Integration: The IPO model

By Stephen Crowley and Graham Hubbs

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1. Stephen Crowley (biography)
2. Graham Hubbs (biography)

How can we improve our understanding of knowledge integration? What are the elements of integration?

Sometimes what gets integrated are products of science, such as data sets or scientific models. Sometimes it is not the products that are integrated but instead the methods, as can happen on interdisciplinary teams. On these teams, scientists work together, so sometimes it is the people themselves (scientists are people!) or their disciplinary cultures that get integrated.

These are only some of the possible elements of integration. There is just as wide a variety of processes and products of integration as there are elements. The process of integrating data sets might be a sort of analysis, and the result might be a table or graph that displays the results of research in a conspicuous manner. Integrating diverse scientists into an interdisciplinary team, by contrast, is a matter of people working together, and the result of the integration is not a table or a graph but the team itself.

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Eight grand challenges in socio-environmental systems modeling

By Sondoss Elsawah and Anthony J. Jakeman

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Sondoss Elsawah (biography)

As we enter a new decade with numerous looming social and environmental issues, what are the challenges and opportunities facing the scientific community to unlock the potential of socio-environmental systems modeling?

What is socio-environmental systems modelling?

Socio-environmental systems modelling:

  1. involves developing and/or applying models to investigate complex problems arising from interactions among human (ie. social, economic) and natural (ie. biophysical, ecological, environmental) systems.
  2. can be used to support multiple goals, such as informing decision making and actionable science, promoting learning, education and communication.
  3. is based on a diverse set of computational modeling approaches, including system dynamics, Bayesian networks, agent-based models, dynamic stochastic equilibrium models, statistical microsimulation models and hybrid approaches.

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