Narrow Results

Learning is a key component of firm upgrading in emerging economies, and China is no exception to this. Studies have identified, among others, two critical mechanisms that facilitate learning: (1) connections with supportive local governments that enhance access to resources or publicly funded knowledge and (2) connections to co-located foreign multinational enterprises (MNEs) that enhance access to advanced knowledge and capabilities. However, previous studies on the effects of these connections on learning and innovation have had contradictory results. In this study, we develop a model of firm innovation capabilities based on regional differences in firms’ dependence on government and MNEs. Using a sample of 715 indigenous firms from the three historically dominant economic regions in China, we find that the effects of government and MNE ties on local firms’ learning and innovation performance vary depending on the historically dominant dependency patterns in the region.

Research on the management of innovation has been highly fragmented, and to a large extent non-cumulative. Much of the research has been conducted within three separate disciplines, with relatively little overlap or interaction: the management of research and development or technology; new product development and marketing; and organisational development and change. In this paper, we identify a number of emergent themes which have the potential to integrate these diverse streams of research, and result in a more comprehensive model of the innovation process: complexity, networks and learning. We argue that the innovation process is inherently complex, and therefore we need better characterisations of the technological, market and organisational contingencies which affect the opportunity for innovation. With growing complexity, the focus shifts from competencies based on internal assets such as R&D activities and intellectual property, to the position of a firm within an innovation network and competencies based on its relationships with other organisations. Finally, too much research has been pre-occupied with how firms develop and exploit narrow competencies based on prior experience, rather than how firms acquire new competencies. A focus on organisational learning may provide a richer explanation of the organisational factors which affect the acquisition of new technological and market knowledge.

A considerable number of studies have been assembled over the last decade on the management of the R&D/marketing interface in product innovation. Most of these studies focus on the R&D/marketing interface as a self-contained unit of analysis, offering little explanation of the interface's contribution to a firm's competence building in ways essential to innovation success. This paper is based upon research that demonstrates that the importance of the R&D/marketing interface lies in its dynamic capability in influencing the direction of product development projects towards enhancing existing, or building new, competencies. The case study results show that the shared tasks performed by R&D and marketing departments are concentrated in three areas, i.e. corporate conceptual development (CCD), product conceptual development (PCD) and project implementation (Ip). The results reveal that the performance of the cross-functional team in general, and the R&D/marketing interface in particular, during a project's implementation, is heavily dependent on earlier activities in the areas of CCD and PCD. The former usually involves a sustained period of company-wide strategic preparation, which may or may not be directly targetted at a specific project, whilst the latter refers to previous co-operative experience at the project level. The evidence shows that, even when top management attempts to build an instant platform (e.g. by means of heavyweight project management), in the absence of such earlier activities, the effectiveness of this kind of platform has been far from satisfactory, thus pinpointing the vital importance of learning-before-doing in the innovation process.

Dealing with complex and uncertain environments requires a steady stream of innovation as well as occasional radical transition. Developing mechanisms to secure such continuous improvement (CI) is seen as a major strategic priority for many organisations. In particular, it raises the challenge of increasing employee involvement in the innovation process and of mobilising widespread problem-solving and learning behaviour. This paper reports on a major five-year research programme in the United Kingdom which explores the implementation of high involvement incremental innovation activities. It presents a reference model developed in this work for helping position and guide organisations in their implementation of CI.

This paper explores the evolution of environmental assessment (EA) debates over the last decade within the South African context as reflected in the proceedings of the annual International Association for Impact Assessment, South African chapter (IAIAsa) conferences between 1997 and 2008. Retrospective analysis is important to ensure that the profession avoids unlearning key lessons, keeps and gains perspective, builds the knowledge base and plans for the future. The analysis involved a review of 472 papers presented at these conferences. The results suggest that debates have shifted away from concerns with quality and application of environmental assessment towards serious questions about effectiveness and the value that environmental assessment is adding. It is clear that the profession is currently going through a period of intense introspection, questioning the need for and contribution of EA.

This paper establishes best practices for community-based environmental assessment (CBEA) in Kenya and Tanzania and examines what participants in community-centered approaches to environmental assessment have learned. Three CBEA cases involving water supply projects were studied using interview methods and action research. Findings show that best practices for encouraging meaningful community involvement include providing access and adequate notice to participants, fairer cost sharing, broader representation of women and youth, participant understanding of the CBEA facilitator and culturally appropriate sharing of findings. Learning outcomes attributable to the CBEA process included technical skills for erosion control, new information about environmental assessment (EA) regulations and shared values of environmental sustainability and community unity. An application of selected best practice approaches in a test case, in order to encourage greater participation and learning, had mixed success. For example, attempts at providing early notice still resulted in it being far too late for most participants and only about one-third of the participants were women. However, a pictograph functioned as an effective tool for reporting CBEA results to the community and demonstrating learning outcomes.

