About BlockScience
BlockScience® is a complex systems engineering, R&D, and analytics firm. By integrating cutting-edge research, applied mathematics, and computational engineering, we analyze and design safe and resilient socio-technical systems.
With deep expertise in Blockchain, Token Engineering, AI, Data Science, and Operations Research, we provide engineering, design, and analytics services to a wide range of clients, including for-profit, non-profit, academic, and government organizations.
Our team is interdisciplinary, with engineering teams operating in concert with social science and governance research teams of ethnographers and economists. This enables us to provide a more holistic approach to solving algorithm design problems with complex human behavior implications.
BlockScience uses a hybrid of artificial intelligence and computational social science to develop, teach, and apply best practices in economic systems engineering. Our R&D occurs in iterative cycles between open-source research and software development, and application of the research and tools to client projects.
Our client work includes design and evaluation of economic and governance mechanisms based on research, simulation, and analysis. We also provide post-launch monitoring and maintenance via reporting, analytics, and decision support software.
With our unique blend of engineering, data science, and social science expertise, the BlockScience team aims to diagnose and solve today’s most challenging frontier socio-technical problems.
🍎 Learn more about BlockScience on our website
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🎥 Watch our research presentations and talks on YouTube
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📚 Check out our Medium page
Some of the BlockScience team members at our Summer 2022 Team Retreat in New York. Front row: Nick Hirannet, Jeff Emmett, Jessica Zartler, Michael Zargham, Antonio Reyes, Danilo Lessa Bernardinelli, Lucia Korpas, Emanuel Lima. 2nd row: Daniel Furfari, Heather Ordway, Joshua Jodesty, Jamsheed Shorish, David Sisson, Charlie Rice, Will Wolf. 3rd Row: Sarah Hirannet, Kirstin MacLean, Matt Barlin
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