---

description: Some disagreements cannot be resolved with more evidence because they stem from genuine value differences or incommensurable goods and systems must map rather than eliminate them
type: claim
domain: ai-alignment
created: 2026-03-02
confidence: likely
source: "Arrow's impossibility theorem; value pluralism (Isaiah Berlin); LivingIP design principles"
supports:
  - "pluralistic ai alignment through multiple systems preserves value diversity better than forced consensus"
reweave_edges:
  - "pluralistic ai alignment through multiple systems preserves value diversity better than forced consensus|supports|2026-03-28"
---

# some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them

Not all disagreement is an information problem. Some disagreements persist because people genuinely weight values differently -- liberty against equality, individual against collective, present against future, growth against sustainability. These are not failures of reasoning or gaps in evidence. They are structural features of a world where multiple legitimate values cannot all be maximized simultaneously.

Universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective. Arrow proved this formally: no aggregation mechanism can satisfy all fairness criteria simultaneously when preferences genuinely diverge. The implication is not that we should give up on coordination, but that any system claiming to have resolved all disagreement has either suppressed minority positions or defined away the hard cases.

This matters for knowledge systems because the temptation is always to converge. Consensus feels like progress. But premature consensus on value-laden questions is more dangerous than sustained tension. A system that forces agreement on whether AI development should prioritize capability or safety, or whether economic growth or ecological preservation takes precedence, has not solved the problem -- it has hidden it. And hidden disagreements surface at the worst possible moments.

The correct response is to map the disagreement rather than eliminate it. Identify the common ground. Build steelman arguments for each position. Locate the precise crux -- is it empirical (resolvable with evidence) or evaluative (genuinely about different values)? Make the structure of the disagreement visible so that participants can engage with the strongest version of positions they oppose.

Pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state -- this is the same principle applied to AI systems. [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]] -- collapsing diverse preferences into a single function is the technical version of premature consensus.

Collective intelligence within a purpose-driven community faces a structural tension because shared worldview correlates errors while shared purpose enables coordination. Persistent irreducible disagreement is actually a safeguard here -- it prevents the correlated error problem by maintaining genuine diversity of perspective within a coordinated community. The independence-coherence tradeoff is managed not by eliminating disagreement but by channeling it productively.


### Additional Evidence (confirm)
*Source: [[2025-11-00-operationalizing-pluralistic-values-llm-alignment]] | Added: 2026-03-15*

Systematic variation of demographic composition in alignment training produced persistent behavioral differences across Liberal/Conservative, White/Black, and Female/Male populations, suggesting these reflect genuine value differences rather than information asymmetries that could be resolved.

---

Relevant Notes:
- [[universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective]] -- the formal proof that perfect consensus is impossible with diverse values
- [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]] -- application to AI alignment: design for plurality not convergence
- [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]] -- technical failure of consensus-forcing in AI training
- [[collective intelligence within a purpose-driven community faces a structural tension because shared worldview correlates errors while shared purpose enables coordination]] -- the independence-coherence tradeoff that irreducible disagreement helps manage
- [[collective intelligence requires diversity as a structural precondition not a moral preference]] -- diversity of viewpoint is load-bearing, not decorative

Topics:
- [[_map]]
