ROMA: Role Optimization & Motivation Alignment

• The paper demonstrates that aligning programming roles with individual Big Five traits can boost intrinsic motivation by up to 65% in controlled studies.
• ROMA is a framework that integrates Self-Determination Theory with personality assessments to tailor role assignments in software teams.
• ROMA employs an adaptive, heuristic process with periodic reassessments and statistical validation to optimize team dynamics and motivation.

Role Optimization Motivation Alignment (ROMA) is a personality-driven, self-determination-based framework for optimizing programming role assignments in both human-human and human-AI collaboration, with the stated aim of improving intrinsic motivation, self-determination need satisfaction, and team dynamics by aligning the division of programming work with individual psychological profiles (Valovy, 1 Nov 2025). In earlier distributed pair-programming work, ROMA appears as a developing framework that aligns human/AI programming roles with individual Big Five personality traits in order to optimize individual motivation and team productivity in Very Small Entities and undergraduate courses (Valovy et al., 2024). Across these formulations, ROMA is presented as a framework and applied decision process rather than as a fully specified optimization algorithm.

1. Emergence and scope

ROMA emerged within behavioral software engineering as a response to the claim that programming-role allocation is usually handled ad hoc, often by availability, technical seniority, or informal intuition, while giving insufficient weight to how personality traits shape role preferences and motivational outcomes (Valovy, 1 Nov 2025). Its substantive scope is software construction and programming work rather than the entire software lifecycle, and its primary organizational targets are Very Small Entities, SOHOs, and undergraduate software teams. In the earlier operational formulation, the intended domains were Very Small Entities, undergraduate or academic programming settings, distributed pair-programming environments, and potentially broader human/AI collaboration contexts (Valovy et al., 2024).

ROMA is best understood as part of a broader lineage of software-role assignment research that argued for integrating competence fit, personal traits, motivational factors, team-building considerations, and project constraints rather than treating staffing as a competence-only problem (Varona et al., 2021). What distinguishes ROMA within that lineage is its explicit concentration on intrinsic motivation and self-determination, and later, on AI collaboration modes and human agency preservation (Valovy, 1 Nov 2025).

The framework’s central problem statement is consistent across its major formulations. Programming roles such as Pilot, Navigator, and Solo are treated as psychologically non-equivalent; individuals differ in personality; and therefore identical role assignments will not motivate everyone equally (Valovy et al., 2024). The dissertation version generalizes this logic to human-AI collaboration, asking how self-determination, intrinsic motivation, and team dynamics can be enhanced through personality-driven optimization of human-AI programming roles in VSEs, SOHOs, and undergraduate teams (Valovy, 1 Nov 2025).

2. Theoretical foundations and formal status

ROMA is grounded primarily in Self-Determination Theory and Big Five personality psychology (Valovy, 1 Nov 2025). Its motivational logic is that role assignments should support the three basic psychological needs of autonomy, competence, and relatedness, thereby improving intrinsic motivation. In the earlier pilot formulation, the underlying causal model was stated conceptually as

$$\text{Personality traits} \rightarrow \text{Role fit} \rightarrow \text{Higher intrinsic motivation} \rightarrow \text{Better collaboration/productivity/well-being}.$$

That expression was presented as a conceptual mechanism rather than as a formal assignment equation (Valovy et al., 2024).

The framework uses the Big Five traits—Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism—as moderators of role preference and motivational response (Valovy, 1 Nov 2025). In the dissertation, these traits are not treated merely as descriptive covariates; they are presented as shaping how developers experience particular collaboration roles and AI interaction modes. The AI extension adds two notable concepts: “synthetic relatedness,” defined as quasi-social satisfaction derived from interaction with AI, and “meta-autonomy,” defined as autonomy exercised at the level of orchestration and delegation rather than direct implementation (Valovy, 1 Nov 2025).

