The Social Optimization of Collective Judgment: A Multiobjective Framework from Consensus to Human-Machine Collaboration

By Zhen-Song Chen

The Social Optimization of Collective Judgment: A Multiobjective Framework from Consensus to Human-Machine Collaboration presents a unified treatment of expert judgment aggregation under uncertainty. The book demonstrates how a broad family of optimization-based aggregation methods can be understood within a single framework built on simplex-constrained quantile averaging. It develops aggregation models that progressively incorporate consensus, fairness, social welfare, altruism, reproducibility, scalability, and human-machine collaboration, providing both theoretical foundations and practical implementation guidance.

The book begins with the foundations of classical pooling and optimization-driven aggregation before developing socially grounded aggregation methods that incorporate fairness, welfare, and altruistic objectives. It then addresses scalable and reproducible optimization techniques, including hierarchical aggregation and convex reformulations. A dedicated chapter examines the integration of large language models as virtual experts within auditable decision processes. Five worked case studies apply the framework to building information modeling and digital twin maturity assessment, digital transformation barriers, rail transit accessibility, and human-machine forecasting, followed by a discussion of challenges, open problems, and future research directions.

The book will be valuable to researchers and graduate students in decision analysis, operations research, group decision-making, computational intelligence, forecasting, and social choice; data scientists and machine learning researchers working on calibration and ensemble methods; policy analysts, public administration specialists, engineers, economists, finance professionals, transportation researchers, and public health analysts who require transparent and reproducible approaches to combining expert judgments under uncertainty.

• Format: Paperback
• Language: English
• ISBN: 0443583145
• ISBN13: 9780443583148
• Release Date: June 2027
• Publisher: Elsevier
• Length: 295 Pages