Sudeep Bhatia
Biographic Data
| ID | 3609650 |
|---|---|
| NAME | Sudeep Bhatia |
| GIVEN NAMES | Sudeep |
| FAMILY NAME | Bhatia |
| SIGNATURE | BHATIA S |
| AFFILIATIONS | University of Pennsylvania |
| ORCID | 0000-0001-6068-684X |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 27 |
| AUTHOR COUNT | 13 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
Extremeness Aversion and Choice Set Composition: Exposure to Multiple Extreme Options Reduces Extremeness Aversion
Extremeness aversion—the tendency for consumers to prefer middling options in a choice set—is an incredibly robust and well-studied phenomenon. However, it has primarily been studied in the context of two- or three-option choice sets. In six studies (Ntotal = 9,377), we suggest that consumers’ aversion to extreme options depends on the frequency of similar options in the choice set. In particular, we find that consumers are relatively more likely…
A deep learning approach to personality assessment: Generalizing across items and expanding the reach of survey-based research
Traditional methods of personality assessment, and survey-based research in general, cannot make inferences about new items that have not been surveyed previously. This limits the amount of information that can be obtained from a given survey. In this article, we tackle this problem by leveraging recent advances in statistical natural language processing. Specifically, we extract "embedding" representations of questionnaire items from deep neural…
Years of Context Effects: Merging the Behavioral and Quantitative Perspectives
Over the past 50 years, consumer researchers have presented extensive evidence that consumer preference can be swayed by the decision context, particularly the configuration of the choice set. Importantly, behavioral research on context effects has inspired prominent quantitative research on multialternative decision-making published in leading psychology, management, economics, and general interest journals. While both streams of research seem t…
Information acquisition and decision strategies in intertemporal choice
Semantic determinants of memorability
Predicting leadership perception with large-scale natural language data
Judgment errors in naturalistic numerical estimation
Psychological mechanisms of loss aversion: A drift-diffusion decomposition
Changes in Gender Stereotypes Over Time: A Computational Analysis
We combined established psychological measures with techniques in machine learning to measure changes in gender stereotypes over the course of the 20th century as expressed in large-scale historical natural language data. Although our analysis replicated robust gender biases previously documented in the literature, we found that the strength of these biases has diminished over time. This appears to be driven by changes in gender biases for stereo…
Optimal cue aggregation in the absence of criterion knowledge
The study of multi‐cue judgment investigates how decision makers aggregate cues to predict the value of a criterion variable. We consider a multi‐cue judgment task in which decision makers have prior knowledge of inter‐cue relationships but are ignorant of how the cues correlate with the criterion. In this setting, a naive judgment strategy prescribes weighting the cues equally. Although many participants are well described via an equal weighting…
Trait Associations for Hillary Clinton and Donald Trump in News Media: A Computational Analysis
We study media representations of Hillary Clinton and Donald Trump in the 2016 U.S. presidential election. In particular, we train models of semantic memory on a large number of news media outlets that published online articles during the course of the election. Based on the structure of word co-occurrence in these media outlets, our models learn semantic representations for the two presidential candidates as well as for widely studied personalit…
Decision Making in Environments with Non‐Independent Dimensions
This paper tests whether the dimensions involved in preferential choice tasks are evaluated independently from one another. Common decision heuristics satisfy dimensional independence, and multi‐strategy models that assume that decision makers use a repertoire of these heuristics predict that they are unable to represent and respond to dimensional dependencies in the decision environment. In contrast, some single‐strategy models are able to viola…
Confirmatory Search and Asymmetric Dominance
Decision makers use confirmatory search strategies in judgment tasks. As a result of this, their attention towards task‐relevant cues is biased in favor of cues supporting available responses. Changing these responses can alter the cues used in the judgment task and, subsequently, alter beliefs. We use this mechanism to predict and explain the emergence of the asymmetric dominance effect in judgment. In four sets of experiments, we document syste…
Changes in Gender Stereotypes Over Time: A Computational Analysis
We combined established psychological measures with techniques in machine learning to measure changes in gender stereotypes over the course of the 20th century as expressed in large-scale historical natural language data. Although our analysis replicated robust gender biases previously documented in the literature, we found that the strength of these biases has diminished over time. This appears to be driven by changes in gender biases for stereo…
Years of Context Effects: Merging the Behavioral and Quantitative Perspectives
Over the past 50 years, consumer researchers have presented extensive evidence that consumer preference can be swayed by the decision context, particularly the configuration of the choice set. Importantly, behavioral research on context effects has inspired prominent quantitative research on multialternative decision-making published in leading psychology, management, economics, and general interest journals. While both streams of research seem t…
