Quantitative modelling of the human–Earth System a new kind of science?

The five grand challenges set out for Earth System Science by the International Council for Science in 2010 require a true fusion of social science, economics and natural science—a fusion that has not yet been achieved. In this paper we propose that constructing quantitative models of the dynamics of the human–Earth system can serve as a catalyst for this fusion. We confront well-known objections to modelling societal dynamics by drawing lessons from the development of natural science over the last four centuries and applying them to social and economic science. First, we pose three questions that require real integration of the three fields of science. They concern the coupling of physical planetary boundaries via social processes; the extension of the concept of planetary boundaries to the human–Earth System; and the possibly self-defeating nature of the United Nation’s Millennium Development Goals. Second, we ask whether there are regularities or ‘attractors’ in the human–Earth System analogous to those that prompted the search for laws of nature. We nominate some candidates and discuss why we should observe them given that human actors with foresight and intentionality play a fundamental role in the human–Earth System. We conclude that, at sufficiently large time and space scales, social processes are predictable in some sense. Third, we canvass some essential mathematical techniques that this research fusion must incorporate, and we ask what kind of data would be needed to validate or falsify our models. Finally, we briefly review the state of the art in quantitative modelling of the human–Earth System today and highlight a gap between so-called integrated assessment models applied at regional and global scale, which could be filled by a new scale of model

Piggybacking on an Autonomous Hauler: Business Models Enabling a System-of-Systems Approach to Mapping an Underground Mine

With ever-increasing productivity targets in mining operations, there is a growing interest in mining automation. In future mines, remote-controlled and autonomous haulers will operate underground guided by LiDAR sensors. We envision reusing LiDAR measurements to maintain accurate mine maps that would contribute to both safety and productivity. Extrapolating from a pilot project on reliable wireless communication in Boliden's Kankberg mine, we propose establishing a system-of-systems (SoS) with LIDAR-equipped haulers and existing mapping solutions as constituent systems. SoS requirements engineering inevitably adds a political layer, as independent actors are stakeholders both on the system and SoS levels. We present four SoS scenarios representing different business models, discussing how development and operations could be distributed among Boliden and external stakeholders, e.g., the vehicle suppliers, the hauling company, and the developers of the mapping software. Based on eight key variation points, we compare the four scenarios from both technical and business perspectives. Finally, we validate our findings in a seminar with participants from the relevant stakeholders. We conclude that to determine which scenario is the most promising for Boliden, trade-offs regarding control, costs, risks, and innovation must be carefully evaluated.Comment: Preprint of industry track paper accepted for the 25th IEEE International Conference on Requirements Engineering (RE'17

Process-Oriented Collective Operations

Distributing process-oriented programs across a cluster of machines requires careful attention to the effects of network latency. The MPI standard, widely used for cluster computation, defines a number of collective operations: efficient, reusable algorithms for performing operations among a group of machines in the cluster. In this paper, we describe our techniques for implementing MPI communication patterns in process-oriented languages, and how we have used them to implement collective operations in PyCSP and occam-pi on top of an asynchronous messaging framework. We show how to make use of collective operations in distributed processoriented applications. We also show how the process-oriented model can be used to increase concurrency in existing collective operation algorithms

Inverse Methods: a Powerful Tool for Evaluating Aerosol Data, Exemplified on Cases With Relevance for the Atmosphere and the Aerosol Climate Effect

For a complete description of a given aerosol, more than one parameter is necessary, e.g. parameters concerning size distribution, chemical composition, and particle morphology. On the other hand, most instruments measuring aerosol properties are sensitive mostly to one parameter, but cross-sensitive to others. These cross-sensitivities are often eliminated by assumptions during data evaluation, inducing systematic uncertainties in the results. The use of assumptions can be reduced by combining the information of several instruments on the same aerosol and using inverse methods for interpretation of the data. The presentation focuses on two application examples of these methods. The first example concerns a size distribution inversion algorithm that combines data from several instruments into one size distribution. The second example deals with an algorithm that retrieves the aerosol asymmetry parameter (with respect to particle scattering) from measurements of the aerosol absorption and spectral scattering and hemispheric backscattering coefficients, thereby providing a set of parameters that completely describes an aerosol with respect to its direct climate effect

Complex wave patterns in an effective reaction–diffusion model for chemical reactions in microemulsions

An effective medium theory is employed to derive a simple qualitative model of a pattern forming chemical reaction in a microemulsion. This spatially heterogeneous system is composed of water nanodroplets randomly distributed in oil. While some steps of the reaction are performed only inside the droplets, the transport through the extended medium occurs by diffusion of intermediate chemical reactants as well as by collisions of the droplets. We start to model the system with heterogeneous reaction–diffusion equations and then derive an equivalent effective spatially homogeneous reaction–diffusion model by using earlier results on homogenization in heterogeneous reaction–diffusion systems [ S. Alonso, M. Bär, and R. Kapral, J. Chem. Phys. 134, 214102 (2009)]. We study the linear stability of the spatially homogeneous state in the resulting effective model and obtain a phase diagram of pattern formation, that is qualitatively similar to earlier experimental results for the Belousov–Zhabotinsky reaction in an aerosol OT (AOT)-water-in-oil microemulsion [ V. K. Vanag and I. R. Epstein, Phys. Rev. Lett. 87, 228301 (2001)]. Moreover, we reproduce many patterns that have been observed in experiments with the Belousov–Zhabotinsky reaction in an AOT oil-in-water microemulsion by direct numerical simulations.Peer ReviewedPostprint (published version