I have always been obsessed with science fiction, especially the stories where computers become more powerful and smarter than humans. In 1999, after watching the movie “The Matrix”, my obsession got a boost in the sense that I started fantasizing about a computer system that could simulate societal dynamics and evolution with a high level of detail.

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I imagined that such a computer system would also be capable of simultaneously simulating thousands or millions of different versions of the future, like you would play the computer game “Civilization” thousands or millions of times at the same time.

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By analyzing the outcomes of all these simulations, it would then be possible to determine if certain future scenarios were more likely to happen than others, thus allowing, to a certain degree, to “predict” the future or at least make it less uncertain.

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Exploring Agent-Based Models and shaping my vision

This fantasy kept floating around in my head for many years until, in 2008, at the start of my career as an academic, I started studying various computational models to simulate the dynamics and evolution of complex systems. I was especially intrigued by the underlying paradigm of agent-based models, namely that the evolution of a complex system emerges from the discrete actions and interactions of individual agents composing it.

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Agent-based models are mainly used in academics, especially in biology or social sciences, to simulate the growth of tumors or people's behavior in a crowd based on the actions and interactions of individual agents (cells, people, organizations, etc.).

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The more I read about these models, the more I thought that building the computer system I had been obsessed with since the release of “The Matrix” in 1999 maybe wasn’t just a pure science fiction thing. I decided to experiment with these models but soon realized I was too optimistic and naive.

With two failed attempts to build a first and very simple version of the envisioned computer system, I experienced the hard way that simulating societal dynamics and evolution with all its complexity and variability was just too hard of a challenge. One that could certainly not be tackled by one person.

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I estimated that it would at least take another 25 years of scientific and technological progress before agent-based models would be capable of modeling in detail the reasoning, decisions, behavior, actions, and interactions of all individual actors in our society (individuals, commercial companies, trade unions, non-profit organizations,  governments, etc.). Over the years following my failed attempts, I slowly but surely lost interest in the subject.

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Societal dynamics emerge from a chain of discrete events, the essence of Trensition’s core model.

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Fast forward to the summer of 2017. In my free time, I had been working for almost a year on a first, basic version of a mathematical model that in 2019 would become the core of Trensition to analyze and forecast the evolution of a wide variety of economic, societal, political, technological, scientific and environmental factors and events.

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I built this model because I wanted to compare the evolution and adoption of a particular technology in different industries. At that time, there was simply no tool available on the market that allowed such analysis. By the time I had finished the latter model, I realized that instead of considering societal dynamics and evolution to emerge from individual agents acting and interacting with each other, you could also think to emerge from a chain of discrete events changing the state of society- precisely the events analyzed by Trensition’s core model. This shift in perspective rekindled the moonshot thinking of yesteryear, breathing new life into my aspirations.

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From vision to mission: joining forces

Until the summer of 2017, I had conducted all the research in my free time out of pure curiosity. I started wondering if, somehow, I could continue my research in a commercial context and make a living out of it. That’s when, in the fall of 2017, I reached out to my later co-founder, Vincent Defour, to present him some results of my work.

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Vincent was a strategy and innovation consultant who understood what companies were struggling with to remain innovative and plan for the long term. After only a couple of minutes, he told me that my solution had the potential to facilitate his work and could even support the strategic decisions of organizations.

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We decided to explore the possibility of starting a company with the long-term mission of building the world’s first computer system capable of analyzing and simulating societal dynamics and evolution and applying it to support strategic decision-making in organizations.

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Two years later, after much additional research and discussions, we finally founded Trensition and officially started our mission with co-founder Mathias Colpaert.