We propose a class of multiple-prior representations of preferences under ambiguity, where the belief the decision-maker (DM) uses to evaluate an uncertain prospect is the outcome of a game played by two conflicting forces, Pessimism and Optimism. The model does not restrict the sign of the DM’s ambiguity attitude, and we show that it provides a unified framework through which to characterize different degrees of ambiguity aversion, and to represent the co-existence of negative and positive ambiguity attitudes within individuals as documented in experiments. We prove that our baseline representation, dual-self expected utility (DSEU), yields a novel representation of the class of invariant biseparable preferences (Ghirardato, Maccheroni, and Marinacci, 2004), which drops uncertainty aversion from maxmin expected utility (Gilboa and Schmeidler, 1989), while extensions of DSEU allow for more general departures from independence. We also provide foundations for a generalization of prior-by-prior belief updating to our model.
We propose a class of multiple-prior representations of preferences under ambiguity where the belief the decision-maker (DM) uses to evaluate an uncertain prospect is the outcome of a game played by two conflicting forces, Pessimism and Optimism. The model does not restrict the sign of the DM’s ambiguity attitude, and we show that it provides a unified framework through which to characterize different degrees of ambiguity aversion, as well as to represent context-dependent negative and positive ambiguity attitudes documented in experiments. We prove that our baseline representation, Boolean expected utility (BEU), yields a novel representation of the class of invariant biseparable preferences (Ghirardato, Maccheroni, and Marinacci, 2004), which drops uncertainty aversion from maxmin expected utility (Gilboa and Schmeidler, 1989), while extensions of BEU allow for more general departures from independence.
The authors address two novel and significant challenges in using online text reviews to obtain attribute level ratings. First, they introduce the problem of inferring attribute level sentiment from text data to the marketing literature and develop a deep learning model to address it. While extant bag of words based topic models are fairly good at attribute discovery based on frequency of word or phrase occurrences, associating sentiments to attributes requires exploiting the spatial and sequential structure of language. Second, they illustrate how to correct for attribute self-selection—reviewers choose the subset of attributes to write about—in metrics of attribute level restaurant performance. Using Yelp.com reviews for empirical illustration, they find that a hybrid deep learning (CNN-LSTM) model, where CNN and LSTM exploit the spatial and sequential structure of language respectively provide the best performance in accuracy, training speed and training data size requirements. The model does particularly well on the “hard” sentiment classification problems. Further, accounting for attribute self-selection significantly impacts sentiment scores, especially on attributes that are frequently missing.
A critical element of word of mouth (WOM) or buzz marketing is to identify seeds, often central actors with high degree in the social network. Seed identification typically requires data on the full network structure, which is often unavailable. We therefore examine the impact of WOM seeding strategies motivated by the friendship paradox to obtain more central nodes without knowing network structure on adoption. Higher-degree nodes may be less effective as seeds if these nodes communicate less with neighbors or are less persuasive when they communicate; therefore whether friendship paradox motivated seeding strategies increase or reduce WOM and adoption remains an empirical question. We develop and estimate a model of WOM and adoption using data on microfinance adoption across 43 villages in India for which we have data on social networks. Counterfactuals show that the proposed seeding strategies are about 15-24% more effective in increasing adoption relative to random seeding. These strategies are also about 5-13% more effective than the firm’s leader seeding strategy, and are relatively more effective when we have fewer seeds.
The authors address two significant challenges in using online text reviews to obtain finegrained attribute level sentiment ratings. First, in contrast to methods that rely on word frequency, they develop a deep learning convolutional-LSTM hybrid model to account for language structure. The convolutional layer accounts for spatial structure (adjacent word groups or phrases) and LSTM accounts for sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in constructing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior attribute sentiment scoring accuracy with their model. They find three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Reviewers write to inform and vent/praise, but not based on attribute importance. The heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings. More broadly, our results suggest that social science research should pay more attention to reduce measurement error in variables constructed from text.
The authors address two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, they develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in construct-ing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior accuracy in converting text to numerical attribute sentiment scores with their model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings.
Behavior at the individual level in panels or at the station level in spatial models is often influenced by aspects of the system in aggregate. In particular, the nature of the interaction between individual-specific explanatory variables and an individual dependent variable may be affected by `global’ variables that are relevant in decision making and shared communally by all individuals in the sample. To capture such behavioral features, we employ a functional coefficient panel model in which certain communal covariates may jointly influence panel interactions by means of their impact on the model coefficients. Two classes of estimation procedures are proposed, one based on station averaged data the other on the full panel, and their asymptotic properties are derived. Inference regarding the functional coefficient is also considered. The finite sample performance of the proposed estimators and tests are examined by simulation. An empirical spatial model illustration is provided in which the climate sensitivity of temperature to atmospheric CO_2 concentration is studied at both station and global levels.