Eight grand challenges in socio-environmental systems modeling

By Sondoss Elsawah and Anthony J. Jakeman

1. Sondoss Elsawah (biography)
2. Anthony Jakeman (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.

Eight grand challenges

With the advent of new techniques, data sources, and computational power, the expectation is that socio-environmental systems modeling should be more widely used to inform decision making at multiple scales. Nevertheless, this is not a straightforward endeavour, and both theoretical and methodological challenges abound.

It is therefore timely to identify and formulate current grand challenges in socio-environmental systems modeling, in order to propose clear directions for future generations of models and modeling, to both their developers and users.

We have identified eight areas of challenges, which are also illustrated in the figure below:

  1. Bridging epistemologies across disciplines
  2. Integrated treatment of modeling uncertainty
  3. Combining qualitative and quantitative methods and data sources
  4. Dealing with scales and scaling
  5. Capturing systemic changes in socio-environmental systems
  6. Integrating the human dimension
  7. Elevating the adoption of socio-environmental systems models and impacts on policy
  8. Leveraging new data types and sources.

For each challenge, we briefly highlight the nature of the challenge and key steps in the way forward. For more detail see Elsawah and colleagues (2020).

1: Bridging epistemologies across disciplines

Nature of the challenge:

  • Disciplinary training which limits the ability to develop interdisciplinary approaches
  • Ambiguity about what constitutes data resulting from differences in epistemologies
  • Institutional gate-keeping practices by disciplinary experts who reject novel interdisciplinary methodological approaches and theoretical frameworks
  • Lack of standard collaboration norms.

The way forward:

  • Training in multiple disciplines
  • Effective communication and trust in interdisciplinary collaborations
  • Advancing multi-method approaches
  • Acknowledging the multiple purposes of modeling
  • Diverse reward schemes.

2: Integrated treatment of modeling uncertainty

Nature of the challenge:

  • Limited adoption of integrated uncertainty assessment in practice
  • Limited communication of uncertainty to decision makers.

The way forward:

  • More attention to the qualitative aspects of uncertainty
  • More attention to methods that identify and integrate model structure sources of uncertainty
  • Moving beyond traditional quantitative methods
  • More attention to deep uncertainty and exploratory methods
  • More attention to surrogate modeling methods
  • Better utilization of statistical data analysis techniques to inform uncertainty analysis
  • Strengthening the communication process among model developers and the audience.

3: Combining qualitative and quantitative methods and data sources

Nature of the challenge:

  • Determining the right balance between quantitative and qualitative aspects of data collection and model building
  • Implementing mixed-methods in practice
  • Disciplinary perceptions of methods and data.

The way forward:

  • Reflective and comparative studies to examine the effect of alternative designs
  • Development of methods to support semantics mediation
  • Focusing on qualitative outputs of models.

4: Dealing with scales and scaling

Nature of the challenge:

  • Representing and matching scales in socio-environmental systems models
  • Different levels of knowledge and data about the social and environmental subsystems at various scales
  • Modeling phenomena across multiple scales.

The way forward:

  • Evaluation and comparison of different methodological choices related to scale
  • Developing accessible resources on scaling method
  • Using social models at different scales to represent the vertical interactions within the social subsystem and cross-scale processes in socio-environmental systems.

5: Capturing systemic changes in socio-environmental systems

Nature of the challenge:

  • Lack of knowledge and data on the fundamental processes that drive systemic shifts in social systems
  • Limited methods for modeling systemic changes.

The way forward:

  • Improving knowledge and data for social systems
  • New methods for reasoning about and modeling systemic change
  • Dealing with uncertainty issues as they relate to systemic change.

6: Integrating the human dimension

Nature of the challenge:

  • Limited funding for social science
  • Inherent difficulties in gathering data and representing the process of actual decision-making in models.

The way forward:

  • Better alignment between theory and data that inform social decision rules
  • Going beyond ad hoc assumptions or stylized theories underpinning human behaviour
  • Converging on a set of generic modules to represent iconic socio-economic decisions in the environmental context.

