◽  
Author(s):  
Eric J. Johnson ◽  
Simon Gachter ◽  
Andreas Herrmann
Keyword(s):  
Individual Differences ◽  
Loss Aversion
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