Social learning in a multi-agent system

In a persistent multi-agent system, it should be possible for new agents to benefit from the accumulated learning of more experienced agents. Parallel reasoning can be applied to the case of newborn animals, and thus the biological literature on social learning may aid in the construction of effective multi-agent systems. Biologists have looked at both the functions of social learning and the mechanisms that enable it. Many researchers have focused on the cognitively complex mechanism of imitation; we will also consider a range of simpler mechanisms that could more easily be implemented in robotic or software agents. Research in artificial life shows that complex global phenomena can arise from simple local rules. Similarly, complex information sharing at the system level may result from quite simple individual learning rules. We demonstrate in simulation that simple mechanisms can outperform imitation in a multi-agent system, and that the effectiveness of any social learning strategy will depend on the agents' environment. Our simple mechanisms have obvious advantages in terms of robustness and design costs

Evaluating weaknesses of "perceptual-cognitive training" and "brain training" methods in sport: An ecological dynamics critique

The recent upsurge in "brain training and perceptual-cognitive training," proposing to improve isolated processes, such as brain function, visual perception, and decision-making, has created significant interest in elite sports practitioners, seeking to create an "edge" for athletes. The claims of these related "performance-enhancing industries" can be considered together as part of a process training approach proposing enhanced cognitive and perceptual skills and brain capacity to support performance in everyday life activities, including sport. For example, the "process training industry" promotes the idea that playing games not only makes you a better player but also makes you smarter, more alert, and a faster learner. In this position paper, we critically evaluate the effectiveness of both types of process training programmes in generalizing transfer to sport performance. These issues are addressed in three stages. First, we evaluate empirical evidence in support of perceptual-cognitive process training and its application to enhancing sport performance. Second, we critically review putative modularized mechanisms underpinning this kind of training, addressing limitations and subsequent problems. Specifically, we consider merits of this highly specific form of training, which focuses on training of isolated processes such as cognitive processes (attention, memory, thinking) and visual perception processes, separately from performance behaviors and actions. We conclude that these approaches may, at best, provide some "general transfer" of underlying processes to specific sport environments, but lack "specificity of transfer" to contextualize actual performance behaviors. A major weakness of process training methods is their focus on enhancing the performance in body "modules" (e.g., eye, brain, memory, anticipatory sub-systems). What is lacking is evidence on how these isolated components are modified and subsequently interact with other process "modules," which are considered to underlie sport performance. Finally, we propose how an ecological dynamics approach, aligned with an embodied framework of cognition undermines the rationale that modularized processes can enhance performance in competitive sport. An ecological dynamics perspective proposes that the body is a complex adaptive system, interacting with performance environments in a functionally integrated manner, emphasizing that the inter-relation between motor processes, cognitive and perceptual functions, and the constraints of a sport task is best understood at the performer-environment scale of analysis

The costs of conflict

A new study by researchers from The University of Queensland and Shift (an independent, non-profit center for business and human rights practice)/Harvard Kennedy School has uncovered the true scale of the costs companies incur when they come into conflict with local communities

Social Learning in a Multi-Agent System

In a persistent multi-agent system, it should be possible for new agents to benefit from the accumulated learning of more experienced agents. Parallel reasoning can be applied to the case of newborn animals, and thus the biological literature on social learning may aid in the construction of effective multi-agent systems. Biologists have looked at both the functions of social learning and the mechanisms that enable it. Many researchers have focused on the cognitively complex mechanism of imitation; we will also consider a range of simpler mechanisms that could more easily be implemented in robotic or software agents. Research in artificial life shows that complex global phenomena can arise from simple local rules. Similarly, complex information sharing at the system level may result from quite simple individual learning rules. We demonstrate in simulation that simple mechanisms can outperform imitation in a multi-agent system, and that the effectiveness of any social learning strategy will depend on the agents' environment. Our simple mechanisms have obvious advantages in terms of robustness and design costs

Linking behaviour to dynamics of populations and communities : Application of novel approaches in behavioural ecology to conservation

The impact of environmental change on the reproduction and survival of wildlife is often behaviourally mediated, placing behavioural ecology in a central position to quantify population- and community-level consequences of anthropogenic threats to biodiversity. This theme issue demonstrates how recent conceptual and methodological advances in the discipline are applied to inform conservation. The issue highlights how the focus in behavioural ecology on understanding variation in behaviour between individuals, rather than just measuring the population mean, is critical to explaining demographic stochasticity and thereby reducing fuzziness of population models. The contributions also show the importance of knowing the mechanisms by which behaviour is achieved, i.e. the role of learning, reasoning and instincts, in order to understand how behaviours change in human-modified environments, where their function is less likely to be adaptive. More recent work has thus abandoned the 'adaptationist' paradigm of early behavioural ecology and increasingly measures evolutionary processes directly by quantifying selection gradients and phenotypic plasticity. To support quantitative predictions at the population and community levels, a rich arsenal of modelling techniques has developed, and interdisciplinary approaches show promising prospects for predicting the effectiveness of alternative management options, with the social sciences, movement ecology and epidemiology particularly pertinent. The theme issue furthermore explores the relevance of behaviour for global threat assessment, and practical advice is given as to how behavioural ecologists can augment their conservation impact by carefully selecting and promoting their study systems, and increasing their engagement with local communities, natural resource managers and policy-makers. Its aim to uncover the nuts and bolts of how natural systems work positions behavioural ecology squarely in the heart of conservation biology, where its perspective offers an all-important complement to more descriptive 'big-picture' approaches to priority setting. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'