Understanding which values dominate over long time scales—and why—could offer powerful insights into societal evolution, existential risks, and even the alignment of artificial intelligence. Currently, research on what might be called "grabby values" (those that spread efficiently due to expansion-conducive traits) is sparse and informal. This idea proposes systematizing this inquiry by synthesizing existing knowledge, refining core hypotheses, and fostering interdisciplinary collaboration.

Bridging the Gap in Value Theory

The concept hinges on the observation that certain values—whether cultural, economic, or ideological—tend to outcompete others over time. For example, universal religions historically spread rapidly due to traits like simplicity and active proselytization, while market economies dominate for their efficiency in resource allocation. Formalizing this into a research framework could help answer questions like: What makes a value "grabby"? How do these values interact with technological or environmental pressures?

Key steps to develop this field might include:

• A literature review linking ideas from cultural evolution, game theory, and historical case studies.
• Workshops to refine definitions and test hypotheses across disciplines (e.g., philosopher-led ethics discussions paired with economist-led modeling).
• Open-access publications to attract researchers and identify actionable insights for AI alignment or risk mitigation.

Why This Matters Now

The rise of AI adds urgency: if artificial agents evolve values dynamically, understanding "grabby" traits could prevent misalignment. Similarly, predicting how values shape global cooperation—or conflict—may help mitigate existential risks. For academics and foundations invested in long-term outcomes, this represents a high-leverage opportunity to seed a new field with explanatory and predictive power.

An MVP could start small—a working paper and pilot workshop—before scaling to empirical studies or simulations. Early collaboration would be critical, ensuring the theory integrates diverse perspectives while remaining rigorously testable.