In the last decade, the emphasis of regional environmental assessment (EA) has shifted away from simply project approval towards facilitating environmental governance by accommodating heterogeneous stakeholders and emphasising relationship building across diverse institutions. However, there are very few advanced regional EA cases that may be studied to understand how practice has evolved and the implications for regional environmental governance. This paper characterises and assesses the interactions among the members of the Crown of the Continent Managers Partnership (CMP), whereby individuals with planning, policy-making, and EA roles attempted to implement an adaptive approach to regional cumulative effects assessment. Twelve in-depth, semi-structured interviews with key stakeholders provide data used in the investigation. The analysis demonstrates opportunities for an approach to regional EA that facilitates environmental governance through collective visioning, innovative leadership, learning from failure, and collaborative science and management. Lessons from the CMP are relevant internationally to jurisdictions seeking to implement regional EA via multi-disciplinary, multi-jurisdictional partnerships.

In this study, we revisit the well-known notion of fuzzy state machines and discuss their development through learning. The systematic development of fuzzy state machines has not been pursued as intensively as it could have been expected from the breadth of the possible usage of them as various modelling platforms. We concentrate on the generalization of the well known architectures exploited in Boolean system synthesis, namely Moore and Mealy machines and show how these can be implemented in terms of generic functional modules such as fuzzy JK flip-flops and fuzzy logic neurons (AND and OR neurons) organized in the form of logic processors. It is shown that the design of the fuzzy state machines can be accomplished through their learning. The detailed learning algorithm is presented and illustrated with a series of numeric examples. The study reveals an interesting option of constructing digital systems through learning: the original problem is solved in the setting of fuzzy state machines and afterwards "binarised" into the two-valued format realized via the standard digital hardware.

This paper investigates some central issues of monetary policy by offering a model in which a central bank tries to stabilize fluctuations in aggregate output and inflation in an adaptive complex economy. We resort to evolutionary algorithms to model the central bank behaviour under discretion, and confront the efficiency of discretion with the choice of full commitment to a fixed rule.

The brain has evolved to enable organisms to survive in a complicated and dynamic world. Its operation is based upon a priori models of the environment which are adapted, during learning, in response to new and changing stimuli. The same qualities that make biological learning mechanisms ideal for organisms make their underlying mathematical algorithms ideal for certain technological applications, especially those concerned with understanding the physical processes giving rise to complicated data sets. In this paper, we offer a mathematical model for the underlying mechanisms of biological learning, and we show how this mathematical approach to learning can yield a solution to the problem of imaging time-varying objects from X-ray computed tomographic (CT) data. This problem relates to several practical aspects of CT imaging including the correction of motion artifacts caused by patient movement or breathing.

This paper explores a possible cause of persistence in stock return volatility. Artificial stock markets are examined with different learning mechanisms, i.e. imitative and experiential learning. The simulation result shows that an economy with imitative learning gives rise to persistence of return volatility while an experiential learning economy does not. We find that volatility becomes persistent as investors learn through imitating the prediction methods of others. Imitation is crucial to producing the persistence in stock return volatility.

This paper studies the problem of accumulating heterogeneous capital goods in an economy with imperfect markets populated by boundedly rational agents. It relaxes classical assumptions about information and cognition. The agents are not capable of computing an equilibrium path to steady state. Agents discover prices by interacting with each other. The economy accumulates a near-optimal mix of capital goods. The structure of interactions between agents filters their behavior in such a way that limited rationality at the micro-level does not translate to grossly inefficient outcomes at the macro-level.

Extensive literature has shown that games provide engaging, dynamic, and authentic learning contexts. An understanding of how learning takes place while gaming can inform the design of effective educational games and aid their integration into contemporary classrooms. This study used inductive methods to provide a detailed description of the use of video games for learning in a school setting. Results demonstrate that learning occurred across multiple levels and multiple granularities, and can be triggered by particular cues in the game or social environment. Characteristics of the most frequently occurring instances of learning are discussed. Results of this study suggest great potential for the use of games in education for learning, and can inform future game design.

The goal of this research is to study how to augument mobile learning by applying Web page adaptation techniques. In this paper, we present a case study of how we applied Web page adaptation to facilitate mobile learning on the Blackboard Learning System. Without requiring different versions of the original learning materials, our research provides automatic delivery and presentation of adaptive Web-based learning materials based on students' receiving profiles. Experimental results demonstrate that our method provides effective and efficient delivery of Web-based learning material over the mobile Internet, which results in an extension of the time and space of learning, as well as encouraging and facilitating collaboration among students.

