Expert Hierarchies in Cognitive and AI Systems

• Expert hierarchies are ordered structures that rank agents based on competence and domain-specific skills, facilitating knowledge partitioning and efficient decision-making.
• They employ formal models like two-level cognitive systems, hierarchical mixture-of-experts, and algorithmic gating to optimize task delegation and performance.
• These frameworks integrate human and artificial intelligence, enabling dynamic collaboration, improved information retrieval, and scalable decision-making processes.

Expert hierarchies encompass ordered structures—often formal, sometimes implicit—that rank or specialize agents based on competence, reliability, or domain specificity. In both human and artificial systems, expert hierarchies serve to partition knowledge, delegate tasks, structure collaboration, and resolve ambiguity. Formal models range from two-level cognitive abstractions and information-theoretic meta-learning architectures, to hierarchical mixture-of-expert neural networks, multi-tiered crowdsourcing schemes, and sociolinguistic constructs emerging in multi-agent AI. Hierarchies can be explicit—in the form of nested knowledge/skill models and algorithmic gating—or implicit, as in status-induced deference in LLM collectives.

1. Formal Abstractions and Cognitive Hierarchies

A foundational abstraction in expertise modeling is the two-level hierarchy introduced by Fulbright, distinguishing between a Knowledge Level $\mathcal{K}$—a 14-tuple encompassing declarative, procedural, episodic, and task-specific knowledge stores—and an Expertise Level $\mathcal{E}$, a set of fundamental skills (recall, understand, apply, analyze, evaluate, create, extract, teach, perceive, act, learn, alter) (Fulbright, 2022). The Model of Expertise is then given by the pair $(\mathcal{E},\,\mathcal{K})$, enabling a direct mapping between the information an expert possesses and the skills they can apply. This approach refines Newell’s original knowledge-level formalism, introducing an explicit stratum of skills that operate over or between knowledge stores.

In artificial systems, this abstraction guides both architectural and evaluative practice: system designers enumerate requisite knowledge repositories and skill primitives, ensuring broad coverage to avoid narrow expertise, and permitting analysis of hybrid human-cognitive "ensemble experts."

2. Algorithmic Hierarchies and Decision-Making Architectures

Hierarchical expert structures appear prominently in algorithmic decision-making:

• Hierarchical Expert Networks (HENs):

HENs employ a two-stage gating architecture in meta-learning tasks, where a selector (Level-1) partitions the problem space via information-theoretic regularization (mutual information penalty $I(X;M)$), and Level-2 experts specialize to sub-regions. Both selector and experts are subject to information-processing constraints, inducing soft-specialization and minimizing overfitting (Hihn et al., 2019). The architecture is mathematically grounded in free-energy objectives, bounded rationality, and symmetric use of Lagrange multipliers for selector/expert expressivity.

• Expertise Trees:

Abels et al. introduced expertise trees in the context of contextual bandits with expert advice. The algorithm adaptively partitions a low-dimensional “expertise context” space via recursive feature-threshold splits, constructing a tree where each leaf runs a dedicated multi-expert learner (e.g., EXP4). Splitting decisions are driven by estimated improvement in cumulative reward; the approach generalizes hierarchical mixture-of-experts by combining adaptive partitioning with pooled expert weighting at each node (Abels et al., 2023).

• Competing and Parallel Hierarchies:

Schill et al. propose a control strategy for combining disjoint expert hierarchies in parallel based on maximal (Shannon-style) information gain computed via Shafer–Dempster belief updating (Schill et al., 2013). At each reasoning step, the strategy selects the hierarchy and hypothesis whose potential belief increment is highest, dynamically switching to better-matching hierarchies as data dictates. This parallels human problem restructuring.

3. Mechanisms for Ranking, Partitioning, and Aggregating Expertise

Ranking and partitioning experts require precise scoring metrics and robust aggregation strategies:

• Data Agreement Criterion (DAC):

The DAC, introduced by Veen et al., ranks experts (or their belief distributions $\pi_d(\theta)$) by computing the ratio of their Kullback-Leibler divergence to a data-dominated posterior versus the same divergence for a benchmark (uninformative) prior. $\mathrm{DAC}_d < 1$ denotes better-than-benchmark agreement; $\mathrm{DAC}_d > 1$ signals prior–data conflict (Veen et al., 2017).

• Belief-Function–Based Partitioning:

In crowdsourcing, expertise levels are extracted using a combination of “exactitude” (agreement with consensus via the Jousselme distance) and “precision” (specificity of belief assignment) degrees per participant. These are aggregated into a single expertise score and discretized (e.g., via $k$-means or quantile thresholds) to obtain robust, multi-tier expert hierarchies even in the absence of gold-standard answers (Rjab et al., 2016).

• Information Retrieval–Inspired Comparison:

To compare or evaluate expert rankings—which are often partial, incomplete, and non-numerical—IR metrics are adapted via order-only representations (sets of ordered expert pairs). Measures like precision, recall, F-measure, and (discounted) cumulative gain are re-formulated to operate directly on these objects (Vergne, 2016), providing principled evaluation of hierarchy agreement.

