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From Digital Twins to Digital Triplets in Economics and Financial Decision-Making: History
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Contributor: Ioannis Passas

This entry reviews the evolution from Digital Twins (DT) to Predictive Digital Twins (PDT) and Digital Triplets (DTr), culminating in Predictive Digital Ecosystems, which focus on economic and financial decision-making. It discusses historical developments, technical foundations, practical applications, ethical and regulatory challenges, and future directions. The overview integrates mature knowledge from engineering, data science, and economic domains to provide a structured reference framework for understanding and deploying Predictive Digital Ecosystems.

  • Digital Twins
  • Predictive Digital Twins
  • Digital Triplets
  • Predictive Digital Ecosystems
  • economic decision-making
  • financial technologies
  • predictive analytics
  • Internet of Things (IoT)
  • artificial intelligence (AI)
  • blockchain
The emergence of Digital Twins (DTs) represents a transformative development in the application of digital technologies across various sectors, including manufacturing, engineering, healthcare, and increasingly, economic, and financial decision-making [1][2][3][4][5][6]. A DT is traditionally understood as a real-time, dynamic digital representation of a physical asset, system, or process [2]. Initially developed to enhance monitoring and maintenance operations by replicating physical objects within a virtual environment, DTs evolved considerably with advances in Internet of Things (IoT), Big Data, artificial intelligence (AI), and cloud computing technologies [2][7][8]. With the integration of predictive modeling, DTs gave rise to Predictive Digital Twins (PDTs), which go beyond descriptive capabilities to offer foresight into future system behaviors based on current and historical data [9][10]. The increasing complexity and interdependence of systems have led to the concept of DTr [11][12][13], where multiple PDTs are interconnected within a holistic framework known as a Predictive Digital Ecosystem (PDE) [9][14]. These interconnected systems provide comprehensive predictive analytics capabilities and support proactive decision-making processes, particularly in the highly dynamic and interconnected fields of economics and finance [15]. This entry provides a comprehensive review of the development, conceptual foundations, technological infrastructures, applications, ethical considerations, and future directions of DTs, PDTs, and DTrs within the context of Predictive Digital Ecosystems.
The conceptual roots of Digital Twins can be traced back to the early simulation models developed for aerospace engineering [16][17][18]. The idea matured significantly during NASA’s Apollo program in the 1960s, where exact physical replicas of spacecraft were maintained on Earth to simulate and troubleshoot conditions encountered during missions [18][19]. However, it was not until 2005 that Dr. Michael Grieves formally introduced the term “Digital Twin” in the context of Product Lifecycle Management (PLM) [20][21]. The original framework proposed the integration of physical products, virtual products, and the connections between them to enable enhanced design, manufacturing, and operational processes. The subsequent development of IoT technologies provided the infrastructure necessary for real-time data flow between the physical and digital realms, making the realization of true Digital Twins feasible. Over time, the role of DTs expanded from static models to dynamic, real-time systems capable of supporting complex decision-making processes [22][23]. Integrating predictive analytics methodologies, including machine learning algorithms and advanced simulation techniques, further evolved DTs into Predictive Digital Twins, enabling organizations to anticipate future states and optimize operational and strategic responses [24]. The growing interconnectedness of economic activities and the increasing need for holistic system representations have prompted the emergence of DTrs and PDEs, which conceptualize entire ecosystems of interconnected predictive models capable of collaborative learning, adaptation, and decision-making.
A key trajectory in digital transformation involves the transition from Digital Twins (DTs) to Predictive Digital Ecosystems (PDEs), which encapsulate real-time situational awareness, machine learning foresight, and ecosystemic optimization. DTs initially emerged in industrial settings as digital replicas of physical assets, enabling diagnostics and performance monitoring.
To further clarify the architectural variety of Digital Twins and how this progression informs PDEs, Table 1 presents a typology of DT architectures. The typology identifies four major categories—Basic Replicative DTs, Monitoring Twins, Predictive Twins, and autonomous PDEs—each with increasing levels of integration, real-time responsiveness, and decision-making autonomy.
Table 1. Typology of Digital Twin architectures: from simulation to autonomy.
Architecture Type Defining Characteristics Data Integration Autonomy Level Key Use Cases
Model-Driven DT Based on physics-based or engineering simulation models Low Low Aerospace simulation, material stress testing
Data-Driven DT Empirical modeling via sensor streams, Machine Learning (ML) pattern recognition High Medium Manufacturing, predictive maintenance
Hybrid DT Integrates data streams with pre-existing analytical or mechanistic models Medium–High Medium–High Healthcare, urban traffic control, fintech
Cognitive DT/DTr Self-learning systems with real-time environmental adaptation Very High High Autonomous vehicles, smart grids, ESG compliance
This typology elucidates the transformation from object-centric monitoring to ecosystemic prediction and governance. Basic DTs rely on batch data and serve as visual diagnostics tools. Monitoring Twins incorporate real-time telemetry but remain reactive. Predictive Twins, by contrast, leverage machine learning for forecasting system states. Finally, autonomous PDEs represent the apex of architectural complexity, integrating decentralized AI, federated learning, and blockchain-enabled traceability to support autonomous optimization across sectors like urban mobility, finance, and supply chains.

This entry is adapted from the peer-reviewed paper 10.3390/encyclopedia5030087

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