A need exists for intuitive hand gesture machine interaction in which the machine not only recognizes gestures, but also the human feels comfortable and natural in their execution. The gesture vocabulary design problem is rigorously formulated as a multi-objective optimization problem. Psycho-physiological measures (intuitiveness, comfort) and gesture recognition accuracy are taken as the multi-objective factors. The hand gestures are static and recognized by a vision based fuzzy c-means classifier. A meta-heuristic approach decomposes the problem into two sub-problems: finding the subsets of gestures that meet a minimal accuracy requirement, and matching gestures to commands to maximize the human factors objective. The result is a set of Pareto optimal solutions in which no objective may be increased without a concomitant decrease in another. Several solutions from the Pareto set are selected by the user using prioritized objectives. Software programs are developed to automate the collection of intuitive and stress indices. The method is tested for a simulated car — maze navigation task. Validation tests were conducted to substantiate the claim that solutions that maximize intuitiveness, comfort, and recognition accuracy performance measures can be used as proxies for the minimization task time objective. Learning and memorability were also tested.

In this paper, we propose a model describing the interaction between two species: a plant population that gets pollinated by an insect population. We assume the plant population is divided into two groups: the first group in mutualistic relationship with the insect and the second group attracting the insects while deceiving them and not delivering any reward. In addition, we assume that the insect population reduces the number of visits to the plants after several unsuccessful visits. We are interested in the conditions for the coexistence of both species, especially in the appearance of damped or sustained oscillations. We focus the analysis on the parameters that measure the balance among deceit, the benefit that the insect gets from the plant, and the learning by the pollinators. We are especially interested in analyzing the effect of learning by the insect population due to unsuccessfully visiting the deceiving plants.

This article argues that conscious attention exists not so much for selecting an immediate action as for using the current task to focus specialized learning for the action-selection mechanism(s) and predictive models on tasks and environmental contingencies likely to affect the conscious agent. It is perfectly possible to build this sort of a system into machine intelligence, but it would not be strictly necessary unless the intelligence needs to learn and is resource-bounded with respect to the rate of learning versus the rate of relevant environmental change. Support for this theory is drawn from scientific research and AI simulations. Consequences are discussed with respect to self-consciousness and ethical obligations to and for AI.

Development is a process that is full of uncertainties, and even more so is the process of economic transition. Because of uncertainties and country specificity, development must be a process of learning, selective adaptation, and industrial upgrading. This paper attempts to distill lessons from China's reform and opening up process and investigate the underlying reasons behind China's success in trade integration and economic growth. From its beginnings with home-grown and second-best institutions, China has embarked on a long journey of reform, experimentation, and learning by doing. It is moving from a comparative advantage-defying strategy to a comparative advantage-following strategy. The country is catching up quickly through augmenting its factor endowments and upgrading industries; but this has been only partially successful. Although China is facing several difficult challenges — including rising inequality, an industrial structure that is overly capital- and energy-intensive, and related environmental degradation — it is better positioned to tackle them now than it was 30 years ago. This paper reviews the drivers behind China's learning and trade integration and provides both positive and negative lessons for developing countries with diverse natural endowments, especially those in Sub-Saharan Africa.

We present a model of technological adaptation in response to a change in climate conditions. The main feature of the model is that new technologies are not just risky, but also ambiguous. Pessimistic agents are thus averse to adopting a new technology. Learning is induced by optimists, who are willing to try out technologies about which there is little evidence available. We show that both optimists and pessimists are crucial for a successful adaptation. While optimists provide the public good of information which gives pessimists an incentive to innovate, pessimists choose the new technology persistently in the long-run which increases the average returns for the society. Hence, the optimal share of optimists in the society is strictly positive. When the share of optimists in the society is too low, innovation is slow and the obtained steady-state is inefficient. We discuss two policies which can potentially alleviate this inefficiency: Subsidies and provision of additional information. We show that if precise and relevant information is available, pessimists would be willing to pay for it and consequently adopt the new technology. Hence, providing information might be a more efficient policy, which is both self-financing and results in better social outcomes.

Low-probability, high-impact risks are critical features of climate change economics; however, there are many unanswered policy and modeling questions about the implications of fat-tailed uncertainty. This paper examines the impact of fat-tailed uncertainty about the climate sensitivity on abatement decisions using a sequential decision-making framework. The results demonstrate how policy prescriptions from integrated assessment models are sensitive to the specifications of uncertainty, learning, and damages. Fat tails alone do not merit immediate and stringent mitigation but require strongly convex damages and slow learning. The analysis illustrates the potential value of midcourse corrections on reducing consumption risks imposed by uncertain damages from climate change and focuses attention on the dynamics of learning.