What are Digital Twins & Digital Triplets?

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.

• Cite:  Passas, I. (2025). From Digital Twins to Digital Triplets in Economics and Financial Decision-Making. Encyclopedia, 5(3), 87. https://doi.org/10.3390/encyclopedia5030087