Ideometrics: Idea Selection and Progress

• Ideometrics is a framework that treats ideas as fundamental units, focusing on their generation, evaluation, prioritisation, implementation, and outcome monitoring.
• It defines a multi-component process (G, E, P, Iₑ, O) to assess and improve how societies select ideas that lead to preferred future states.
• The framework extends to civilisational progress by integrating documentation and transmission, emphasizing the impact of weak links on overall growth.

Ideometrics is a framework that treats ideas—rather than wealth, health, or technology—as the fundamental units of analysis for understanding human and civilisational progress. In its strongest formulation, it is proposed as the foundation of a generalised and potentially testable theory of progress, centered on the full idea life cycle: generation, evaluation, prioritisation, implementation, and subsequent validation through outcomes. On this view, progress is not primarily a static stock of favorable outcomes, but a dynamic selection process under uncertainty, scarcity, and limited human capacity, in which individuals and societies become better at choosing ideas that actually lead to preferred future states (Rudan et al., 29 May 2026).

1. Definition and conceptual basis

The conceptual starting point of ideometrics is the claim that the human brain can be treated as a “sensor of ideas.” Ideas are defined not as abstract mental contents in isolation, but as competing possibilities for purposeful action that imply alternative futures. The brain perceives or generates such possibilities, evaluates them with available information, assigns expected value, prioritises among them under constraints, and acts on them. Action then changes the world, producing new information that feeds back into further idea generation and revision. Ideometrics is therefore the science of this full cycle (Rudan et al., 29 May 2026).

This framing shifts analytical emphasis away from conventional outcome indicators such as GDP, longevity, scientific output, or technological sophistication. Those outcomes are treated as downstream products of a more fundamental process: the quality of idea selection. A society may display strong current outcomes while deteriorating in its ability to select good future actions; conversely, a society with modest present outcomes may be improving in the underlying processes that generate future gains. Progress therefore becomes a property of a dynamic selection mechanism under uncertainty, not merely a description of accumulated goods (Rudan et al., 29 May 2026).

The paper gives an explicit formal definition:

“measurable improvement in the ability of individuals and societies to generate, evaluate, prioritise, and implement ideas - under scarcity of human capacity, energy, time and resources - in a way that increasingly aligns prioritised ideas with those that truly lead to preferred future states, given available information and uncertainty.” (Rudan et al., 29 May 2026)

Several elements are central in that definition. Progress concerns ability rather than achieved stock; it is constrained by scarcity; it unfolds under uncertainty; and it is judged by increasing alignment between selected ideas and those that actually produce better futures. The paper also notes that preferred future states are partly subjective and difficult to specify completely, although survival, health, safety, and financial stability are presented as examples with broad appeal (Rudan et al., 29 May 2026).

2. The idea life cycle and its formal components

The operative structure of ideometrics is organized around five linked components. The first four define the present quality of the decision process; the fifth tests that process against realized outcomes.

$G$, idea generation quality concerns whether the initial pool of candidate ideas is sufficiently comprehensive, diverse, and novel. If the search space is narrow or systematically excludes high-value possibilities, the entire process is damaged at the outset. The paper links this component to CHNRI work and the “4D” idea structure, and suggests operationalisation through saturation of idea generation, semantic diversity, novelty, and possibly AI-assisted large-scale ideation (Rudan et al., 29 May 2026).

$E$, idea evaluation quality, is the core ideometric step. It measures how well the process estimates the perceived future value of candidate ideas using current evidence, expert judgment, weighted criteria, or AI. High $E$ requires criteria that actually capture the relevant dimensions of the decision context, appropriate criterion weighting, and evaluators who are sufficiently expert and numerous to support robust crowdsourcing. The paper explicitly suggests an analogy with principal component analysis: the evaluation criteria should approximate the principal dimensions of contextual variation that determine eventual success (Rudan et al., 29 May 2026).

$P$, idea prioritisation efficiency, measures how faithfully the ideas that score highest in evaluation are actually selected for implementation. This is the point at which politics, corruption, institutional inertia, bias, and misaligned incentives can degrade progress. The paper treats $P$ as the integrity or “decision discipline” of the transition from ranking to action (Rudan et al., 29 May 2026).

These three process elements are grouped as the quality of ideometrics process:

$Q_i = G \times E \times P$

The fourth component is $I_e$, implementation effectiveness. The paper initially introduces a placeholder expression,

$I_e = \frac{I(t)}{S(t_0)}$

where $I(t)$ is the number of selected ideas successfully executed by future time $t$, and $E$0 is the set of ideas prioritised at initial time $E$1. It immediately notes, however, that this is too crude, because implementation quality also depends on fidelity, timeliness, cost control, and whether implementation actually moved reality toward preferred states (Rudan et al., 29 May 2026).

The fifth component is $E$2, outcome monitoring, which measures realized future value after implementation:

$E$3

Here $E$4 is the realized future value of implemented idea $E$5 at time $E$6. This distinction between process quality and realized value is fundamental. A society may score highly on generation, evaluation, prioritisation, and implementation, yet still fail if its evaluations were systematically wrong. Ideometrics therefore distinguishes the quality of the machinery from the truth of its forecasts (Rudan et al., 29 May 2026).

3. The Ideometric Index of Human Progress

The paper formalizes the present quality of the idea-selection process through the Ideometric Index of Human Progress (IIHP):

$E$7

The multiplicative form is important. It implies that progress is a chain with weak links: very poor generation, evaluation, prioritisation, or implementation can collapse the whole process. The paper states that if

$E$8

then rising IIHP acts as an enabling mechanism for future human progress, provided that evaluation is in fact identifying high-value ideas (Rudan et al., 29 May 2026).