Despite its name, ROMA is not formalized in the cited literature as an optimization problem with a closed-form objective over assignments. The early paper explicitly states that it does not provide a numerical trait-to-role scoring equation, cluster centroid definitions, threshold values, a partner compatibility matrix, pseudocode, a utility function, or a combinatorial optimization criterion (Valovy et al., 2024). The dissertation likewise states that it does not provide a formal mathematical optimization objective for ROMA itself, though it does use a linear mixed-effects formulation as its main statistical backbone:

$$Y_{ij} = X_{ij}\beta + Z_{ij}b_i + \varepsilon_{ij}.$$

Here, $Y_{ij}$ is the motivational outcome for participant $i$ at observation $j$, $X_{ij}\beta$ denotes fixed effects such as role and personality, $Z_{ij}b_i$ random effects for participant-specific baselines, and $\varepsilon_{ij}$ the residual term (Valovy, 1 Nov 2025).

A plausible implication is that ROMA is more accurately classified as an evidence-based adaptive framework than as a mathematically complete assigner. Its empirical backbone is statistical and design-science-oriented, while its assignment logic is heuristic, recommendation-oriented, and monitored over time rather than solved once by a formal optimizer.

3. Role taxonomies, archetypes, and assignment logic

ROMA’s original human-human role taxonomy centers on three programming roles: Pilot, Navigator, and Solo (Valovy et al., 2024). Pilot denotes direct implementation and coding; Navigator strategic oversight, reviewing, communication, and direction; Solo independent development (Valovy, 1 Nov 2025). The human-AI extension adds three major AI collaboration modes: Co-Pilot, Co-Navigator, and Agent. Co-Pilot corresponds to inline, immediate, context-aware support; Co-Navigator to conversational, explanatory, multi-turn guidance; and Agent to more autonomous execution-oriented AI behavior (Valovy, 1 Nov 2025).

The dissertation’s operational architecture is organized into four components: Personality Assessment Protocol, Role Assignment Matrix, Team Dynamics Guidelines, and Monitoring and Adaptation Protocol (Valovy, 1 Nov 2025). Assignment is personality-informed but not static. ROMA includes periodic reassessment through IMI pulse checks, MWMS checks, retrospectives, project-phase reassessment, and role reassignment or rotation when motivation drops (Valovy, 1 Nov 2025).

The framework’s later synthesis identifies five archetypes:

Archetype	Trait pattern	Preferred roles
Explorer	high Openness	Pilot; often Co-Pilot
Orchestrator	high Extraversion and Agreeableness	Navigator; often Co-Navigator
Craftsperson	high Neuroticism and low Extraversion	Solo; selective Agent usage
Architect	high Conscientiousness	flexible; quality-oversight or structured roles
Adapter	balanced profile	rotating or context-dependent roles

These five archetypes are not purely cluster outputs. The dissertation states that the initial empirical work produced three statistically derived clusters, while the later five-archetype system is a theoretical generalization integrating trait theory, qualitative insights, and design logic (Valovy, 1 Nov 2025). The original three-cluster operationalization associated high Openness with Pilot, Extraversion and Agreeableness with Navigator, and Neuroticism and Introversion with Solo; it also noted that Conscientiousness was present descriptively in the data but not mapped as a formal role-allocation rule in that early formulation (Valovy et al., 2024).

The human-AI extension further differentiates AI “modes” from AI “roles.” In Co-Pilot mode, canonical AI roles include Accelerator and Safety-Net; in Co-Navigator, Mentor, Rubber Duck, Domain Expert, and Critic; in Agent mode, Executor, Optimizer, and Coordinator (Valovy, 1 Nov 2025). This distinction matters because ROMA does not treat AI merely as a tool category; it treats assignment mode as motivationally consequential, with some modes better preserving agency, competence, or relatedness for particular personality profiles.

