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Missing Dimensions: Integrating Human and Social Systems into Digital Twin Engineering

Francis Bordeleau, Mark van den Brand

arXiv:2609.12131v1cs.SE

TL;DR

DT engineering often underrepresents human and social dimensions, even though those dimensions affect what a twin must represent. The paper proposes a four-dimension framework and argues that, as DTs extend beyond engineered systems, they should emphasize decision support and occupant empowerment.

  • Problem

    DT engineering often reduces twins to engineered or natural/biological systems, while human and social dimensions are ignored or only superficially addressed.

    Source: §1 · p. 1

  • Method

    The paper proposes a framework of Engineered, Natural/Biological, Human, and Social dimensions, using a smart building to discuss their modeling requirements and implications.

    Source: §1 · pp. 1–2

  • Results

    The paper argues that DTs extending into human and social dimensions should shift emphasis from automated control toward decision support and occupant empowerment.

    Source: §2 · p. 2, §1 · p. 2

  • Takeaways & Limitations

    A DT should account for dimensions relevant to its purpose, with data and models differing across dimensions and uncertainty increasing toward the Social dimension.

    Source: §1 · p. 1, §3 · p. 3

    Update frequency depends on purpose and goals, ranging from real-time synchronization to periodic batch updates.

    Source: §2 · p. 2

Abstract

from arXiv · show

Digital twins (DTs) have emerged as a key technology at the core of digital transformation, yet their engineering practice remains too narrowly focused on engineered and natural systems. This paper argues that four system dimensions must be explicitly recognized in DT engineering: Engineered, Natural/Biological, Human, and Social. Each dimension brings distinct properties, modeling requirements, and ethical obligations that fundamentally shape what a DT must represent and how it must be built. We further argue that as DTs extend into the Human and Social dimensions, the emphasis should shift from automated control toward decision support and occupant empowerment. We illustrate our arguments using a smart building as a running example and identify four open research challenges for multi-dimensional DT engineering.

1 Introduction

The paper argues that DT engineering must account for four system dimensions, each with distinct properties, modeling requirements, and ethical obligations. Its smart-building example shows how a DT’s purpose can expand its scope from engineered assets to occupant and collective needs.

1 Introduction

  • The framework distinguishes Engineered, Natural/Biological, Human, and Social dimensions, with human agency distinct from biological processes and social networks shaped by relationships and norms.The Human dimension includes individual physiology, behavior, cognition, preferences, and needs; the Social dimension concerns interacting individuals, groups, and institutions.Source: §1 · p. 1
  • A smart-building DT may progress from modeling HVAC and structural systems to incorporating climate and circadian cycles, occupant profiles and accessibility needs, and collective building use.This progression changes the DT’s required data, models, and ethical obligations.Source: §1 · p. 1
  • Current DT engineering often overlooks or superficially addresses human and social dimensions, and lacks guidance for identifying, integrating, and balancing them.The paper locates this limitation primarily in engineering practice rather than in the DT artifact itself.Source: §1 · p. 1
  • The smart-building example is presented as applicable beyond buildings, including healthcare, smart cities, and industrial systems.Source: §1 · p. 2
  • The paper proposes a four-dimension framework and identifies open research challenges for systematically engineering multi-dimensional DTs.It also argues for shifting emphasis beyond automated control toward decision support and human empowerment.Source: §1 · p. 2

2 Automated control is not essential for DTs

Automated actuation is not required for a DT: monitoring, simulation, and decision support can provide actionable insight. As DTs incorporate human and social dimensions, the paper argues for keeping occupants meaningfully involved and in control.

2 Automated control is not essential for DTs

  • The paper defines a DT as a dynamic digital replica of an Actual Twin, continuously updated with data and able to interact with and influence it.Its four foundational pillars are purpose, services, models, and data.Source: §2 · p. 2
  • A DT’s purpose and goals determine its required representation and update cadence, which may range from real-time synchronization to periodic batch updates.Source: §2 · p. 2
  • A DT that provides actionable insight through monitoring, simulation, and decision support qualifies even without automated actuation.The paper identifies automated control as a capability rather than an essential characteristic of a DT.Source: §2 · p. 2
  • Unilateral environmental adjustments may conflict with occupant agency or comfort, or miss needs not captured in an occupant profile.Source: §2 · p. 2
  • For human and social dimensions, the paper favors decision support, recommendations, and occupant empowerment over autonomous environmental control.It argues that occupants should remain meaningfully in the loop and in control of their environment.Source: §2 · p. 2

3 System dimensions

The four system dimensions are nonexclusive, and a DT should represent those relevant to its purpose. As dimensions become more abstract, modeling uncertainty and the need to accommodate drift increase.

