The Institute of Entropology

Independent research on the physics of information, resources and society.

The Institute of Entropology is a private research institute founded by three physicists whose work spans neuroscience, stochastic thermodynamics, dynamical systems and machine learning. We study systems that prsocess information: brains, economies, ecosystems and machines. Our starting conviction is that entropy is what makes these systems commensurable, and that a shared entropic accounting is the missing common language between the sciences that describe them.

Why entropy

Physics learned entropy twice, once as a measure of heat and irreversibility and once as a measure of information. Over the last three decades, stochastic thermodynamics has established the exchange rate between the two. Every act of measurement, prediction, memory and control carries a thermodynamic price, and every process that holds a system in an ordered state pays for that order with dissipation somewhere else. This gives us a single ledger in which computation, energy, materials and emissions can be entered in comparable terms. The stakes of that ledger are easy to see. A human brain learns continuously, controls a body and maintains a model of the world on roughly twenty watts. The machine learning systems now being deployed to do a narrower version of that work consume many orders of magnitude more, and the infrastructure built to run them is already reshaping electricity grids, land use and water budgets. How much information processing a given quantity of free energy can buy has become an industrial and political question.

Why now

Automation, computation and AI have delivered remarkable technical progress alongside a widespread sense of insecurity: flat prospects, rising inequality, fear of losing one’s work, and a politics increasingly organised around that fear. At the same time, growth pursued without explicit accounting for resource depletion and emissions continues to shift risk onto the future. Clean energy and recycling are usually offered as the way out. We take those technologies seriously. But their arrival cannot be scheduled, and efficiency gains have a long history of being absorbed by the demand they unlock. The rebound effect is a feedback loop, and feedback loops are precisely the kind of object that intuition handles badly and dynamical modelling handles well. The same holds for AI adoption in the workforce, which can raise measured productivity while leaving a workforce that is overloaded, financially precarious and ultimately less capable, and for the current build-out of data centres, which adds ecological and social strain to a system already carrying both. We want these technologies to succeed. That requires measuring what they actually do to the whole system they enter, including the parts that never appear on a balance sheet.

What we do

Foundational research. Thermodynamic and information-theoretic limits on learning, inference and control. The efficiency gap between biological and artificial computation. The stability, tipping points and long-run attractors of systems held far from equilibrium by continuous throughput, whether those systems are neural, ecological or economic. Applied research and advice. Quantitative scenario models built for people who have to decide something: public administrations, agencies, and institutions responsible for resource management, energy, industrial strategy and the introduction of AI into working life. Our models make their assumptions explicit, carry their uncertainty forward instead of hiding it, include the feedbacks that determine the outcome, and report results in carbon, energy, money and employment at the same time. We do not supply optimism or alarm on request. We build models that can be inspected, challenged and rerun with different assumptions, and we say plainly what they do and do not support.

Current work

Our first full-scale prototype models the wood supply chain of the Italian province of South Tyrol from the ground up. A biologically detailed model of forest growth under climate change is coupled to the dynamics of biomass extraction and to the three main downstream commodities: construction timber, furniture, and biomass for heating. The model tracks carbon capture, economic cost, industrial output and energy yield together, with employment dynamics in development. The province is small enough to model with real biological and economic detail, and structured enough to be informative. The architecture is designed to scale to national economies and to transfer to other countries and other resource systems.

Who we are

Three founding researchers who all met a decade ago at the Max Planck Institute for Dynamics and Self-Organization where they earned their PhD.

Dr. Bernhard Altaner studied physics and mathematics in Konstanz and Cambridge and earned his PhD in Göttingen on the foundations of stochastic thermodynamics. His research focuses on information processing in complex systems, ranging from the molecular to the cosmological scale. He currently works on smart energy management systems and various side quests. Currently, he investigates what AI research might teach us about universal aspects of agentic information processing in the brain and its connection to consciousness. Currently he lives in Ulm, Germany.

Dr. Debsankha Manik studied physics at the IISER-Kolkata, India and received his PhD on the dynamics of complex flow networks from the Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany. He has worked on transforming the mobility sector through on-demand mobility, both in academia (within large publicly funded research projects), and in industry (including at the Volkswagen-owned startup MOIA). He has not only contributed crucial development work for developing algorithms driving on demand mobility of people and goods, but also has collaborated with various public transportation agencies in Lower Saxony, Leipzig and Munich. Currently he lives in Hamburg, Germany.

Dr. David Hofmann studied physics at the Technische Universität München and earned his PhD in computational and theoretical neuroscience at the Max Planck Institute for Dynamics and Self-Organization. He worked as a postdoctoral researcher among other institutes at the prestigious MIT Computer Science and Artificial Intelligence Laboratory. In his free time he is engaged with climate mitigation advocacy in South Tyrol as one of the founders of the large civil society alliance Climate Action South Tyrol, through which he has garnered much experience with policy work and a solid domain expertise on climate change and sustainability. Currently he lives in Bressanone/Brixen, Italy.

If you are intrigued about what we do, reach out to learn more: david.hofmann@mytum.de