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Parallel Systems is creating a zero-emission freight transport system, believing that the future of freight transport should be fast, affordable, and clean while reducing congestion on highways. The company was founded in 2020 and is headquartered in Los Angeles, California.
Precision World opens new ways to observe, analyze, and predict today’s complex systems using sensors and computer vision.
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Parallel is an innovation consultancy helping clients navigate this space and develop the products, services, and investments that define it.
Advancing a New Category – Simulation Intelligence
As we go about our daily lives, we constantly create and use mental models of the world around us to help with navigation, weighing options, and decision-making:
However, as decisions grow more complex, digital models offer more accurate, precise, and faster solutions than our mental models. This is why software-based models, like spreadsheets, are so prevalent in business and engineering. These approximations of the world—these simulations—allow us to test ideas, conduct experiments, and make assumptions about the future without expending resources or breaking things.
Simulation is an approximate imitation of the operation of a process or system that represents its behavior over time.
As more of our actions become digitized, new inputs for feeding these models become available. In turn, as decisions increasingly rely on simulations, their outputs and recommendations grow more significant.
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Today, the stakes are higher than ever. We face a climate crisis, a viral pandemic, rising inequality, surveillance capitalism, and a polarized misinformation environment. Meanwhile, technology propels us into a world increasingly automated, virtual, and data-driven. The convergence of these forces brings significant changes for individuals, businesses, and society.
Simulation intelligence refers to emerging strategies for designing simulations that aim to improve people’s ability to understand and apply them in real-world contexts.
When we think about simulations, they can be purely mathematical (e.g., the performance of a financial portfolio under different conditions) or purely visual (e.g., light rays illuminating a CG scene). However, they become incredibly powerful when both work together—combining imagery and mathematics to reveal insights otherwise incomprehensible.
Design in this context encompasses not just the simulation itself but how it operates, how users interact with it, the information it conveys, the weight assigned to various parameters, and how easy it is to question. Simulation intelligence can be viewed as a stack of five interconnected layers: strategy, data, logic, communication, and interaction.
All these considerations are critical for developing applications that meet their objectives—not just being useful but also being used. This makes simulation intelligence a multidisciplinary effort, drawing on diverse skill sets:
Optimizing how people observe, analyze, and predict the physical world lies at the core of simulation intelligence. As everything around us becomes more connected, complex, and automated, these capabilities are becoming essential.
This will manifest across industries—from energy to agriculture, logistics to healthcare. While specific requirements will vary, common use cases will influence them all.
Situational Awareness Since World War II and Churchill’s smoke-filled bunker, we’ve been familiar with the idea of “war rooms”—immersive data spaces centralizing all available information to support tactical decision-making. The concept of the “big board” has been a cinematic staple, from Dr. Strangelove to Avatar, evolving in technical sophistication with each generation. Today, with a world of data at our fingertips, these spaces are as relevant as ever, but situational awareness is no longer limited to state-level players. With a pocket device, you can now know a company’s value, a ship’s position, or your blood oxygen level in real time.
In recent years, the term “digital twin” has become a common way to describe these representations. Initially used in manufacturing and engineering, it is now expanding to encompass entire companies, economies, or even oceans.
Traditional digital twins often involve a one-to-one 3D replica of a real-world counterpart. As people become more familiar with the concept, a broader range of design patterns will emerge, considering diverse approaches to visualization and interaction.
The challenge is not just replicating what already exists but helping users focus on what is critical to them in the moment. This involves providing the appropriate levels of detail, context, and abstraction for the task at hand—even as the situation evolves. When we observe these here-and-now simulations, we want to know not only what has happened but also why, how things are interconnected, and what is likely to occur.
Predictive Modeling Approaches to predictive modeling have existed for decades, but only recently have the necessary data and computational power become more widely accessible.
A key component of these techniques is the concept of abstraction—reducing the nearly infinite complexity of the real world to a manageable number of parameters that provide a workable approximation of system dynamics. Depending on the problem, different levels of abstraction and modeling approaches may be used. System dynamics often model higher-level strategic issues. Discrete-event simulation is widely used in manufacturing and services industries to describe processes. Agent-based modeling describes systems through the behavior of individual agents, from cars in traffic simulations to cells in in-silico drug trials.
Many tools are available for building simulation models, but these are often highly technical applications inaccessible to non-specialists, ultimately offering simplistic, chart-based outputs. The abstraction involved makes it easy for model developers to confine themselves to it, forgetting the broader context of the system they’re modeling or overlooking second- and third-order effects of various scenarios.
Simulation intelligence encourages a different approach. By anchoring predictive modeling to strategic objectives and considering the importance of communication and interaction, it aims to enhance the usability and understanding of these powerful tools. As the range of decision-making and forecasting grows deeper through simulations, transparency, traceability, and fairness become increasingly critical. This is especially important as we delegate authority to entirely virtual and automated entities.
Currently, some of the world’s most valuable companies generate massive revenues from data produced by billions of people. They have built knowledge graphs reflecting and predicting the changing ontologies of the world. With the exponential growth in connected devices, even more opportunities to extract value will arise.
To understand these ecosystems, we must comprehend them. We will need new maps for this uncharted territory.
Just as today’s digital twins help us understand interactions between real-world assets, we will see the emergence of synthetic environments that provide ways to visualize and control intangible assets made of data and code. By definition, these spaces will be highly abstract. Unlike digital twins, they have no physical counterpart to replicate. As folders and files provided metaphors for PC interfaces, it’s likely these new artificial realities will borrow heavily from the physical world. Designing these systems and how people interact with them will present challenges and opportunities for future product leaders.