  • A given Actual Twin may span multiple dimensions, so the DT should cover those relevant to its purpose and goals.Data and models differ by dimension, uncertainty increases toward the Social dimension, and the DT should accommodate drift.Source: §3 · pp. 2–3
  • Engineered systems are most tractable to model, while Natural/Biological systems add uncertainty because they cannot be fully specified or controlled by design.Biological behavior also reflects life processes, beyond physical and chemical laws.Source: §3 · pp. 2–3
  • Human modeling must account for active agents whose responses vary over time, while protecting privacy, consent, and individual autonomy.In buildings, the Human dimension can support personalized comfort, accessibility, and services.Source: §3 · p. 3
  • Social systems are the most abstract and volatile dimension because collective behavior is difficult to predict or model precisely.Building examples include shared-space negotiation, organizational policies, emergency response, and collective energy norms.Source: §3 · p. 3

4 Impact of system dimensions on DT engineering

DT engineering requires deliberate choices about scope, fidelity, and evolution. As a twin grows to cover additional aspects and dimensions, the work also requires multidisciplinary collaboration and tools that let domain experts contribute to its models.

  • DT scope should focus on aspects relevant to its goals, fidelity should match its purpose, and development should evolve incrementally.Excess fidelity adds unnecessary cost and complexity; scope, fidelity, models, and services can evolve as understanding deepens.Source: §4 · pp. 3–4
  • A smart-building comfort twin may grow from engineered HVAC control to models of individual physiology and preferences, extending into Human and Social dimensions.The set of covered aspects can expand with changing user requirements, new data, and broader operational objectives.Source: §4 · p. 4
  • Multi-dimensional twins require collaboration among specialists from engineering, life sciences, behavioral science, accessibility, and social science.Contributors should work as independently as possible while maintaining a coherent and consistent DT.Source: §4 · p. 4
  • Domain experts need abstractions, domain-specific languages, and visual notations to define, validate, and evolve models without working directly in software infrastructure.Model-driven engineering is identified as particularly suited to providing these tools.Source: §4 · p. 4

5 Research Challenges

The paper identifies four open challenges for systematically integrating all dimensions into DT engineering: collaboration and tooling, architecture, heterogeneous integration, and drift management.

  • Cross-dimensional tooling must bridge domain expertise and software implementation so non-software experts can define, validate, and evolve relevant models and data.This includes expertise across engineering, life sciences, behavioral science, and social science.Source: §5 · p. 4
  • A multi-dimensional reference architecture must integrate heterogeneous data, modeling paradigms, and independently developed services while supporting separation of concerns and incremental evolution.These properties support parallel contributions from multidisciplinary teams.Source: §5 · p. 4
  • Integrating dimension-specific data and models into a coherent, interoperable DT while preserving consistency and traceability remains an open challenge.The sources range from physics simulations and sensor streams to physiological, behavioral, preference, and social-interaction models.Source: §5 · p. 4
  • Drift detection must be part of DT operation, with detected drift triggering evolution to restore trustworthiness.Drift is especially acute for human and social dimensions because they are more volatile and harder to synchronize.Source: §5 · p. 4

6 Summary

The paper concludes that DT engineering needs to address the full complexity of represented systems through four dimensions and their distinct requirements. It argues for decision support and occupant empowerment as twins extend into human and social systems.

  • Engineered, Natural/Biological, Human, and Social dimensions bring distinct modeling requirements and ethical obligations to DT design and evolution.Source: §6 · p. 4
  • As DTs extend into Human and Social dimensions, automated control should give way to decision support and occupant empowerment.Source: §6 · p. 4
  • Four open research challenges must be addressed to make multi-dimensional DT engineering systematic and principled.Source: §6 · p. 4
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