|Title||Using learning networks to understand complex systems: a case study of biological, geophysical and social research in the Amazon|
|Publication Type||Journal Article|
|Year of Publication||2010|
|Authors||Barlow J.R., Ewers R.M., Anderson L., Aragao L., Baker T.R., Boyd E., Feldpausch T.R., Gloor E., Hall A., Malhi Y., Milliken W., Mulligan M., Parry L., Pennington T., Peres C.A, Phillips O.L., Roman-Cuesta R.M., Tobias J.A., Gardner T.A.|
|Keywords||biodiversity, collaborative learning, conservation, deforestation, degradation, interdisciplinary research, learning networks, livelihoods, social learning, sustainable learning|
Annotation for Using learning networks to understand complex systems: a case study of biological, geophysical and social research in the Amazon
To examine research oriented learning networks as a framework for understanding and managing complex systems.
The quality of future scientific research will be dependent on the degree to which (i) information and knowledge is shared across diverse research skills and interests; (ii) awareness and understanding is generated in regard to challenges across disciplines; and (iii) economies of scale are created by pooling research efforts in a way that increases the efficacy of research outcomes. This article explores these assertions by evaluating an interdisciplinary synthesis of 14 different research areas focusing on a single geographic location. This analysis found that arriving at an improved and holistic understanding of specific issues was often dependent on combining and coordinating knowledge from diverse disciplinary areas. Synthesis of effort was seen to potentially improve the timeliness of information and knowledge, increase research efficiency in an area where funding is chronically limited, improve interconnections and feedbacks, increase the spatial and temporal scale of contextual research, and to provide a more unified front for disseminating information. Coordinating effort within a defined geographic focus can improve the awareness and quality of research questions within and across disciplines, while increasing the acquisition of reliable knowledge.
The development of learning networks that enhance the interaction among diverse scientific communities is essential for adaptive learning and action. Networks potentially reach beyond more traditional means of knowledge sharing, such as data sharing, exploring opportunities for collaboration through the whole research process including problem formulation, research design, data analysis, publications and dissemination of findings, and sharing research protocols and infrastructure. This article raises a number of key issues and concepts that are critical to an effective adaptive learning framework. For example: (i) improving interconnections, feedback and communication across diverse system elements; (ii) increasing context specificity while expanding spatial and temporal scales; and (iii) enhancing dissemination of knowledge to inform effective action. Compartmentalising research effort is a significant limiting factor for adaptive learning which, in many cases, is institutionalised through existing funding mechanisms and reward systems. This article provides practical insight and suggestions as to how this issue may begin to be addressed, based on emerging learning networks.