The paper then defines human progress more directly in terms of idea selection. Let $E$9 be the set of all possible ideas, $E$0 the subset prioritised in the present, $E$1 the true future value of idea $E$2, and $E$3 the perceived future value assigned in the present. Human progress at future time $E$4 is written as:

$E$5

This means that progress occurs when the ideas actually selected have a higher average true future value than the average idea in the full possibility set. If the prioritised subset performs worse than the baseline pool, the society is in regress, even if some absolute outcomes remain positive (Rudan et al., 29 May 2026).

A more decision-theoretic expression models an idea as a mapping from states of the world to outcomes,

$E$6

with true value

$E$7

for true world state $E$8, and perceived value

$E$9

for a probability distribution $P$0 over possible states $P$1. The paper gives a sufficient condition for progress as improving alignment between perceived and true value:

$P$2

In prose, progress occurs when the error in estimating the future value of ideas shrinks over time. This alignment between perceived future value and true realized future value is the conceptual center of the framework. Science, markets, democracy, and AI contribute to progress insofar as they improve this alignment; misinformation, bias, ideology, and corrupted institutions degrade it (Rudan et al., 29 May 2026).

4. Civilisational extension: documentation, transmission, and long-run accumulation

The paper extends the same logic from individuals and societies to long periods of history through the Ideometric Index of Civilisational Progress (IICP). A civilization is treated as a large-scale idea-selection system, but long-run accumulation depends on more than generating, evaluating, prioritising, implementing, and observing ideas. It also depends on whether societies document what they learn and transmit that knowledge across generations (Rudan et al., 29 May 2026).

The resulting expression is:

$P$3

where $P$4 measures successful documentation and preservation of valuable and harmful ideas alike, and $P$5 measures successful intergenerational transmission through education and institutions. The transformed outcome term $P$6 is introduced to fit the integrated civilisational index (Rudan et al., 29 May 2026).

Documentation matters because civilizations forget both successes and disasters if they are not recorded. Transmission matters because preserved knowledge that is not taught effectively is functionally lost. These additions allow the framework to explain both civilisational accumulation and civilisational fragility. The paper explicitly argues that if any one of $P$7 is near zero, long-run progress is severely constrained; this is why IICP is said to reveal routes to stagnation, regress, and collapse (Rudan et al., 29 May 2026).

The paper mentions the Scientific Revolution, digital storage, and modern education as historical developments that likely raised, respectively, $P$8 and $P$9, $P$0, and $P$1. It also reframes historical change as a long-running experiment in idea selection, retention, and transmission: civilizations prosper when they improve at discovering and acting on valuable ideas while preserving and teaching what they have learned (Rudan et al., 29 May 2026).

5. Operationalization and empirical agenda

A central claim of the framework is that ideometrics must not remain a philosophical metaphor. The paper repeatedly insists on approximate empirical operationalization, even if early implementations are partial (Rudan et al., 29 May 2026).

For $P$2, the proposed measures include saturation curves in idea elicitation, semantic distance, novelty metrics, and AI-generated idea sets. For $P$3, the paper suggests retrospective validation by comparing predicted rankings with later outcomes, including research impact, stock performance, or start-up success. It also proposes improving criteria through contextual analysis or PCA-like methods (Rudan et al., 29 May 2026).

For $P$4, the suggested measure is the degree to which implemented ideas track earlier rankings, together with analysis of whether deviations are explained by uncertainty or by political and institutional distortion. For $P$5, the paper points to implementation-monitoring tools such as PLANET, and to metrics involving delays, fidelity, cost overruns, and execution quality. For $P$6, proposed testing domains include stock portfolios, venture investments, research-grant portfolios, public-policy decisions, and personal life choices (Rudan et al., 29 May 2026).

The framework is also positioned relative to economics and history. In economics it is described as closest to endogenous growth theory and expected utility/decision theory, especially the view that ideas drive growth. The claimed difference is that ideometrics goes further upstream, by studying how ideas are generated and selected before they become economically productive, and further downstream, by checking whether selected ideas actually produce realized value. More broadly, the framework is presented as an attempt to unify economics, decision theory, evolutionary theory, information theory, philosophy of science, cybernetics, predictive processing, and collective intelligence (Rudan et al., 29 May 2026).

The paper’s favored examples are stock markets, start-up investing, research-grant selection, and CHNRI exercises in health research, because these domains combine many candidate ideas, scarce resources, explicit criteria, and delayed outcomes. It also proposes direct comparison between human expert crowdsourcing and AI systems as alternative idea-evaluation engines (Rudan et al., 29 May 2026).

6. Extensions and broader measurement-oriented usage

The most expansive extension applies ideometrics to consciousness, time, space, dreams, evolution, decision-making, and AI. In that account, consciousness is presented as a system that internally simulates alternative futures and evaluates them through three core criteria—attractiveness, feasibility, and potential impact—before acting toward preferred states. The paper suggests that consciousness may reduce the informational entropy of many possible futures through ideometric processes, while AI may already compute feasibility and potential impact without subjective experience (Rudan, 30 May 2026).

A broader measurement-oriented usage also appears in computational linguistics and political methodology. In ideological measurement, one paper treats a LLM not as a measurement device but as **“a single, fall