Description:
Trendme - Ratings in Real Time for Better Decisions for Consumers and for Providers in Improved Services and Goods
Docket # 21-3769
Researchers in the Department of Sociology at Princeton University and in the Department of Psychology at Hunter College have developed a new process by which consumers and/or producers of goods and/or services can organize ratings temporally. The process can be achieved through a web browser extension on the user side or by the producer side as a feature of their display of ratings. This innovation allows the user to adjudicate between choices with more timely and relevant information (i.e. the difference between two “four-star” rated restaurants may be that one is improving in quality and the other is declining). Moreover, it allows the producer or service provider to learn from the trend information, by observing the impact of dynamic changes in the nature of the service or product on ratings in real time.
Trendme can be used by end users to make better decisions and by producers to improve services/goods through social learning.
Background:
The statistical gold standard for designing crowd-sourced information systems has been to obtain independent and unbiased evaluations (Heckman 1979). But in crowd-sourced systems that rely on voluntary cooperation, outcomes may depend on dynamic interactions between cooperation and crowd wisdom. Minimizing social influences then bears the cost of compromising crowd wisdom. More importantly, minimizing social influences may interfere with efforts to perpetuate cooperation. In a preliminary field study, we demonstrated a positive outcome of increasing social influences in a rating system (Tchernichovski et al. 2017). In a more recent study (Tchernichovski et al. 2019) we demonstrated the utility of virtual world experiment in optimizing crowd wisdom via costly signaling (Princeton Docket # 20-3660, A Rating Method for Improved Accuracy of Customer Evaluations and Cost Savings). Trendme extends these virtual world experiments into a generalized system for optimizing the utility of social influences in crowd sourced evaluation systems by presentation of trends of ratings/evaluations in lieu of the traditional approach of reporting cumulative scores.
Applications:
• Improved goods and services provided by producers thanks to realtime information
• Better decision making by end users of goods and services
Advantages:
• Optimizes social influences in crowd-sourced evaluation systems
• Presents trends/evaluations in lieu of traditional cumulative scores
• Improvement (or decline0 in satisfaction with services/goods overtime easily detectable
• Detection of trends for improved decision-making by both providers and consumers
Publication
Tchernichovski, O., S. Frey, N. Jacoby, D. Conley 2021. Experimenting with online governance. Research Topic Title: Peer Governance in Online Communities Journal/Specialty: Frontiers in Human Dynamics - Social Networks.
Inventors
Dalton Conley is the Henry Putnam University Professor in Sociology. He holds PhDs in both sociology and biology. Conley’s scholarship has primarily dealt with the intergenerational social and genetic transmission of socioeconomic and health status from parents to children. He has been the recipient of Guggenheim, Robert Wood Johnson Foundation and Russell Sage Foundation fellowships as well as a CAREER Award and the Alan T. Waterman Award from the National Science Foundation. He is an elected fellow of the American Academy of Arts and Sciences and an elected member of the National Academy of Sciences.
Ofer Tchernichovski is Professor of Psychology, Hunter College, City University of New York. A Ph.D. in zoology, he uses the songbird to study mechanisms of vocal learning. Like early speech development in the human infant, the songbird learns to imitate complex sounds during a critical period of development. His lab studies the animal behavior and dynamics of vocal learning and sound production across different brain levels. The lab aims to uncover the specific physiological and molecular (gene expression) brain processes that underlie song learning.
Contact
Laurie Tzodikov
tzodikov@princeton.edu
Princeton University Office of Technology Licensing
(609) 258-7256