AI Product Value Assessment Model: An Interdisciplinary Integration Based on Information Theory, Economics, and Psychology
It addresses the issue of blind investments by enterprises in AI development, offering a tool for rational value assessment, though it appears incremental as it builds on existing interdisciplinary theories.
This paper tackles the problem of irrational investment in AI products by developing a multi-dimensional evaluation model that integrates information theory, economics, and psychology to quantify value, validated through 10 commercial cases to distinguish successful and failed products.
In recent years, breakthroughs in artificial intelligence (AI) technology have triggered global industrial transformations, with applications permeating various fields such as finance, healthcare, education, and manufacturing. However, this rapid iteration is accompanied by irrational development, where enterprises blindly invest due to technology hype, often overlooking systematic value assessments. This paper develops a multi-dimensional evaluation model that integrates information theory's entropy reduction principle, economics' bounded rationality framework, and psychology's irrational decision theories to quantify AI product value. Key factors include positive dimensions (e.g., uncertainty elimination, efficiency gains, cost savings, decision quality improvement) and negative risks (e.g., error probability, impact, and correction costs). A non-linear formula captures factor couplings, and validation through 10 commercial cases demonstrates the model's effectiveness in distinguishing successful and failed products, supporting hypotheses on synergistic positive effects, non-linear negative impacts, and interactive regulations. Results reveal value generation logic, offering enterprises tools to avoid blind investments and promote rational AI industry development. Future directions include adaptive weights, dynamic mechanisms, and extensions to emerging AI technologies like generative models.