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Non-equilibrium physical systems, be they biological or otherwise, are powered by differences in intensive thermodynamic variables, which result in flows of matter and energy through the system. This thesis is concerned with the response of physical systems and ecosystems to complex types of boundary conditions, where the flows and intensive variables are constrained to be functions of one another. I concentrate on what I call negative feedback boundary conditions, where the potential difference is a decreasing function of the flow. Evidence from climate science suggests that, in at least some cases, systems under these conditions obey a principle of maximum entropy production. Similar extremum principles have been suggested for ecosystems. Building on recent work in theoretical physics, I present a statisticalmechanical argument in favour of this principle, which makes its range of application clearer. Negative feedback boundary conditions can arise naturally in ecological scenarios, where the difference in potential is the free-energy density of the environment and the negative feedback applies to the ecosystem as a whole. I present examples of this, and develop a simple but general model of a biological population evolving under such conditions. The evolution of faster and more efficient metabolisms results in a lower environmental energy density, supporting an argument that simpler metabolisms could have persisted more easily in early environments. Negative feedback conditions may also have played a role in the origins of life, and specifically in the origins of individuation, the splitting up of living matter into distinct organisms, a notion related to the theory of autopoiesis. I present simulation models to clarify the concept of individuation and to back up this hypothesis. Finally I propose and model a mechanism whereby systems can grow adaptively under positive reinforcement boundary conditions by the canalisation of fluctuations in their structure.

International audience; This research introduces a multi-modeling approach to the growth of transportation networks. More precisely, we imple- ment and compare several models, based on biological net- work growth, cost-benefit rules, and gravity potential break- down. The resulting multi-modeling framework is calibrated on observed topological data for the European road network. We show that different heuristics are complementary to cover the feasible topological space and that all are necessary to approach existing configurations, what suggests the superpo- sition of corresponding processes in territorial systems.

Dung beetles can perform impressive multiple motor behaviors using their legs. The behaviors include walking and rolling a large dung ball on different terrains, e.g., level ground and different slopes. To achieve such complex behaviors for legged robots, we propose here a modular neural controller for dung beetle-like locomotion and object transportation behaviors of a dung beetle-like robot. The modular controller consists of several modules based on three generic neural modules. The main modules include 1) a neural oscillator network module (as a central pattern generator (CPG)), 2) a neural CPG postprocessing module (PCPG), 3) a velocity regulating network module (VRN). The CPG generates basic rhythmic patterns. The patterns are first shaped by the PCPG and their amplitudes as well as phases are later modified by the VRN to obtain proper motor patterns for locomotion and object transportation. Combining all these neural modules, we can achieve different motor patterns for four different actions which are forward walking, backward walking, level-ground ball rolling, and sloped-ground ball rolling. All these actions can be activated by four input neurons. The experimental results show that the simulated dung beetle-like robot can robustly perform the actions. The average forward speed is 0.058 cm/s and the robot is able to roll a large ball (about 3 times of its body height and 2 times of its weight) up different slope angles up to 25 degrees.

Are we evolving into a new Post-Industrial epoch driven by the emerging living and intelligent technologies, spearheaded by the digital revolution? Or are we just entering a new period in the Industrial epoch, where we don’t need to radically update our institutions, the market and our cultural narratives? Using an evolutionary perspective and time series analysis we seek to investigate these questions. We report a study of the long term dynamics of the economy across 28 countries and across different socio-technical epochs, some dating back to 1270, while others starting at the onset of the Industrial Revolution. Using standard time series analysis methods we report overall growth dynamics as well as rhythmic phenomena in the growth patterns after the onset of the Industrial Revolution. Our main data sources include the Maddison Project [4], Edvinson [1], Our Wold in Data [7] and updated data references in the cited literature. Assuming these evolutionary changes of our societies are driven by fundamental inventions that are capitalized through innovation when the socio-economic climate is appropriate (Mensch) [5], we can use a simple mathematical model (Mosekilde et al.) [6] to interpret and to better understand the underlying dynamics for the empirical data as well as provide informed, tentative forcasts for the future (Rosenlyst et al.) [9].