Evolutionary economics and regional policy

The principal objective of this paper is to formulate some possible links between evolutionary economics and regional policy, a topic that has not (yet) been covered by the literature. We firstly give a brief overview of some issues of regional policy, conceived as a strategy to influence the spatial matrix of economic development. Then, we outline what we take to be the essential arguments and components of evolutionary economics. More in particular, we focus attention on the economic foundation of technology policy from an evolutionary perspective, and how this deviates from the so-called "equilibrium" rationale. Then, we examine in what way evolutionary insights may be helpful for regional policy matters. Our main emphasis is to investigate the degrees fo freedom policy makers may have to determine the future development of regions. When evolutionary mechanisms like chance and increasing returns are mainly involved in the spatial formation of new economic activities, there are several, but quite contradictory, options for policy makers. On the one hand, the importance of early chance events implies that multiple potential outcomes of location are quite thinkable. This is a principal problem for regional policy because new development paths can not be planned or even foreseen. On the other hand, policy makers may have a role to play here. Since space exercises only a minor influence on the location of new economic activities, there is room for policy makers to act and to build-up a favourable local environment. In this respect, urbanisation economies may offer advantages of flexibility secured by a diversity of activities which tends to prevent a process of negative lock-in. When evolutionary mechanisms like selection and path dependency are crucial for the geography of innovation, policy makers are expected to have more influence on the spatial pattern of innovation. In such circumstances, new variety is regarded as strongly embedded in its surrounding environment: the local environment acts as a sort of selection mechanism because it may, or may not, provide conditions favourable to meet the new requirements of new technology. Adaptation to change is largely constrained by the boundaries of the spatial matrix laid down in the past: only minor modifications tend to take place and do not undermine the logic of the spatial system.

Adaptive Governance and Evolving Solutions to Natural Resource Conflicts

New Zealand is facing increasing challenges in managing natural resources (land, freshwater, marine space and air quality) under pressures from domestic (population growth, agricultural intensification, cultural expectations) and international (climate change) sources. These challenges can be described in terms of managing ‘wicked problems’; i.e. problems that may not be understood fully until they have been solved, where stakeholders have different world views and frames for understanding the problem, the constraints affecting the problem and the resources required to solve it change over time, and no complete solution is ever actually found. Adaptive governance addresses wicked problems through a framework to engage stakeholders in a participative process to create a long term vision. The vision must identify competing goals and a process for balancing them over time that acknowledges conflicts cannot always be resolved in a single lasting decision. Circumstances, goals and priorities can all vary over time and by region. The Resource Management Act can be seen as an adaptive governance structure where frameworks for resources such as water may take years to evolve and decades to fully implement. Adaptive management is about delivery through an incremental/experimental approach, limits on the certainty that governments can provide and stakeholders can demand, and flexibility in processes and results. In New Zealand it also requires balancing central government expertise and resources, with local authorities which can reflect local goals and knowledge, but have varying resources and can face quite distinct issues of widely differing severity. It is important to signal the incremental, overlapping, iterative and time-consuming nature of the work involved in developing and implementing adaptive governance and management frameworks. Managing the expectations of those involved as to the nature of the process and their role in it, and the scope and timing of likely outcomes, is key to sustaining participation.Adaptive capacity; governance; resilience

Endogenous space in the Net era

Libre Software communities are among the most interesting and advanced socio-economic laboratories on the Net. In terms of directions of Regional Science research, this paper addresses a simple question: “Is the socio-economics of digital nets out of scope for Regional Science, or might the latter expand to a cybergeography of digitally enhanced territories ?” As for most simple questions, answers are neither so obvious nor easy. The authors start drafting one in a positive sense, focussing upon a file rouge running across the paper: endogenous spaces woven by socio-economic processes. The drafted answer declines on an Evolutionary Location Theory formulation, together with two computational modelling views. Keywords: Complex networks, Computational modelling, Economics of Internet, Endogenous spaces, Evolutionary location theory, Free or Libre Software, Path dependence, Positionality.

Combining evolutionary algorithms and agent-based simulation for the development of urbanisation policies

Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. To help in these decision-making processes, this thesis provides an empirical study of using evolutionary approaches to solve sequential decision making problems under uncertainty in stochastic environments. To achieve this goal, this work is underpinned by developing a theoretical framework based on the economic model of Alonso and the associated methodology for modelling spatial and temporal urban growth, in order to better understand the complexity inherent in this kind of system and to generate and improve relevant knowledge for the urban planning community. The model was hybridised with cellular automata and agent-based model and extended to encompass green space planning based on urban cost and satisfaction. Monte Carlo sampling techniques and the use of the urban model as a surrogate tool were the two main elements investigated and applied to overcome the noise and uncertainty derived from dealing with future trends and expectations. Once the evolutionary algorithms were equipped with these mechanisms, the problem under consideration was defined and characterised as a type of adaptive submodular. Afterwards, the performance of a non-adaptive evolutionary approach with a random search and a very smart greedy algorithm was compared and in which way the complexity that is linked with the configuration of the problem modifies the performance of both algorithms was analysed. Later on, the application of very distinct frameworks incorporating evolutionary algorithm approaches for this problem was explored: (i) an ‘offline’ approach, in which a candidate solution encodes a complete set of decisions, which is then evaluated by full simulation, and (ii) an ‘online’ approach which involves a sequential series of optimizations, each making only a single decision, and starting its simulations from the endpoint of the previous run

Contemporary Innovation Policy and Instruments: Challenges and Implications

In this paper we review major theoretical (neoclassical economics, evolutionary, systemic and knowledge-based) insights about innovation and we analyse their implications for the characteristics of contemporary innovation policy and instruments. We show that the perspectives complement each other but altogether reveal the need to redefine the current general philosophy as well as the modes of operationalisation of contemporary innovation policy. We argue that systemic instruments ensuring proper organisation of innovation systems give a promise of increased rates and desired (more sustainable) direction of innovation.systemic instruments, innovation policy, innovation theory, policy mix, innovation system, sustainability