Engineering Resilience. Enabling Adaptive systems. Discover our framework for scalable system-level design of adaptive architectures.
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Disruptions in complex systems
Dynamic adaptation in complex systems is not yet fully understood. Complex systems are perpetually in flux, facing constant change and unexpected disruptions (Fig. 1). Those that successfully adapt not only survive but thrive, becoming resilient over time, while those that fail to adapt suffer significant losses. Steering such systems toward beneficial outcomes remains a daunting task, let alone engineering the features that enable their effective modulation.
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Figure 1. Complex system disruptions using a ripple propagation model. The diagram illustrates how various disturbances affect system dynamics through expanding ripple effects, exemplified by five key disruption types The central impact point and concentric ripples represent the spatial and temporal propagation of disruptions through complex systems, demonstrating how initial perturbations can amplify or transform as they spread through system networks.
Resilience through disruptions
Resilience is a multifaceted property that determines the longevity and success of a system. Rather than a static ability to resist change, resilience entails dynamic adaptation—absorbing disturbances, learning from them, and transforming in ways that can even improve overall performance (Fig. 2).
Although powerful tools exist for simulating diverse scenarios and understanding outcome contexts, they often operate independently, lacking a unified, multi-paradigm integration. We address this gap by developing frameworks that fuse multiple computational paradigms. Our goal is to use this integrated approach to deeply understand what adaptation means in key domains, contextualizing outcomes across varied scenarios, and become strategy innovators (steering system to beneficial outcomes).
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Figure 2. System resilience using a ripple propagation model. This diagram presents resilience as a dynamic process with interconnected mechanisms. The models show key resilience components (upper figure), and the cyclic evolution of adaptation including the discovery of strategies for perpetuality of systems (lower figure).
By merging these methods, our framework uncover robust strategies that enable systems to learn from stress and recovery cycles. This approach not only refines our understanding of dynamic adaptation but also transforms disruptions into catalysts for innovation. Ultimately, our work aims to pave the way for resilient systems that drive strategic, adaptive decision-making in dynamic domains.
Our approach
We are an applied research company pioneering hybrid composite AI. Our scientific foundations lie at the intersection of neurosymbolic AI, reinforcement learning, and optimization in simulated environments. We focus on developing operational software that integrates these methods for both domain-specific and cross-domain applications. By bridging scientific discovery with practical applications, we aim to transform our understanding of disruptions for adaptation, growth, and strategy innovation.
Our long-term research goal is to translate and incorporate bio-inspired principles (adaptive and evolutionary tested rules in nature's playbook) into modular composites as operational units for hybrid ecosystems. For example, we are interested in implementing this bio-computing approach to investigate how cellular memory, adaptation, and resilient evolution can guide the development of new computational paradigms.
Applied domains
Building on these principles and strategic partnerships, we deliver adaptive systems in finance (risk-adaptive strategies), digital commerce (market ecosystem optimization), software (enhance system-wide operations), techbio, and edtech (adaptive learning environments).
Our strategy is grounded in the belief that failure is a valuable learning opportunity. By teaching AI to contextualize and embrace disruptions, we empower it to uncover resilient strategies that can be scaled from individual domains to entire ecosystems.
Conclusion
As scientists and generalists at heart, we teach AI to embrace failure as a pathway for learning innovation. Anchored by five interlinked pillars—science-driven innovation, hybrid composite systems, finance, e-commerce, and edtech—our work not only deepens our understanding of dynamic adaptation but also lays the groundwork for semi-autonomous organizations composed of modular systems that are resilient and continuously adapting.
Teaching machines the nurturing resilience of nature
Integrating specialized modules into interconnected digital ecosystems that exhibit collective and adaptive properties.
Developing specialized composite models for several domains (e.g., finance and digital commerce), with operational modules coordinating their behavior. Science driven innovation for aligned and adaptive ecosystems.
"Where nature's resilence meets intelligent systems"
Daice Labs Inc.
Brookline, MA, USA
© 2025 Daice Labs Inc.
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