AGENT-BASED MODELING FOR MIGRATIONJulia 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. http://ccl.northwestern.edu/netlogo/models/Ethnocentrism. 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. 3 2012. Source: UNOSAT

PRACTICAL APPLICATIONSInfluence 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 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)

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. 2011

High vulnerability: need to change situation (‘stress’)Low vulnerability: invest in migration

High vulnerability: in-situ copingLow 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. 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: Nature Vol 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.

Contact me!

juliablocher@gmail.com julia.blocher@nrc.ch

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www.internal-displacement.org/