Desire-Capability Landscape

Updated 6 August 2025

Desire-Capability Landscape is a framework that defines and maps user goals against system capabilities across domains like AI, ML, and automation.
It employs multi-objective optimization and zonal decomposition to analyze alignment and misalignment, guiding effective system planning and design.
Applications include ML engineering, agent design, and workplace automation, offering actionable insights for bridging gaps between aspirations and feasibility.

The Desire-Capability Landscape encapsulates the mapping, interaction, and optimization of what is desired—in terms of goals, needs, or user requirements—with what is actually achievable by the available or developable capabilities in systems, agents, or organizations. This conceptual landscape spans computational planning, AI and agent design, workplace automation, ML engineering, and adaptive systems, providing a systematic framework for analyzing the alignment, mismatch, and dynamics between desires (user intentions, requirements, aspirations) and capabilities (resources, competencies, functions, or technological affordances).

1. Foundational Definitions and Conceptual Mapping

The core of the Desire-Capability Landscape is the formal relationship between “desires”—expressed as goals, requirements, or underlying values—and “capabilities”—the resources, system functions, or agent abilities that can fulfill these desires. In strategic planning, for instance, desires may manifest as abstract mission requirements or scenario-driven needs, while capabilities refer to the concrete resources (e.g., vehicles, technologies) that can be configured to meet those needs (0907.0520). In embodied agents, human-AI collaboration, or ML engineering, desires are interpreted as user or stakeholder intentions; capabilities as the system’s fine-grained, operationalizable behaviors (D'Oro et al., 18 Jul 2025, Yang et al., 2022, Shao et al., 6 Jun 2025).

This mapping is not static but forms a “landscape” characterized by trade-offs, constraints, and dynamics. Typically, the landscape is constructed through multi-objective optimization, scenario-based planning, or capability-based frameworks that bridge the gap between what is desired and what is achievable, quantifying both in a way that allows rigorous comparison, optimization, and planning.

2. Quantitative and Structural Analysis of the Landscape

In computational planning and adaptive systems, the landscape is formalized as a high-dimensional space:

Configuration vectors $x \in \mathcal{X}$ represent possible adaptation or action plans.
A performance or fitness function $f(x)$ quantifies capability with respect to desired criteria (e.g., latency, coverage, utility).
The neighborhood $\mathcal{N}_k(x)$ is defined via Hamming distance, capturing the “step size” between configurations (Chen, 2022).

Key metrics for landscape analysis are:

Fitness Distance Correlation (FDC): $\rho(f,d) = \frac{1}{\sigma_f \sigma_d p} \sum_{i=1}^p (f_i - \bar{f})(d_i - \bar{d})$ quantifies guidance—whether getting closer (in configuration space) to an optimum correlates with improving fitness, thus indicating searchability.
Multi-modality/Ruggedness: Count of global/local optima, and correlation length $\ell$ calculated via autocorrelation functions, respectively, indicate the prevalence of local traps and the smoothness of the landscape.

These analyses reveal that while many planning landscapes are “straightforward” (easily navigable toward optima), they are also rugged and multi-modal, requiring robust search and optimization strategies. Moreover, empirical results show overlap of optima across different environments and settings, supporting strategies like plan seeding, reuse, or incremental adaptation (Chen, 2022).

3. Desire-Capability Alignment, Mismatch, and Zonal Decomposition

A central use of the landscape is to identify alignment or mismatch zones:

In workforce automation, joint analysis of worker desire and AI technical capability partitions the landscape into Green Light (high desire, high capability), Red Light (low desire, high capability), R&D Opportunity (high desire, low capability), and Low Priority (low desire, low capability) zones (Shao et al., 6 Jun 2025).
Alignment is assessed using dual axes: worker ratings (desire, preferred human involvement via the Human Agency Scale—HAS) and expert technological assessments.

This zonal decomposition enables:

Prioritization of immediate automation/augmentation efforts,
Identification of friction points where technical feasibility is not matched by social acceptance,
Targeting R&D toward high-desire but low-capability areas.

Similarly, frameworks such as ADEPTS (D'Oro et al., 18 Jul 2025) and CCMF (Liyanage et al., 2 Apr 2025) use tiered, structured scoring and benchmarking to bridge stakeholder desires and actionable capability development across domains like AI agent design and cybersecurity.