4. Empirical development and findings

The earliest direct empirical ROMA evidence came from a pilot quasi-experiment in distributed pair programming involving 4 Czech VSE professionals, 12 sessions, 6 rounds per session, and 72 analysis data points (Valovy et al., 2024). Participants completed the Intrinsic Motivation Inventory Enjoyment/Interest subscale after each round, and all participants exhibited high Openness as predominant trait. Role-based motivation differences were near-significant under ANOVA and Kruskal–Wallis:

$F(2,9)=3.88,\quad p=0.061$

and

$\chi^2(2)=5.65,\quad p=0.059.$

The study’s within-person consistency result was stronger:

$$Y_{ij} = X_{ij}\beta + Z_{ij}b_i + \varepsilon_{ij}.$$0

with an average difference of $$Y_{ij} = X_{ij}\beta + Z_{ij}b_i + \varepsilon_{ij}.$$1 points and $$Y_{ij} = X_{ij}\beta + Z_{ij}b_i + \varepsilon_{ij}.$$2 CI $$Y_{ij} = X_{ij}\beta + Z_{ij}b_i + \varepsilon_{ij}.$$3. For participants in the high-Openness cluster, mean intrinsic motivation was highest in the Pilot role:

$$Y_{ij} = X_{ij}\beta + Z_{ij}b_i + \varepsilon_{ij}.$$4

The authors interpreted this as support for the openness-to-Pilot mapping.

The dissertation substantially broadened that empirical base through five Design Science Research cycles, reporting 200 experimental participants and 46 interview respondents, while also referencing earlier survey work with 243 participants in external firms (Valovy, 1 Nov 2025). Its core human-human LME dataset comprised 66 participants and 1092 motivation observations. The initial clustering used hierarchical cluster analysis with Euclidean distance, complete-linkage criterion, and Dunn index validation; the reported Dunn index of $$Y_{ij} = X_{ij}\beta + Z_{ij}b_i + \varepsilon_{ij}.$$5 supported the three-cluster solution (Valovy, 1 Nov 2025).

At the aggregate level, the dissertation reports that personality-aligned role assignments yielded 23% average motivation increases among professionals and up to 65% among undergraduates (Valovy, 1 Nov 2025). Its strongest quantitative support comes from repeated-measures motivational analysis. The main effect of role was

$$Y_{ij} = X_{ij}\beta + Z_{ij}b_i + \varepsilon_{ij}.$$6

Pairwise comparisons showed:

• Pilot vs Solo: mean difference $$Y_{ij} = X_{ij}\beta + Z_{ij}b_i + \varepsilon_{ij}.$$7, $$Y_{ij} = X_{ij}\beta + Z_{ij}b_i + \varepsilon_{ij}.$$8 CI $$Y_{ij} = X_{ij}\beta + Z_{ij}b_i + \varepsilon_{ij}.$$9, $Y_{ij}$0, Cohen’s $Y_{ij}$1
• Navigator vs Solo: mean difference $Y_{ij}$2, $Y_{ij}$3 CI $Y_{ij}$4, $Y_{ij}$5, Cohen’s $Y_{ij}$6
• Pilot vs Navigator: mean difference $Y_{ij}$7, $Y_{ij}$8 CI $Y_{ij}$9, $i$0, Cohen’s $i$1

This yields the empirical ordering

$i$2

for intrinsic motivation on average (Valovy, 1 Nov 2025).

The dissertation also reports trait-role moderation coefficients that closely match the framework’s mapping logic. High Openness enhances Pilot motivation by $i$3 ($i$4); high Extraversion enhances Navigator by $i$5 ($i$6); high Agreeableness enhances Navigator by $i$7 ($i$8); high Neuroticism enhances Solo by $i$9 ($j$0); and high Neuroticism reduces Pilot by $j$1 ($j$2) (Valovy, 1 Nov 2025). The reported variance decomposition was marginal $j$3 and conditional $j$4, indicating that participant-specific baselines materially contribute to the observed motivational variance.

Qualitative evidence across both works converges on the same mechanism. Pilot was repeatedly described as more active, stimulating, and engaging; Navigator could feel passive, especially in distributed settings; and Solo supported focus but often lacked collaborative energy (Valovy et al., 2024). In the later dissertation, assignment modes were reported as crucial for satisfaction, and AI mode choice was described as crucial for need satisfaction in human-AI collaboration (Valovy, 1 Nov 2025).

