AGENT-BASED MODELING FOR MIGRATION Julia M. Blocher Sciences Po, 17 March 2014

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

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

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

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

Practical example Source: Wilensky, U. (2003). NetLogo Ethnocentrism model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

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

AGENT-BASED MODELING FOR ENVIRONMENT AND CLIMATE CHANGE INDUCED MIGRATION

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

Concretely – how do you build an ABM? Overview Purpose Entities, state variables, and scales Process overview and scheduling Design Basic principles Agent and agent rule design Environment design Details Initialization Input data Verification and validation

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. Agent-based migration model (RABMM-T)

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. Barreteau et al. (2001) ) Geographic data: import topographies and GIS, remote sensing Lab experiments to test computational models

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

PRACTICAL APPLICATIONS Influence of climate on political drivers of migration as adaptation in mixed livelihood zones in North-Eastern Ethiopia

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

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

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 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)

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

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

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. 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. Agent-based migration model (RABMM-T)

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

Sources Black, R., S.R. Bennett, S.M. Thomas, J. Beddington. Migration as adaptation. In: Nature (2011) Vol. 478, 27 Oct 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: Nature Vol 2, pp 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 [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. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

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