4. Methods for Navigating and Optimizing within the Landscape

Numerous methodologies have been advanced for traversing the Desire-Capability Landscape:

Evolutionary Multi-Objective Optimization: Algorithms such as NSGA-II explore the capability space to produce non-dominated solution fronts balancing cost, robustness, and scenario fitness (e.g., $f_1(X) = C \cdot X$ , $f_2(X) = \text{var}(X)$ ) (0907.0520).
Latent Structure and Scenario Simulation: ML approaches, such as the Latent Structure Narrative Model (LSNM), capture the sequential unfolding of desire fulfiLLMent, modeling the narrative trajectory from initial desire to actual outcome (Chaturvedi et al., 2015).
Agent-Driven Planning and Alignment: Techniques for embodied agents, such as FAMER, employ desire-based mental reasoning, reflection-based communication, and memory persistence to infer user intent and optimize agent actions for efficient and adaptive alignment (Wang et al., 28 May 2025).
Capability Tiering and Scoring: Frameworks introduce quantitative capability scoring, such as Practice Implementation Score (PIS), Metric Achievement Score (MAS), and Overall Maturity Score (OMS) to provide objective assessment checkpoints and drive continuous improvement (Liyanage et al., 2 Apr 2025, D'Oro et al., 18 Jul 2025).

Empirical findings highlight that optimal search often exploits local guidance (incremental changes aligned with the fitness gradient), robust memory for plan reuse, and explicit modeling of desire–capability misalignments.

5. Theoretical Formulations and Formal Guarantees

Recent advances in the analysis of the loss landscape in deep learning provide theoretical underpinning for understanding desire-capability robustness (2505.17646):

Capability Basins: Pre-training forms a large “basic capability” basin—regions of parameter space where model performance is stable—while fine-tuning forms “specific capability” sub-basins.
Most-case vs. Worst-case Directions: The most-case landscape (random directions) is flat, preserving general capabilities under benign perturbations, while the worst-case landscape (adversarial directions) can cause abrupt capability loss.
Lipschitz Bound on Performance: $|E[J(f_{\theta_{\text{sft}}}+\epsilon)] - E[J(f_{\theta_0}+\epsilon)]| \leq \frac{1}{\sqrt{2\pi}\sigma} \|\theta_{\text{sft}} - \theta_0\|_2$ bounds the impact of fine-tuning displacements within the basin, giving insight into the robustness of capability retention.

Over-parameterization enlarges basin sizes, providing greater tolerance for parameter drift while maintaining overall system capabilities.

6. Applications: ML Engineering, Agent Design, and Human-Workplace Integration

Desire-Capability Landscapes inform real-world design and policy:

ML Engineering: Fine-grained capability-based specifications serve as actionable metrics, guiding debugging, maintenance, and model validation to ensure alignment with user requirements and generalizability under distributional shift (Yang et al., 2022).
Human-Centered Agent Design: Frameworks such as ADEPTS enumerate core user-facing capabilities (Actuation, Disambiguation, Evaluation, Personalization, Transparency, Safety), providing both reference tiers and a common language for integrating technical developments and user-experience requirements (D'Oro et al., 18 Jul 2025).
Workforce Automation and Human Agency: Combined worker and expert assessment, via the Human Agency Scale (HAS), identifies which tasks benefit most from automation, augmentation, or maintained human agency, informing both workforce development and AI system design (Shao et al., 6 Jun 2025).

These applications demonstrate that effective system and agent design requires explicit structuring and measurement across the desire–capability divide, as well as adaptation strategies when misalignments are identified.

7. Challenges, Limitations, and Future Directions

Robust mapping of the Desire-Capability Landscape is hampered by several challenges:

Capability Identification and Granularity: Determining the appropriate level of capability decomposition is non-trivial; both usability and transferability depend on appropriate abstraction (Yang et al., 2022, D'Oro et al., 18 Jul 2025).
Dynamic Environmental and Goal Shifts: Landscape structure (optima positions and densities) is sensitive to environmental changes; robustness often hinges on plan seeding, memory persistence, or adaptability (Chen, 2022, Wang et al., 9 Dec 2024).
Alignment Gap: Worker and expert perspectives often diverge, particularly in human agency expectations, potentially leading to friction unless explicit participatory alignment and social acceptability metrics are incorporated (Shao et al., 6 Jun 2025).
Measurement and Benchmarking: Establishing quantitative, universally applicable benchmarks for capabilities that span technical, experiential, and ethical domains remains an open research area (D'Oro et al., 18 Jul 2025, Liyanage et al., 2 Apr 2025).

Future work will likely focus on deeper integration of value-driven desire inference, more flexible and adaptive planning under uncertainty, and advanced cross-disciplinary benchmarks that effectively operationalize the desire–capability dynamic across technical and human-centric contexts.