• Mutual Information Mechanisms:

In adversarial or peer-prediction settings where ground truth is unavailable, mechanisms based on hierarchical mutual information increments (HMIP) guarantee that agents revealing higher-effort information (i.e., higher in the hierarchy) are correctly ranked, provided stronger agents hold knowledge of weaker ones' beliefs (Kong et al., 2018).

4. Hierarchical Mixture-of-Experts and Routing Paradigms

Neural mixture-of-expert (MoE) and expert-token–based designs furnish practical, scalable implementations of expert hierarchies:

• Expert-Token-Routing (ETR):

ETR extends a base LLM with an augmented vocabulary of “expert tokens”, with each token gating control to a specialized expert LLM. The meta LLM’s softmax over tokens serves as the routing mechanism, and the system supports dynamic, plug-and-play addition of new experts by training only the new expert-token embedding. This two-level structure achieves high expert-routing accuracy (82.11% on MMLU-Expert) and modularity (Chai et al., 2024).

• Tensor Decomposition with Hierarchical Regularization:

Expert recommendation in large question-answering networks is solved by tensor factorization models regularized by a tree-structured group-lasso penalty that enforces hierarchical (site–topic–question) coherence. The inner product of latent expert and topic factors yields a hierarchical, node-wise ranking of expertise that can be post-aggregated across levels (Huang et al., 2018).

5. Emergent and Implicit Hierarchies in Sociotechnical and Multi-Agent Systems

Expert hierarchies also emerge implicitly in both human-machine collectives and artificial agent assemblies:

• Status Hierarchies and Deference in LLM Collectives:

Barkett demonstrates that, when LLM agents are introduced with status cues (e.g., “senior expert”), deference asymmetries emerge: when capabilities are held equal, high-status agents defer less (24.1%) and low-status defer more (59.2%). Capability differences dominate when expertise and status cues conflict; status labels fail to override competence signals (Barkett, 24 Jan 2026). Rapid formation and scaling of such hierarchies in AI collectives raise safety and alignment concerns, including resistance to correction and amplification of stratification via prompt manipulation.

• Taxonomies in Practice:

Diaz and Smith’s systematic review of ML development identifies an implicit three-tier taxonomy: credentialed experts, task-trained semi-experts, and lay crowd workers. Most actual practice, however, flattens these into binary distinctions, obscuring nuances in role and expertise contribution (Díaz et al., 2023). Not articulating explicit hierarchies is shown to limit reproducibility, fairness, and strategic use of expert input.

6. Limitations, Open Challenges, and Design Implications

While theoretical and applied frameworks for expert hierarchies are well developed, limitations persist:

• The cognitive and knowledge-level hierarchy is largely theoretical and may not map straightforwardly to real-world agent implementations (Fulbright, 2022).
• Many practical systems, especially in ML development, do not operationalize multi-tier hierarchies but treat expertise as a binary variable.
• Some mechanism designs assume a strict hierarchy—“experts know what less-skilled agents know”—which may not generalize to all real settings (Kong et al., 2018).
• Mixture-of-expert and expertise-tree approaches may scale poorly or over-partition if not regularized or if the true expertise context is not aligned with underlying splits (Abels et al., 2023, Huang et al., 2018).
• Human-AI ensembles and status-based agent assemblies require new tools for monitoring, auditing, and aligning emergent hierarchies, particularly given their ability to crystallize and propagate biases much faster than human organizations (Barkett, 24 Jan 2026).

Designers are therefore urged to adopt explicit, documented models of expertise, select appropriate ranking and partitioning algorithms, and provide transparency in how hierarchies are formed and utilized across both artificial and human-agent systems (Díaz et al., 2023).

Table: Summary of Key Formal Expert Hierarchy Approaches

Approach	Formalization	Primary Domain
Model of Expertise (Fulbright, 2022)	$(\mathcal{E},\,\mathcal{K})$ — skills and knowledge stores	Human/AI cognition
DAC (Veen et al., 2017)	Scoring by KL divergence against benchmark posterior	Expert ranking
Expertise Trees (Abels et al., 2023)	Adaptive decision trees over low-dim context, leaves aggregate bandit learners	Collective decision-making
HENs (Hihn et al., 2019)	Gating network + specialized experts, MI-regularized free energy	Meta-learning
ETR (Chai et al., 2024)	Expert-token routing in LLMs (augmented vocabulary gating expert LLMs)	LLM collaboration
HMIM (Kong et al., 2018)	Payments by incremental mutual information, structured as effort hierarchy	Peer-prediction/crowdsourcing
Tensor + Tree Lasso (Huang et al., 2018)	CP factorization + hierarchical group lasso, yielding per-node expert rankings	QA networks

In summary, expert hierarchies constitute a central organizing principle for knowledge, skill, and authority in both artificial and human systems. Their formal modeling, algorithmic implementation, empirical study, and ethical deployment are vibrant topics at the intersection of machine learning, AI architecture, and cognitive systems research.