The missing ingredient:inter-municipal cooperation and central-local relations in the UK

The thrust of the argument presented in this chapter is that inter-municipal cooperation (IMC) in the United Kingdom reflects local government's constitutional position and its exposure to the exigencies of Westminster (elected central government) and Whitehall (centre of the professional civil service that services central government). For the most part councils are without general powers of competence and are restricted in what they can do by Parliament. This suggests that the capacity for locally driven IMC is restricted and operates principally within a framework constructed by central government's policy objectives and legislation and the political expediencies of the governing political party. In practice, however, recent examples of IMC demonstrate that the practices are more complex than this initial analysis suggests. Central government may exert top-down pressures and impose hierarchical directives, but there are important countervailing forces. Constitutional changes in Scotland and Wales have shifted the locus of central- local relations away from Westminster and Whitehall. In England, the seeding of English government regional offices in 1994 has evolved into an important structural arrangement that encourages councils to work together. Within the local government community there is now widespread acknowledgement that to achieve the ambitious targets set by central government, councils are, by necessity, bound to cooperate and work with other agencies. In recent years, the fragmentation of public service delivery has affected the scope of IMC. Elected local government in the UK is now only one piece of a complex jigsaw of agencies that provides services to the public; whether it is with non-elected bodies, such as health authorities, public protection authorities (police and fire), voluntary nonprofit organisations or for-profit bodies, councils are expected to cooperate widely with agencies in their localities. Indeed, for projects such as regeneration and community renewal, councils may act as the coordinating agency but the success of such projects is measured by collaboration and partnership working (Davies 2002). To place these developments in context, IMC is an example of how, in spite of the fragmentation of traditional forms of government, councils work with other public service agencies and other councils through the medium of interagency partnerships, collaboration between organisations and a mixed economy of service providers. Such an analysis suggests that, following changes to the system of local government, contemporary forms of IMC are less dependent on vertical arrangements (top-down direction from central government) as they are replaced by horizontal modes (expansion of networks and partnership arrangements). Evidence suggests, however that central government continues to steer local authorities through the agency of inspectorates and regulatory bodies, and through policy initiatives, such as local strategic partnerships and local area agreements (Kelly 2006), thus questioning whether, in the case of UK local government, the shift from hierarchy to network and market solutions is less differentiated and transformation less complete than some literature suggests. Vertical or horizontal pressures may promote IMC, yet similar drivers may deter collaboration between local authorities. An example of negative vertical pressure was central government's change of the systems of local taxation during the 1980s. The new taxation regime replaced a tax on property with a tax on individual residency. Although the community charge lasted only a few years, it was a highpoint of the then Conservative government policy that encouraged councils to compete with each other on the basis of the level of local taxation. In practice, however, the complexity of local government funding in the UK rendered worthless any meaningful ambition of councils competing with each other, especially as central government granting to local authorities is predicated (however imperfectly) on at least notional equalisation between those areas with lower tax yields and the more prosperous locations. Horizontal pressures comprise factors such as planning decisions. Over the last quarter century, councils have competed on the granting of permission to out-of-town retail and leisure complexes, now recognised as detrimental to neighbouring authorities because economic forces prevail and local, independent shops are unable to compete with multiple companies. These examples illustrate tensions at the core of the UK polity of whether IMC is feasible when competition between local authorities heightened by local differences reduces opportunities for collaboration. An alternative perspective on IMC is to explore whether specific purposes or functions promote or restrict it. Whether in the principle areas of local government responsibilities relating to social welfare, development and maintenance of the local infrastructure or environmental matters, there are examples of IMC. But opportunities have diminished considerably as councils lost responsibility for services provision as a result of privatisation and transfer of powers to new government agencies or to central government. Over the last twenty years councils have lost their role in the provision of further-or higher-education, public transport and water/sewage. Councils have commissioning power but only a limited presence in providing housing needs, social care and waste management. In other words, as a result of central government policy, there are, in practice, currently far fewer opportunities for councils to cooperate. Since 1997, the New Labour government has promoted IMC through vertical drivers and the development; the operation of these policy initiatives is discussed following the framework of the editors. Current examples of IMC are notable for being driven by higher tiers of government, working with subordinate authorities in principal-agent relations. Collaboration between local authorities and intra-interand cross-sectoral partnerships are initiated by central government. In other words, IMC is shaped by hierarchical drivers from higher levels of government but, in practice, is locally varied and determined less by formula than by necessity and function. © 2007 Springer.

The curious absence of inter-municipal co-operation in England

In Europe local authorities often work with their neighbouring municipalities, whether to address a specific task or goal or through the course of regular policy making and implementation. In England, however, inter-municipal co-operation (IMC) is less common. Councils may work with service providers from the private and non-profit sectors but less often with neighbouring local authorities. Why this is the case may be explained by a number of historical and policy factors that often encourage councils to compete, rather than to work collaboratively with each other. The present government has encouraged councils to work in partnership with other organizations but there are few examples of increased horizontal cooperation between local authorities. Instead the prevailing model remains fixed on vertical co-working predicated on a principal-agent relationship between higher and lower tiers of government.

