Localized Learning and Social Capital The Geography Effect in Technological and Institutional Dynamics

Providing a concise working definition of social capital, this conceptual paper analyses why social capital is important for learning and economic development, why it has a regional dimension, and how it is created. It argues that with the rise of the Knowledge Economy, social capital is becoming valuable because it organizes markets, lowering business firms’ costs of coordinating and allowing them to flexibly connect and reconnect. Thus, it serves as a social framework for localized learning in both breadth and depth. The paper suggests that a range of social phenomena such as altruism, trust, participation, and inclusion, are created when a matrix of various social relations is combined with particular normative and cognitive social institutions that facilitate cooperation and reciprocity. Such a matrix of social relations, plus facilitating institutions, is what the paper defines as “social capital”. The paper further suggests that social capital is formed at the regional (rather than national or international) level, because it is at this level we find the densest matrices of social relations. The paper also offers a discussion of how regional policies may be suited for promoting social capital.Social capital, knowledge economy, regional dimension

Multi-Agent Game Abstraction via Graph Attention Neural Network

In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms.Comment: Accepted by AAAI202

The Mirroring Hypothesis: Theory, Evidence and Exceptions

The mirroring hypothesis predicts that the organizational patterns of a development project (e.g. communication links, geographic collocation, team and firm co-membership) will correspond to the technical patterns of dependency in the system under development. Scholars in a range of disciplines have argued that mirroring is either necessary or a highly desirable feature of development projects, but evidence pertaining to the hypothesis is widely scattered across fields, research sites, and methodologies. In this paper, we formally define the mirroring hypothesis and review 102 empirical studies spanning three levels of organization: within a single firm, across firms, and in open community-based development projects. The hypothesis was supported in 69% of the cases. Support for the hypothesis was strongest in the within-firm sample, less strong in the across-firm sample, and relatively weak in the open collaborative sample. Based on a detailed analysis of the cases in which the mirroring hypothesis was not supported, we introduce the concept of actionable transparency as a means of achieving coordination without mirroring. We present examples from practice and describe the more complex organizational patterns that emerge when actionable transparency allows designers to 'break the mirror.'Modularity, innovation, product and process development, organization design, design structure, organizational structure, organizational ties

Evolutionary robotics and neuroscience

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A Role-Based Approach for Orchestrating Emergent Configurations in the Internet of Things

The Internet of Things (IoT) is envisioned as a global network of connected things enabling ubiquitous machine-to-machine (M2M) communication. With estimations of billions of sensors and devices to be connected in the coming years, the IoT has been advocated as having a great potential to impact the way we live, but also how we work. However, the connectivity aspect in itself only accounts for the underlying M2M infrastructure. In order to properly support engineering IoT systems and applications, it is key to orchestrate heterogeneous 'things' in a seamless, adaptive and dynamic manner, such that the system can exhibit a goal-directed behaviour and take appropriate actions. Yet, this form of interaction between things needs to take a user-centric approach and by no means elude the users' requirements. To this end, contextualisation is an important feature of the system, allowing it to infer user activities and prompt the user with relevant information and interactions even in the absence of intentional commands. In this work we propose a role-based model for emergent configurations of connected systems as a means to model, manage, and reason about IoT systems including the user's interaction with them. We put a special focus on integrating the user perspective in order to guide the emergent configurations such that systems goals are aligned with the users' intentions. We discuss related scientific and technical challenges and provide several uses cases outlining the concept of emergent configurations.Comment: In Proceedings of the Second International Workshop on the Internet of Agents @AAMAS201

The Geography Effect in Technological and Institutional Dynamics

Providing a concise working definition of social capital, this conceptual paper analyses why social capital is important for learning and economic development, why it has a regional dimension, and how it is created. It argues that with the rise of the Knowledge Economy, social capital is becoming valuable because it organizes markets, lowering business firms’ costs of coordinating and allowing them to flexibly connect and reconnect. Thus, it serves as a social framework for localized learning in both breadth and depth. The paper suggests that a range of social phenomena such as altruism, trust, participation, and inclusion, are created when a matrix of various social relations is combined with particular normative and cognitive social institutions that facilitate cooperation and reciprocity. Such a matrix of social relations, plus facilitating institutions, is what the paper defines as “social capital”. The paper further suggests that social capital is formed at the regional (rather than national or international) level, because it is at this level we find the densest matrices of social relations. The paper also offers a discussion of how regional policies may be suited for promoting social capital

Neural Circuit Architectural Priors for Embodied Control

Artificial neural networks for motor control usually adopt generic architectures like fully connected MLPs. While general, these tabula rasa architectures rely on large amounts of experience to learn, are not easily transferable to new bodies, and have internal dynamics that are difficult to interpret. In nature, animals are born with highly structured connectivity in their nervous systems shaped by evolution; this innate circuitry acts synergistically with learning mechanisms to provide inductive biases that enable most animals to function well soon after birth and learn efficiently. Convolutional networks inspired by visual circuitry have encoded useful biases for vision. However, it is unknown the extent to which ANN architectures inspired by neural circuitry can yield useful biases for other AI domains. In this work, we ask what advantages biologically inspired ANN architecture can provide in the domain of motor control. Specifically, we translate C. elegans locomotion circuits into an ANN model controlling a simulated Swimmer agent. On a locomotion task, our architecture achieves good initial performance and asymptotic performance comparable with MLPs, while dramatically improving data efficiency and requiring orders of magnitude fewer parameters. Our architecture is interpretable and transfers to new body designs. An ablation analysis shows that constrained excitation/inhibition is crucial for learning, while weight initialization contributes to good initial performance. Our work demonstrates several advantages of biologically inspired ANN architecture and encourages future work in more complex embodied control.Comment: NeurIPS 202