On the Learning Behavior of Adaptive Networks - Part I: Transient Analysis

This work carries out a detailed transient analysis of the learning behavior of multi-agent networks, and reveals interesting results about the learning abilities of distributed strategies. Among other results, the analysis reveals how combination policies influence the learning process of networked agents, and how these policies can steer the convergence point towards any of many possible Pareto optimal solutions. The results also establish that the learning process of an adaptive network undergoes three (rather than two) well-defined stages of evolution with distinctive convergence rates during the first two stages, while attaining a finite mean-square-error (MSE) level in the last stage. The analysis reveals what aspects of the network topology influence performance directly and suggests design procedures that can optimize performance by adjusting the relevant topology parameters. Interestingly, it is further shown that, in the adaptation regime, each agent in a sparsely connected network is able to achieve the same performance level as that of a centralized stochastic-gradient strategy even for left-stochastic combination strategies. These results lead to a deeper understanding and useful insights on the convergence behavior of coupled distributed learners. The results also lead to effective design mechanisms to help diffuse information more thoroughly over networks.Comment: to appear in IEEE Transactions on Information Theory, 201

CRIBs (Climate Relevant Innovation-system Builders): an effective way forward for international climate technology policy

National systems of innovation (NSIs) provide the context within which all processes of technology development, transfer and uptake occur - they refer to the network of actors (e.g. firms, universities, research institutes, government departments, NGOs) within which innovation occurs, and the strength and nature of the relationships between them. Nurturing NSIs in relation to climate technologies provides a powerful new focus for international policy with potential to underpin more sustained and widespread development and transfer of climate technologies. This working paper builds on an invited presentation by one of the authors at a workshop on NSIs convened by the Technology Executive Committee (TEC) of the United Nations Framework Convention on Climate Change (UNFCCC). It identifies policy recommendations for consideration of the TEC. The intention is both to inform possible recommendations by the TEC to the UNFCCC Conference of the Parties (COP) and to highlight potential areas for future work that the TEC could undertake on this issue

Social learning strategies modify the effect of network structure on group performance

The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines