"Version: 20221201"--Title page verso.

Includes bibliographical references.

1. Entropy as missing information : from Shannon's information theory to Jaynes' maximum entropy principle -- 1.1. Information and its processing in biology, economics and physics -- 1.2. Uncertainty in communication systems : Shannon entropy -- 1.3. Entropy as missing information -- 1.4. Working with incomplete information : the principle of maximum entropy to find minimally prejudiced distributions

2. The synthesis of information theory and thermodynamics : Shannon entropy and Boltzmann entropy are the same thing -- 2.1. Basics of statistical physics -- 2.2. MaxEnt derivation of statistical mechanics -- 2.3. Converting information into energy : from Maxwell's demon to Landauer's eraser -- 2.4. Conclusion

3. Elements of physical biology : the Lotka-Volterra equations -- 3.1. The kinetic formulation of population dynamics -- 3.2. The Lotka-Volterra linear model for single-trophic communities -- 3.3. The statistical mechanics of populations -- 3.4. Conclusion -- Appendix A. Equilibrium stability in population ecology

4. Economics as physics, economics as biology -- 4.1. Economics as social physics, physics as Nature's economics -- 4.2. Neoclassical economics -- 4.3. Economics as biology, or evolutionary economics -- 4.4. Selection dynamics -- 4.5. Linking selection dynamics with ecology and physics -- 4.6. Innovation through mutations -- 4.7. Implementing evolution in economics -- 4.8. The 'Marshall problem' or a transdisciplinary synthetic perspective of economics

5. Inferring effective interaction matrices through MaxEnt -- 5.1. Working with imperfect information -- 5.2. The Lotka-Volterra maximum entropy interaction matrix -- 5.3. How good is the pairwise approximation?

6. Early warning indications of species crashes from effective intraspecific interactions in tropical forests -- 6.1. Background : diversity loss and early warning signals -- 6.2. Goal -- 6.3. Data -- 6.4. Estimating the interaction matrix through MaxEnt -- 6.5. Intraspecific competition interactions are enough to predict the trajectories of tree species -- 6.6. A new early warning signal -- 6.7. Conclusion, caveats and future developments

7. Modelling markets as ecosystems with the help of maximum entropy -- 7.1. Background : a short history of market modelling -- 7.2. Goal -- 7.3. Data -- 7.4. Modelling : replicator dynamics combined with pairwise maximum entropy or RDPME model -- 7.5. Model validation -- 7.6. Conclusion : balance, caveats, extensions and improvements -- Appendix A. A metric to measure the pace of change of the payoff matrix -- 8. Glossary.

This book aims at providing a unifying framework, based on Information Entropy and its maximization, to connect the phenomenology of evolutionary biology, community ecology, financial economics, and statistical physics.

For advanced UG or PG courses in dynamics of complex systems and information theory for students in physics, applied maths, biology, quantitative finance, econophysics, ecophysics, quantitative ecology, applied maths.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Hugo Fort is a Professor at the Physics Department of the Faculty of Sciences of the Republic University (Montevideo, Uruguay) and Head of the Complex System Group. He earned his PhD in physics from the Autonomous University of Barcelona in 1994 and his early research fields included quantum field theory and high energy physics.

Title from PDF title page (viewed on December 5, 2022).