Metaproductivity–Performance Mismatch (MPM)

• MPM is the divergence between observable performance metrics and an agent’s true potential for long-term generative productivity.
• Formal models like the Dilbert–Peter framework and CMP metric quantify how self-promotion inflates perceived performance over actual output.
• Mitigation strategies include realigning evaluation metrics and using clade-based approaches to prioritize sustainable, long-term effectiveness.

Metaproductivity–Performance Mismatch (MPM) refers to the divergence between observable or immediately measured performance and a system’s, agent’s, or employee’s long-term potential for generative improvement or enhanced productivity. Across hierarchical organizations, software engineering teams, and self-improving coding agents, MPM poses substantial challenges to accurate evaluation, effective promotion or selection, and sustainable operational effectiveness.

1. Conceptualization of MPM

The Metaproductivity–Performance Mismatch arises wherever the metrics used to evaluate agents or systems are decoupled from their true value or future utility. In organizational contexts, perceived performance may be inflated by self-promotion or superficial indicators, while real output diminishes. In AI and coding agent development, benchmark scores may fail to predict the metaproductivity of the agent’s lineage or future self-improving trajectory (Sobkowicz, 2010, Wang et al., 24 Oct 2025, Lee et al., 2023).

Key dimensions include:

• Actual Productivity (work output per agent or system)
• Perceived Performance (dependent on visible efforts, self-promotion, or immediate metric scores)
• Metaproductivity (potential for long-term generative improvement, e.g., descendants’ or derivatives’ benchmark success)

MPM quantifies the selective pressure toward traits that enhance evaluation scores without necessarily increasing underlying productivity or generative capacity, thereby leading to organizational or systemic inefficiencies.

2. Formal Models and Quantitative Metrics

Organizational Simulation: Dilbert–Peter Model

The Dilbert–Peter model represents a hierarchical organization with $K$ levels, each node supervising $N$ subordinates. Each agent $i$ is characterized by:

• Raw productivity $w_i$
• Self-promotion parameter $p_i$

Effective productivity is reduced by self-promotion:

$w'_i = w_i - p_i$

Managerial cumulative output is multiplicative:

$$W_i = w'_i \times \left( \sum_{j \in SUB(i)} W_j \right)$$

Perceived performance incorporates susceptibility to self-promotion ($C$):

$U_i = \frac{W_i}{\overline{W}(k)} + C p_i$

Promotion decisions favor high $U_i$, often rewarding political visibility over true output. The MPM here results from the inflation of perceived performance ($U_i$) via self-promotion, even as effective productivity ($W_i$) declines, especially as $C$ increases (Sobkowicz, 2010).

Coding Agent Self-Improvement: CMP Metric and HGM

For coding agents, MPM is defined as the gap between immediate coding benchmark performance (utility $U$) and long-term self-improvement potential aggregated over descendants (metaproductivity). The Clade–Metaproductivity (CMP) metric captures this:

$$CMP_\pi(\mathcal{T}, a) = E_{\mathcal{T}^B \sim p_\pi(\cdot\,|\,\mathcal{T}, a)} \left[ \max_{a' \in C(\mathcal{T}^B, a)} U(a') \right]$$

Empirically, CMP can be estimated as:

$$\widehat{CMP}(a) = \frac{n_{success}^c(a)}{n_{success}^c(a) + n_{failure}^c(a)}$$

where aggregation is over the entire agent clade (Wang et al., 24 Oct 2025).

3. Manifestations of MPM in Real-World Systems

Organizational Promotion and the Peter Principle

• Promotions are often decided on perceived performance ($U_i$) rather than true productivity ($W_i$).
• High self-promotion ($p_i$) can compensate for poor effective output, resulting in the promotion of candidates with diminished actual productivity.
• Under the “Peter hypothesis,” newly promoted agents’ productivity is randomly redrawn, often leading to accelerated organizational inefficiency when selection is based on appearance rather than skill (Sobkowicz, 2010).

Software Engineering Metrics: The Three Layer Productivity Framework

• Production Metrics: Measure raw output, e.g., code commits or pull requests ($O$).
• Productivity Metrics: Normalize output by resource consumed ($R$), e.g., $P = O/R$.
• Performance Metrics: Assess qualitative factors ($Q$), e.g., code quality, maintainability.

Misalignment occurs when organizations rely predominately on production metrics, which do not reflect actual productivity or long-term performance, embodying MPM. Practitioners prefer performance and productivity metrics for accurate assessment, while organizations continue to overemphasize easily quantified production (Lee et al., 2023).

Metric Type	Raw Variable	Key Limitation for MPM
Production	$O$	Ignores resource/context; easy to game
Productivity	$O/R$	May exclude qualitative output
Performance	$Q$	Harder to measure, requires context

4. Mitigation Strategies and Corrective Frameworks

Objective Promotion and Metric Design

• Reducing susceptibility ($C$) to self-promotion in organizational promotion algorithms limits MPM by weighting actual productivity ($W_i$) over visibility ($p_i$) (Sobkowicz, 2010).
• Using the continuity model for promotions preserves actual skills, moderating declines in effectiveness relative to the Peter hypothesis.

Clade-Based Evaluation in Self-Improving Systems

• CMP-based selection policies focus on the generative capacity of agents—in effect, selecting for long-term self-improvement rather than short-term benchmark scores.
• The Huxley–Gödel Machine (HGM) framework applies this principle by using Thompson Sampling on Beta-distributed success/failure clade statistics, prioritizing agent lineages that demonstrate aggregated improvement rather than isolated metric spikes.
• Empirical results confirm increased accuracy and reduced resource consumption when deploying CMP over immediate utility as the selection driver (Wang et al., 24 Oct 2025).

Realignment of Software Engineering Dashboard Metrics

• Organizations should rebalance metric dashboards to emphasize performance and productivity metrics, reducing the weight of raw production counts.
• A composite metric can be formulated:

$$M_{total} = \alpha\, Production + \beta\, Productivity + \gamma\, Performance$$

Where $\gamma$ is set highest, $\alpha$ lowest, and all terms are recalibrated in ongoing feedback loops to sustain alignment with long-term effectiveness (Lee et al., 2023).

5. Consequences of Unchecked MPM

• Organizational Inefficiency: Persistent reliance on perceived performance and self-promotion over productivity results in the elevation of less competent managers, reduction of aggregate output, and confirmation of the Peter Principle (Sobkowicz, 2010).
• Reduced Team Cohesion and Agency: Developers and contributors feel misrepresented when their key metrics are not reflected, risking morale and retention (Lee et al., 2023).
• Benchmark Chasing in AI Agents: In coding agent self-improvement, selection on immediate utility yields lineages that do not generalize, while CMP-based approaches foster agents with human-level or superior performance at lower resource cost (Wang et al., 24 Oct 2025).

6. Research Directions and Broader Implications

• Further refinement of clade or lineage-based evaluation metrics is indicated. Directions include integrating nuanced measures of descendant quality and extending CMP principles to other recursive self-improving systems (Wang et al., 24 Oct 2025).
• Implementation strategies such as decoupling expansion and evaluation, as in asynchronous HGM, allow for scalability and efficiency in dynamic environments.
• The Three Layer Productivity Framework, embodying separation of production, productivity, and performance, provides a diagnostic and prescriptive tool for addressing MPM in engineering organizations (Lee et al., 2023).

A plausible implication is that approaches which integrate long-term generative measures and continuously recalibrate metric weightings are best positioned to correct the Metaproductivity–Performance Mismatch and sustain robust organizational or agent-level performance over time.