Knowledge maintenance and the frame problem

Knowledge maintenance is a major challenge for both knowledge management and the Semantic Web. Operating over the Semantic Web, there will be a network of collaborating agents, each with their own ontologies or knowledge bases. Change in the knowledge state of one agent may need to be propagated across a number of agents and their associated ontologies. The challenge is to decide how to propagate a change of knowledge state. The effects of a change in knowledge state cannot be known in advance, and so an agent cannot know who should be informed unless it adopts a simple ‘tell everyone – everything’ strategy. This situation is highly reminiscent of the classic Frame Problem in AI. We argue that for agent-based technologies to succeed, far greater attention must be given to creating an appropriate model for knowledge update. In a closed system, simple strategies are possible (e.g. ‘sleeping dog’ or ‘cheap test’ or even complete checking). However, in an open system where cause and effect are unpredictable, a coherent cost-benefit based model of agent interaction is essential. Otherwise, the effectiveness of every act of knowledge update/maintenance is brought into question.

Agent Based Models of Competition and Collaboration

Swarm intelligence is a popular paradigm for algorithm design. Frequently drawing inspiration from natural systems, it assigns simple rules to a set of agents with the aim that, through local interactions, they collectively solve some global problem. Current variants of a popular swarm based optimization algorithm, particle swarm optimization (PSO), are investigated with a focus on premature convergence. A novel variant, dispersive PSO, is proposed to address this problem and is shown to lead to increased robustness and performance compared to current PSO algorithms. A nature inspired decentralised multi-agent algorithm is proposed to solve a constrained problem of distributed task allocation. Agents must collect and process the mail batches, without global knowledge of their environment or communication between agents. New rules for specialisation are proposed and are shown to exhibit improved eciency and exibility compared to existing ones. These new rules are compared with a market based approach to agent control. The eciency (average number of tasks performed), the exibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved eciency and robustness. Evolutionary algorithms are employed, both to optimize parameters and to allow the various rules to evolve and compete. We also observe extinction and speciation. In order to interpret algorithm performance we analyse the causes of eciency loss, derive theoretical upper bounds for the eciency, as well as a complete theoretical description of a non-trivial case, and compare these with the experimental results. Motivated by this work we introduce agent "memory" (the possibility for agents to develop preferences for certain cities) and show that not only does it lead to emergent cooperation between agents, but also to a signicant increase in efficiency.

The effect of load on agent-based algorithms for distributed task allocation

Multi-agent algorithms inspired by the division of labour in social insects and by markets, are applied to a constrained problem of distributed task allocation. The efficiency (average number of tasks performed), the flexibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved efficiency and robustness. We employ nature inspired particle swarm optimisation to obtain optimised parameters for all algorithms in a range of representative environments. Although results are obtained for large population sizes to avoid finite size effects, the influence of population size on the performance is also analysed. From a theoretical point of view, we analyse the causes of efficiency loss, derive theoretical upper bounds for the efficiency, and compare these with the experimental results.

Simulation in the supply chain context:matching the simulation tool to the problem

The supply chain can be a source of competitive advantage for the firm. Simulation is an effective tool for investigating supply chain problems. The three main simulation approaches in the supply chain context are System Dynamics (SD), Discrete Event Simulation (DES) and Agent Based Modelling (ABM). A sample from the literature suggests that whilst SD and ABM have been used to address strategic and planning problems, DES has mainly been used on planning and operational problems., A review of received wisdom suggests that historically, driven by custom and practice, certain simulation techniques have been focused on certain problem types. A theoretical review of the techniques, however, suggests that the scope of their application should be much wider and that supply chain practitioners could benefit from applying them in this broader way.

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

An efficient knowledge transfer solution to a novel SMDP formalization of a broker's decision problem

This paper introduces a new technique for optimizing the trading strategy of brokers that autonomously trade in re- tail and wholesale markets. Simultaneous optimization of re- tail and wholesale strategies has been considered by existing studies as intractable. Therefore, each of these strategies is optimized separately and their interdependence is generally ignored, with resulting broker agents not aiming for a glob- ally optimal retail and wholesale strategy. In this paper, we propose a novel formalization, based on a semi-Markov deci- sion process (SMDP), which globally and simultaneously op- timizes retail and wholesale strategies. The SMDP is solved using hierarchical reinforcement learning (HRL) in multi- agent environments. To address the curse of dimensionality, which arises when applying SMDP and HRL to complex de- cision problems, we propose an ecient knowledge transfer approach. This enables the reuse of learned trading skills in order to speed up the learning in new markets, at the same time as making the broker transportable across market envi- ronments. The proposed SMDP-broker has been thoroughly evaluated in two well-established multi-agent simulation en- vironments within the Trading Agent Competition (TAC) community. Analysis of controlled experiments shows that this broker can outperform the top TAC-brokers. More- over, our broker is able to perform well in a wide range of environments by re-using knowledge acquired in previously experienced settings.