An economic approach to consumer product ratings

In three essays we examine user-generated product ratings with aggregation. While recommendation systems have been studied extensively, this simple type of recommendation system has been neglected, despite its prevalence in the field. We develop a novel theoretical model of user-generated ratings. This model improves upon previous work in three ways: it considers rational agents and allows them to abstain from rating when rating is costly; it incorporates rating aggregation (such as averaging ratings); and it considers the effect on rating strategies of multiple simultaneous raters. In the first essay we provide a partial characterization of equilibrium behavior. In the second essay we test this theoretical model in laboratory, and in the third we apply established behavioral models to the data generated in the lab. This study provides clues to the prevalence of extreme-valued ratings in field implementations. We show theoretically that in equilibrium, ratings distributions do not represent the value distributions of sincere ratings. Indeed, we show that if rating strategies follow a set of regularity conditions, then in equilibrium the rate at which players participate is increasing in the extremity of agents' valuations of the product. This theoretical prediction is realized in the lab. We also find that human subjects show a disproportionate predilection for sincere rating, and that when they do send insincere ratings, they are almost always in the direction of exaggeration. Both sincere and exaggerated ratings occur with great frequency despite the fact that such rating strategies are not in subjects' best interest. We therefore apply the behavioral concepts of quantal response equilibrium (QRE) and cursed equilibrium (CE) to the experimental data. Together, these theories explain the data significantly better than does a theory of rational, Bayesian behavior -- accurately predicting key comparative statics. However, the theories fail to predict the high rates of sincerity, and it is clear that a better theory is needed.

A study of protein targeting reveals insights into mitigating protein aggregation

A unique chloroplast Signal Recognition Particle (SRP) in green plants is primarily dedicated to the post-translational targeting of light harvesting chlorophyll-a/b binding (LHC) proteins. Our study of the thermodynamics and kinetics of the GTPases of the system demonstrates that GTPase complex assembly and activation are highly coupled in the chloroplast GTPases, suggesting they may forego the GTPase activation step as a key regulatory point. This reflects adaptations of the chloroplast SRP to the delivery of their unique substrate protein. Devotion to one highly hydrophobic family of proteins also may have allowed the chloroplast SRP system to evolve an efficient chaperone in the cpSRP43 subunit. To understand the mechanism of disaggregation, we showed that LHC proteins form micellar, disc-shaped aggregates that present a recognition motif (L18) on the aggregate surface. Further molecular genetic and structure-activity analyses reveal that the action of cpSRP43 can be dissected into two steps: (i) initial recognition of L18 on the aggregate surface; and (ii) aggregate remodeling, during which highly adaptable binding interactions of cpSRP43 with hydrophobic transmembrane domains of the substrate protein compete with the packing interactions within the aggregate. We also tested the adaptability of cpSRP43 for alternative substrates, specifically in attempts to improve membrane protein expression and inhibition of amyloid beta fibrillization. These preliminary results attest to cpSRP43’s potential as a molecular chaperone and provides the impetus for further engineering endeavors to address problems that stem from protein aggregation.