7: Elevating the adoption of socio-environmental systems models and impacts on policy

Nature of the challenge:

  • Measuring the impact of socio-environmental systems modeling on decision making
  • Lack of understanding of the inevitable uncertainty that is part of modeling complex socio-environmental systems
  • Scaling up of outcomes from participatory modeling across multiple scales.

The way forward:

  • In-depth understanding of participatory modeling aspects
  • Better understanding of the political process underpinning decision making
  • More effective visualization.

8: Leveraging new data types and sources

Nature of the challenge:

  • Dealing with emerging ethical issues
  • Methodological issues around data collection and use.

The way forward:

  • Incorporating ethics and equity considerations
  • Addressing biases and uncertainty.

A vision for the future

We also synthesize a vision for the future of socio-environmental systems modeling, which is organized around harnessing the following opportunities:

  1. education and training to prepare the future generations of socio-environmental systems modelers;
  2. consolidating methodological knowledge through multiple and comparative studies;
  3. shifting from piecemeal and ad-hoc uncertainty assessment practices to integrated uncertainty management.

If these issues can be surmounted, then we can ensure that decision makers have tools that can better address their needs.

We are keen to hear your views about the challenges and opportunities ahead of the socio-environmental systems modeling community. Do our eight challenges resonate with your experience? Are there other challenges that you would add? Which challenge will you be most interested to tackle, and why? Can you suggest other priority areas to focus on?

Eight grand challenges for socio-environmental systems modeling and their underpinning issues (source: Elsawah et al., 2020)

To find out more:
Elsawah, S., Filatova, T., Jakeman, A. J., Kettner, A. J., Zellner, M. L., Athanasiadis, I. N., Hamilton, S. H., Axtell, R. L., Brown, D. G., Gilligan, J. M., Janssen, M. A., Robinson, D. T., Rozenberg, J., Ullah, I. I. T., Lade, S. J. (2020) Eight grand challenges in socio-environmental systems modeling. Socio-Environmental Systems Modeling, 2: 16226. (Online) (DOI): https://doi.org/10.18174/sesmo.2020a16226

Biography: Sondoss Elsawah PhD is an Associate Professor and Deputy Director of the Capability Systems Centre, University of New South Wales Canberra, Australia. Her research focuses on the development and use of multi-method approaches to support learning and decision making in complex socio-ecological and socio-technical decision problems. Application areas include natural resource management and defence capability management. She was the chief investigator of the workshop on Use of socio-environmental systems modeling in actionable science: State-of-the-art, open challenges and opportunities, funded by the National Socio-Environmental Synthesis Center (SESYNC).

Biography: Tony Jakeman PhD is Professor and Director of the Integrated Catchment Assessment and Management (iCAM) Centre, at Fenner School of Environment and Society, The Australian National University, Canberra, Australia. His research interests include system identification, integrated assessment methods and decision support systems for water and associated land resource problems. He is leader of the National Centre for Groundwater Research and Training Program on Integrating Socioeconomics, Policy and Decision Support. He was a member of the workshop on Use of socio-environmental systems modeling in actionable science: State-of-the-art, open challenges and opportunities, funded by the National Socio-Environmental Synthesis Center (SESYNC).

3 thoughts on “Eight grand challenges in socio-environmental systems modeling”

  1. Thanks, Val for your comment. I think there is unexplored opportunities yet around how we can integrate story-telling methods into our modelling toolbox.

    • Great point thanks Sondoss. It would be interesting to discuss this perhaps once some semblance of normality returns!

  2. One part that particularly resonated with me was that of focussing on the qualitative outputs of models. Here you framed it within the challenge of combining disparate data sources and I really see the potential there. I think that examining qualitative features can also contribute to communication about the implications of modelled outputs in the face of uncertainty. It is also an underrated (at least my perception) method for testing/validating models with experts. Thanks for this post Sondoss and Tony. It, and the SESMO paper are interesting reads.


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