This article explores how mathematical modeling, once confined to the sciences, now enriches ten humanities fields—from economics and management to sociology, psychology, and history—by providing quantitative frameworks that deepen understanding, improve predictions, and support evidence‑based decision making.
Mathematical modeling transforms real-world problems into solvable mathematical structures, offering a problem‑driven, step‑by‑step learning path that builds optimization, probability, and differential equation skills while enhancing abstract thinking, interdisciplinary insight, and practical decision‑making for everyday challenges.
The recap details Lin Ziwei’s September 2025 talk on service design’s evolution, key milestones, core tools like personas and user maps, applications in public services, and the discipline’s future interdisciplinary direction, highlighting how designers can shift from one‑way supply to collaborative co‑creation.
This article presents a visual guide to thirty classic mathematical models—spanning natural sciences, social sciences, engineering, and interdisciplinary fields—detailing their core ideas, practical applications, and key insights, illustrating how each model serves as a powerful tool for solving complex real‑world problems.
Understanding the underlying assumptions—whether humans are rational economic agents, emotion‑driven animals, socially constructed beings, gene‑driven organisms, or information processors—reveals why different disciplines model decision‑making differently and highlights each perspective’s insights and limitations.
The Von Bertalanffy growth equation models how organisms transition from slow to rapid to stable growth by balancing synthesis and decomposition, and its principles extend beyond biology to ecology, agriculture, economics, and personal development, highlighting growth as a dynamic equilibrium rather than endless acceleration.
The article explores the characteristics of long‑lasting mathematical models—continuous refinement, expanding applicability, elegant simplicity, extensibility, focus on essence, and philosophical depth—illustrated with examples such as neural networks and the Lotka‑Volterra predator‑prey system, and offers guidance on creating such vibrant models.
Exploring the essential role of mathematics, this article traces its evolution from ancient survival tools to a universal language of rational thought, examines how its applications shape modern life and technology, highlights its limitations, and envisions its future as a bridge across disciplines.
This article introduces Qian Xuesen’s “Great Wisdom” approach, a systematic, interdisciplinary method that breaks down complex problems into manageable subsystems and then integrates them to achieve dynamic, adaptable solutions.
This article examines the interdisciplinary concept of balance, defining it as a system’s stable state, and explores how equilibrium arises and functions in physics, chemistry, biology, economics, and psychology, while questioning whether equilibrium always signifies a desirable or optimal condition.
The article invites readers to revive the mathematician, physicist, economist and other scholarly personas within themselves, showing how interdisciplinary concepts such as Voronoi diagrams, graph theory, knot theory, non‑Euclidean geometry, fuzzy logic and fractal dimensions can enrich everyday problem‑solving and inspire a love of mathematics.
In a recent TV interview, Baidu's CEO predicted the programmer profession may disappear as anyone with basic communication skills could code, while 360's CEO countered with three arguments emphasizing the enduring need for computer experts, the surge in demand from large AI models, and the rise of interdisciplinary AI‑biology talent.
The article critiques the over‑use of Agile in organizations, argues that many have become fatigued by an "Agile industrial complex," calls for a return to the Agile Manifesto and its twelve principles, explores interdisciplinary insights from psychology and the Medici Effect, and outlines internal and external policies for a more balanced Agile practice.
The article distills nine key learning principles from Shing‑Tong Yau’s experiences, covering academic attitude, foundational skill building, interdisciplinary study, independent thinking, and scholarly communication, offering practical guidance for anyone aiming to excel in mathematics and related fields.
This article explores why innovative mathematical modeling matters, presents multi‑angle thinking, new mathematical tools, interdisciplinary integration, and practical tips such as open mindset, extensive reading, simulation, teamwork, and continuous learning, illustrated with classic examples like Google’s PageRank algorithm.
Ant Group and Renmin University successfully completed their collaborative "Blockchain Legal Teaching and Curriculum System Reform" project, which earned the 2022 Beijing Higher Education Teaching Achievement Award first prize and is being submitted for a national teaching award, highlighting a multidisciplinary talent‑cultivation model for blockchain and the digital economy.
The article argues that while mathematics is essential for modeling, solving real-world problems requires a blend of disciplines—biology, computation, writing, and more—highlighting that interdisciplinary collaboration, rather than any single field, is the true driver of effective solutions.
This article examines common misconceptions about design thinking, explains its true interdisciplinary nature, and shows how integrating design thinking with product, business, and technology teams can drive innovation, improve user experience, and create measurable business outcomes.
The STEAM framework, introduced by Georgetta Yakman in 2011, expands STEM by adding arts and defines five interrelated literacies—scientific, technological, engineering, mathematical, and arts—each contributing to a holistic, interdisciplinary education that enhances students’ overall competence across these domains.
This article outlines the fundamental definition of big data, its acquisition, transmission, usability, common algorithmic issues, security concerns, potential reforms in hardware and software, and the major computational and interdisciplinary challenges that must be addressed.