5. Tooling, distributed deployment, and standards integration

ROMA was not developed only as a conceptual scheme. The distributed pair-programming paper describes a working software system with a Rust backend integrated with Solana, a React v18 and TypeScript frontend, JWT-secured API communication, and collaboration through IntelliJ Code With Me and Visual Studio Live Share (Valovy et al., 2024). The application supported registration, personality assessment, session scheduling, role recommendations, calendar integration, and IMI form submission. It was designed to facilitate partner matching based on ROMA suggestions, expertise, and availability.

The blockchain component was explicitly secondary to the psychology of ROMA. Its function was to support transparency, reproducibility, traceability, durability, and potentially generalizability/adaptability through continuous data accumulation (Valovy et al., 2024). Experimental data were stored through Solana transaction memos on Devnet, with participant identities anonymized using SHA-256 hashed IDs. Figure-level description in the paper indicates on-chain storage of hashed participant ID, date/time, nationality, experience, gender, session duration, BFI values, round-level motivation inventory responses, and open-ended feedback. Smart contracts via custom Solana Program Libraries were proposed as future work rather than being part of the current ROMA implementation.

The dissertation translates ROMA into organizational process design through an ISO/IEC 29110 extension for VSEs (Valovy, 1 Nov 2025). It maps ROMA tasks into Project Management and Software Implementation and identifies seven key tasks:

• Integrate Personality Assessment in Hiring
• Assess Multidimensional Work Motivation
• Define the Big Five Assessment Strategy
• Pairing Task
• Measure Intrinsic Motivation Impact
• Review and Update Pairing Strategies
• Support Future Planning and Knowledge Transfer

This standards-oriented extension treats ROMA as a lightweight, monitorable organizational practice rather than a one-shot assessment. Mentioned artifacts include job posting and candidate evaluation forms, team composition matrices, personality assessment policy, sprint backlog pairing assignments, IMI survey forms, motivation impact reports, pairing effectiveness logs, and optimization summaries and best-practice repositories (Valovy, 1 Nov 2025).

6. Limitations, misconceptions, and related nomenclature

Both major ROMA sources emphasize limitations. The early distributed study had only 4 participants, all from a Czech VSE context, and all predominantly high in Openness; it was quasi-experimental, lacked randomization, relied mainly on self-reported IMI scores, and provided limited objective productivity data (Valovy et al., 2024). The dissertation reports broader evidence, but still identifies student-heavy samples in several phases, mostly Czech/European context, limited longitudinal evidence, rapidly changing AI tools, and personality-archetype mappings that are partly design-theoretical rather than uniformly hard-clustered (Valovy, 1 Nov 2025). The same dissertation also warns against over-automation, competence erosion, personality essentialism, privacy and discrimination risks in assessment, and loss of human agency if AI becomes the default actor.

A recurring misconception concerns formal status. ROMA is not, in the cited literature, a mathematically complete optimizer with a published assignment solver. Its role recommendations are heuristic and adaptive, supported by empirical mappings, monitoring loops, and standards integration, but not by an explicit global assignment objective or combinatorial optimization engine (Valovy et al., 2024). A plausible implication is that ROMA leaves open many algorithmic questions about multi-person matching, partner compatibility scoring, dynamic online adaptation, and objective performance optimization.

A second misconception concerns nomenclature. The acronym “ROMA” is overloaded on arXiv. It can refer to “Role-Oriented Multi-Agent Reinforcement Learning” with emergent roles (Wang et al., 2020), “Recursive Open Meta-Agents” for long-horizon multi-agent systems (Alzu'bi et al., 2 Feb 2026), and “ROund-Robin MultiAgent Scheduling” in ROMA-iQSS (Lin et al., 2024). In addition, “RoMA” in “Routing Manifold Alignment Improves Generalization of Mixture-of-Experts LLMs” is explicitly “Routing Manifold Alignment,” not Role Optimization Motivation Alignment (Li et al., 10 Nov 2025). For encyclopedia purposes, Role Optimization Motivation Alignment should therefore be reserved for the personality-driven software-engineering framework developed in the distributed pair-programming and dissertation literature (Valovy et al., 2024).