Agent-based modeling for migration

1. Agent-based modeling for migration Julia M. Blocher Sciences Po, 17 March 2014

2. What is agent-based modeling (ABM)? • A computational method to model and simulate complex systems in the real world • Modeling agents (individual entities) that interact with each other within an environment • Explore the dynamics that arise from the characteristics and behaviors of agents making up biological, social, and other complex systems

3. When do you use ABM? • When it is unrealistic, impossible, or unethical to do real-life experiments • To capture phenomena that can be difficult to predict – or are counterintuitive • When it’s important to include individuals - describe activities rather than structure and processes • When you want to understand behavior – not averages

4. What does ABM do well? • Captures emergent phenomenon • Non-linear behavior • Thresholds • If-then rules • Nonlinear coupling (fluctuations, perturbations) • Provides a ‘natural’ description of a complex system • Is flexible and relatively easy to program • Stakeholder engagement is non-negotiable

5. Examples when this type of simulation would be useful? • Flow simulations: e.g. crowds at concerts, panicking populations, transport and traffic – implications for urban planning and evacuation policies • Market simulations: e.g. neural networks for stock markets and trader behavior • Organizational behavior: e.g. risk-taking in banks • Diffusion behavior: e.g. ‘social contagion,’ transmissible disease infection

6. Practical example Source: Wilensky, U. (2003). NetLogoEthnocentrism model. http://ccl.northwestern.edu/netlogo/models/Ethnocentrism. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

7. What do you need for ABM? • One or more agents: • Self-contained • Autonomous and self-directed • State variables • Communicative (social ability) • Have memory, learning, strategies (adaption and path dependence) • Heterogeneous • A representation of the environment • Agent interactions • Bounded rationality

8. Agent-based modeling for environment and climate change induced migration

9. Why use ABM for environment and migration? • A quantitative approach that doesn’t assume all people respond to climate in the same way • Emphasis on unique context and circumstances in real world phenomenon • Recognizes that individual attitudes and perceptions affect migration decision making • Uses rules of behavior from real-life situations and people in computer simulation model

10. Concretely – how do you build an ABM?

11. Agent-based migration model (RABMM-T) Ten year ensemble for rate of total migration under non-scaled normal (N-) and sigmoid (S-) rainfall scenarios tested. Error bars for S-EXTRAWET and S-EXTRADRY. Source: Smith, C.D. (2013) Modeling migration futures: development and testing of the RABMM-T. Sussex: Univ. Sussex, UK.

12. Combining with other research methods • Large N data • E.g. census and household surveys, data mining • Statistical distributions and other stylized facts • Case studies • E.g. Ethnographic studies, interviews • Participatory methods • Role-playing games and companion modeling (e.g. Barreteauet al. (2001) ) • Geographic data: import topographies and GIS, remote sensing • Lab experiments to test computational models

13. Combining ABM with other methods Flow rates in Al Zaa’tri refugee camp, northern Jordan, Sept. 3 2012. Source: UNOSAT

14. Practical applications Influence of climate on political drivers of migration as adaptation in mixed livelihood zones in North-Eastern Ethiopia

15. Conceptualizing the model Drivers of migration. Source: Black, R., S.R. Bennett, S.M. Thomas, J. Beddington. Nature (2011) Vol 478, 27 Oct. 2011

16. Adaptive migration in mixed livelihood zones in N.E. Ethiopia • Question: What are the mechanisms underlying the processes of influence of environmental change on political drivers of migration – the indirect effects of CC on migration decision-making process? • Hypothesis: In past patterns (‘events’ and changes), ‘coping’ migration response increases overall, for most individuals - rate of migration is decreased for participants in local level policy schemes

17. Methodology & specific aims • Treatment of large N data • 20 year retrospective study • 5-10 expert interviews at multiple levels of governance • Questionnaires, reconstruction of migration histories for 100-150 households of migrants and non-migrants • Focus group discussions • Participatory methods • ABM validated by past patterns to predict migration patterns for ‘future worlds’ simulated by high-end warming scenarios (2, 4, and 6 degrees C warming)

18. Haraghe zones, Ethiopia Livelihoods zones in Ethiopia. Source: FEWSNET (USAID)

19. Conceptualizing the model Individual attitudes and perceptions High vulnerability: need to change situation (‘stress’) Low vulnerability: invest in migration Vulnerability assessment: Need to change v. employing existing strategies High vulnerability: in-situ coping Low vulnerability: in-situ adaptation Drivers of migration. Source: Black, R., S.R. Bennett, S.M. Thomas, J. Beddington. Nature (2011) Vol 478, 27 Oct. 2011

20. Agent-based migration model (RABMM-T) Ten year ensemble for rate of total migration under non-scaled normal (N-) and sigmoid (S-) rainfall scenarios tested. Error bars for S-EXTRAWET and S-EXTRADRY. Source: Smith, C.D. (2013) Modeling migration futures: development and testing of the RABMM-T.

21. Agent-based migration model (RABMM-T) Ten year ensemble rate of total migration under the range of non-demographic scenarios. Source: Smith, C.D. (2013) Modeling migration futures: development and testing of the RABMM-T.

22. What’s the catch? • Trade off between generalizability and goodness of fit with values observed empirically • Survey data and statistics can leave out the most vulnerable and marginalized • How do you scale up the processes of a few agents into the interactions among many agents? • As complexity increases, the more difficult it is to link the model’s structure to its behavior (outcomes) • Not as transparent as other methods

23. Sources • Black, R., S.R. Bennett, S.M. Thomas, J. Beddington. Migration as adaptation. In: Nature (2011) Vol. 478, 27 Oct. 2011 • Black, R., et al. (2011). The effect of environmental change on human migration. In: Global Environmental Change 21, Supplement 1(0): S3–S11. • Kniveton, D., C.D. Smith and R. Black (2012). Emerging migration flows in a changing climate in dryland Africa. In: NatureVol 2, pp. 444-447. • Railsback, S.F. and V. Grimm (2012). Agent-Based and Individual-Based Modeling. Princeton: Princeton University Press, Princeton University. • Smith, C.D. (2013) Modeling migration futures: development and testing of the RABMM-Tanzania. In: Climate and Development Vol 1 2014[Accepted Sept 2013]. • Smith, C.D. (2012) Assessing the Impact of Climate Change upon Migration in Burkina Faso: An Agent-Based Modelling Approach. [DPhil Thesis] University of Sussex. • Tacoli, C. (2011). The links between environmental change and migration; a livelihoods approach. London, International Institute for Environment and Development. • Wilensky, U. (2003). NetLogo Ethnocentrism model. http://ccl.northwestern.edu/netlogo/models/Ethnocentrism. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

24. Contact me! juliablocher@gmail.comjulia.blocher@nrc.ch www.twitter.com/juliablocher www.internal-displacement.org/