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Mukherjee Decision Lab advances fundamental concepts, psychological foundations and cognitive mechanisms about judgment and decision making.
The role of stakes in valuation of gains and losses
2021-2023
A mini review of what have we known so far
Team: Sumitava Mukherjee, Ouroz Khan and Narayanan Srinivasan (IIT Kanpur)
Under review
2021-2023
Examining choices involving gains and losses of money under risk for different sizes of stake in equiprobable gambles
Team: Sumitava Mukherjee and Ouroz Khan
Manuscript under preparation
2019-2020
Typical navigation apps provide route suggestions during local travel. The eventual time taken for a drive could either be more or less than the predicted time. Using comparative hedonic judgments, the impact of prospective gains versus losses of time was examined for common contexts like waiting and local travel. It was found that contrary to the classic version of loss aversion, small gains of lime loomed larger or equal to small losses of time. This dovetail with previous work on money by Mukherjee et al. (2017) that suggested loss aversion is magnitude dependent.
Team: Sumitava Mukherjee and Narayanan Srinivasan (IIT Kanpur)
Publication: Sumitava Mukherjee & Narayanan Srinivasan (2021). Cognition and Emotion, 35(5), 1049-1055 DOI:10.1080/02699931.2021.1907741
Understanding psychological mechanisms using computational models
2021-2023
It is a well-known observation that people tend to dislike risky situations that could potentially lead to a loss, a phenomenon that is called loss aversion. This is often explained using valuation bias, i.e., the subjective value of losses is larger than the subjective value of gains of equal magnitude. However, recent studies using the drift-diffusion model have shown that a pre-valuation bias towards rejection is also a primary determinant of loss-averse behavior. It has large contributions to model fits, predicts a key relationship between rejection rates and response times, and explains the most individual heterogeneity in the rejection rates of participants. We analyzed data from three previously published experiments using the drift-diffusion model and found that these findings generalize to them. However, we found that valuation bias plays the most important role in predicting how likely a person is to accept a given gamble. Our findings also showed that a person's loss aversion parameter (lambda), which captures their propensity to avoid losses is closely related to valuation bias. These results highlight the importance of valuation bias in understanding people's choice patterns. Finally, using the leaky competing accumulator model, we show strong mimicking between valuation bias and an attentional bias wherein people pay more attention to losses as compared to gains. This finding suggests that behaviors that seem to arise due to valuation bias may arise due to such an attentional bias.
Team: Nishad Singhi, Sumeet Agarwal, and Sumitava Mukherjee
Publication: Singhi, N., Agarwal, S., & Mukherjee, S. (2023). Using Computational Models to Understand the Role and Nature of Valuation Bias in Mixed Gambles. Proceedings of the Annual Meeting of the Cognitive Science Society, 45. Retrieved from https://escholarship.org/uc/item/8p78p112
How do we value lives of others?
2019-2021
Outcomes of clinical trials need to be communicated effectively to make decisions that save lives. We investigated whether framing can bias these decisions and if risk preferences shift depending on the number of patients. Hypothetical information about two medicines used in clinical trials having a sure or a risky outcome was presented in either a gain frame (people would be saved) or a loss frame (people would die). The number of patients who signed up for the clinical trials was manipulated in both frames in all the experiments. Using an unnamed disease, lay participants (experiment 1) and would-be medical professionals (experiment 2) were asked to choose which medicine they would have administered. For COVID-19, lay participants were asked which medicine should medical professionals (experiment 3), artificially intelligent software (experiment 4), and they themselves (experiment 5) favour to be administered. Broadly consistent with prospect theory, people were more risk-seeking in the loss frames than the gain frames. However, risk-aversion in gain frames was sensitive to the number of lives with risk-neutrality at low magnitudes and risk-aversion at high magnitudes. In the loss frame, participants were mostly risk-seeking. This pattern was consistent across laypersons and medical professionals, further extended to preferences for choices that medical professionals and artificial intelligence programmes should make in the context of COVID-19. These results underscore how medical decisions can be impacted by the number of lives at stake while revealing inconsistent risk preferences for clinical trials during a real pandemic.
Team: Sumitava Mukherjee, Interns (Divya Reji, Anusha Tomar, Nataasha Khattar)
Publication: Sumitava Mukherjee & Divya Reji (2021). Quarterly Journal of Experimental Psychology DOI: 10.1177/17470218211052037
On Gains and Losses
2019
There seems to be at least three possible scenarios about loss aversion: (a) it is more contextual and nuanced than previously thought, (b) not observable most of the time, (c) superfluous as an explanation (Gal, 2006). If in the face of new empirical evidences, we do not assume that loss aversion is a principle (and hence is always true); then we should not conclude any evidences to the contrary as boundary conditions. It is indeed possible that empirical studies that found contradictions imply we need a theoretical update.
Publication: Mukherjee, S. (2019). Revise the belief in loss aversion. Frontiers in Psychology 10:2723 doi: 10.3389/fpsyg.2019.02723
Mukherjee Decision Lab advances fundamental concepts, psychological foundations and cognitive mechanisms about judgment and decision making.
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