Policy, design and management: the in-vivo laboratory for the science of complex socio-technical systems

Complex systems scientists cannot by themselves perform experiments on complex socio-technical systems. The best they can do is to perform experiments alongside policy makers who are constantly engaged in experiments as the design and manage the systems the systems for which they are responsible. In this context the nature of prediction in the implementation of real systems is much more complicated than it is in traditional science. The goals identified by policymakers change through time, and this is usually managed through the design and management processes. The combination of policy and design is the opportunity – the only opportunity – for complex systems scientists to engage and to be allowed to be involved in in-vivo experiments in large socio-technical systems. In turn this opens up new methodological approaches and questions for the science of complex systems

Hypernetworks for reconstructing the dynamics of multilevel systems

Networks are fundamental for reconstructing the dynamics of many systems, but have the drawback that they are restricted to binary relations. Hypergraphs extend relational structure to multi-vertex edges, but are essentially set-theoretic and unable to represent essential structural properties. Hypernetworks are a natural multidimensional generalisation of networks, representing n-ary relations by simplices with n vertices. The assembly of vertices to make simplices is key for moving between levels in multilevel systems, and integrating dynamics between levels. It is argued that hypernetworks are necessary, if not sufficient, for reconstructing the dynamics of multilevel complex systems

Embracing <i>n</i>-ary Relations in Network Science

Most network scientists restrict their attention to relations between pairs of things, even though most complex systems have structures and dynamics determined by n-ary relation where n is greater than two. Various examples are given to illustrate this. The basic mathematical structures allowing more than two vertices have existed for more than half a century, including hypergraphs and simplicial complexes. To these can be added hypernetworks which, like multiplex networks, allow many relations to be defined on the vertices. Furthermore, hypersimplices provide an essential formalism for representing multilevel part-whole and taxonomic structures for integrating the dynamics of systems between levels. Graphs, hypergraphs, networks, simplicial complex, multiplex network and hypernetworks form a coherent whole from which, for any particular application, the scientist can select the most suitable

Dynamic Structures for Evolving Tactics and Strategies in Team Robotics

The autonomous robot systems of the future will be teams of robots with complementary specialisms. At any instant robot interactions determine relational structures, and sequences of these structures describe the team dynamics as trajectories through space and time. These structures can be represented in algebraic forms that are realizable as dynamic multilevel data structures within individual robots, as the basis of emergent team data structures. Such formalisms are necessary for robots to learn new individual and collective behaviours. The theory is illustrated by the example of robot soccer where robot interactions create structures and trajectories essential to the evolution of new tactics and strategies in a changing environment

Design interventions, prediction and science in the sustainable transition of large, complex systems,

The way that human beings live and consume the natural and environmental resources of the planet are not sustainable. Sustainability involves changes in individual beliefs, expectations, values and behaviours at the microlevel, changes in policy at the macrolevel of governments, and changes in the design of objects, social organisations and structures at the mesolevels. Design for sustainability has a big challenge: we need a ninety percent gain in energy and material efficiencies over the next thirty years. Bottom-up and top-down design and policy interventions are needed at all levels. These multilevel dynamics interact in ways not understood by conventional social and natural science: human beings and their physical environment form a bewilderingly complex multilevel system of systems of systems. The science of complex systems must, necessarily, conduct experiments through policy: scientists do not have the mandate or the money to perform large interventionist experiments. Policy can be construed as designing the future. Thus complex systems are entangled in both policy and design. We conclude that (i) the design professions impact on the community at all levels, and that 'good? design at any level is relative to design at all other levels, and the emergent design of the whole, (ii) design, complex systems science and policy must all work together to create a sustainable future, and (iii) policy and complex systems science must progress through a designerly way of thinking to achieve sustainable design coherently applied at all levels in the complex multilevel system of humankind living on planet earth in the decades, centuries and millennia of the future. This view puts design and complexity science at the centre of policy for sustainability

Representing Patterns of autonomous agent dynamics in multi-robot systems

It is proposed that vocabularies for representing complex systems with interacting agents have a natural lattice hierarchical structure. We investigate this for the example of simulated robot soccer, using data taken from the RoboCup simulation competition. Lattice hierarchies provide symbolic representations for reasoning about systems at appropriate levels. We note the difference between relational constructs being human supplied versus systems that abstract their own constructs autonomously. The lattice hierarchical representation underlies both

Preserving, Protecting, and Expanding Affordable Housing: A Policy Toolkit for Public Health

Resurgent interest in urban living is helping to revive neighborhoods in numerous American cities, stabilizing populations and sometimes beginning to reverse previous declines. One consequence of the influx of residents is new public and private investment in amenities such as parks, bike paths and grocery and other stores. However, new demand and investment can also drive up housing costs. That's particularly true in areas that have traditionally been affordable to low- and moderate-income individuals and families, notes a report by ChangeLab Solutions. With support from Kresge's Health Program, the nonprofit ChangeLab Solutions developed a guide to help practitioners and community advocates preserve and expand the number of affordable rental housing options in high-demand neighborhoods. The guide is aimed at practitioners who work at the intersection of housing and health, an area of increasing emphasis for Kresge's Health Program.The ChangeLab team has produced a policy toolkit with information on housing market trends and research on the links between rising housing costs and poor health outcomes. The toolkit identifies strategies to help ensure that households of all incomes have housing options in the areas where they want to live. It covers six policy areas: preservation, protection, inclusion, revenue generation, incentives and property acquisition