A deep learning approach to personality assessment: Generalizing across items and expanding the reach of survey-based research
Traditional methods of personality assessment, and survey-based research in general, cannot make inferences about new items that have not been surveyed previously. This limits the amount of information that can be obtained from a given survey. In this article, we tackle this problem by leveraging recent advances in statistical natural language processing. Specifically, we extract "embedding" representations of questionnaire items from deep neural…
Confirmatory Search and Asymmetric Dominance
Decision makers use confirmatory search strategies in judgment tasks. As a result of this, their attention towards task‐relevant cues is biased in favor of cues supporting available responses. Changing these responses can alter the cues used in the judgment task and, subsequently, alter beliefs. We use this mechanism to predict and explain the emergence of the asymmetric dominance effect in judgment. In four sets of experiments, we document syste…
Confirmatory Search and Asymmetric Dominance
Decision makers use confirmatory search strategies in judgment tasks. As a result of this, their attention towards task‐relevant cues is biased in favor of cues supporting available responses. Changing these responses can alter the cues used in the judgment task and, subsequently, alter beliefs. We use this mechanism to predict and explain the emergence of the asymmetric dominance effect in judgment. In four sets of experiments, we document syste…
Decision Making in Environments with Non‐Independent Dimensions
This paper tests whether the dimensions involved in preferential choice tasks are evaluated independently from one another. Common decision heuristics satisfy dimensional independence, and multi‐strategy models that assume that decision makers use a repertoire of these heuristics predict that they are unable to represent and respond to dimensional dependencies in the decision environment. In contrast, some single‐strategy models are able to viola…
Trait Associations for Hillary Clinton and Donald Trump in News Media: A Computational Analysis
We study media representations of Hillary Clinton and Donald Trump in the 2016 U.S. presidential election. In particular, we train models of semantic memory on a large number of news media outlets that published online articles during the course of the election. Based on the structure of word co-occurrence in these media outlets, our models learn semantic representations for the two presidential candidates as well as for widely studied personalit…
Optimal cue aggregation in the absence of criterion knowledge
The study of multi‐cue judgment investigates how decision makers aggregate cues to predict the value of a criterion variable. We consider a multi‐cue judgment task in which decision makers have prior knowledge of inter‐cue relationships but are ignorant of how the cues correlate with the criterion. In this setting, a naive judgment strategy prescribes weighting the cues equally. Although many participants are well described via an equal weighting…
Psychological mechanisms of loss aversion: A drift-diffusion decomposition
Changes in Gender Stereotypes Over Time: A Computational Analysis
We combined established psychological measures with techniques in machine learning to measure changes in gender stereotypes over the course of the 20th century as expressed in large-scale historical natural language data. Although our analysis replicated robust gender biases previously documented in the literature, we found that the strength of these biases has diminished over time. This appears to be driven by changes in gender biases for stereo…
Judgment errors in naturalistic numerical estimation
Predicting leadership perception with large-scale natural language data
Information acquisition and decision strategies in intertemporal choice
Semantic determinants of memorability
A deep learning approach to personality assessment: Generalizing across items and expanding the reach of survey-based research
Traditional methods of personality assessment, and survey-based research in general, cannot make inferences about new items that have not been surveyed previously. This limits the amount of information that can be obtained from a given survey. In this article, we tackle this problem by leveraging recent advances in statistical natural language processing. Specifically, we extract "embedding" representations of questionnaire items from deep neural…
Years of Context Effects: Merging the Behavioral and Quantitative Perspectives
Over the past 50 years, consumer researchers have presented extensive evidence that consumer preference can be swayed by the decision context, particularly the configuration of the choice set. Importantly, behavioral research on context effects has inspired prominent quantitative research on multialternative decision-making published in leading psychology, management, economics, and general interest journals. While both streams of research seem t…
Extremeness Aversion and Choice Set Composition: Exposure to Multiple Extreme Options Reduces Extremeness Aversion
Extremeness aversion—the tendency for consumers to prefer middling options in a choice set—is an incredibly robust and well-studied phenomenon. However, it has primarily been studied in the context of two- or three-option choice sets. In six studies (Ntotal = 9,377), we suggest that consumers’ aversion to extreme options depends on the frequency of similar options in the choice set. In particular, we find that consumers are relatively more likely…
Psychology (11 works) · Cognitive psychology (9 works) · Computer Science (9 works) · Decision-Making and Behavioral Economics (8 works) · Social Psychology (8 works) · Artificial Intelligence (5 works) · Economic and Environmental Valuation (5 works) · Mathematics (4 works) · Big Five personality traits (3 works) · Cognition (3 works)