Post on 15-Apr-2017
Mitigating The Refugee Crisis
Background● Due to civil war in Syria and unrest in the region, more than 4 million
Syrians have fled the country ● The refugees have been displaced all throughout Europe, with some
countries not having the resources necessary to accommodate them● Hungary only allows refugees in the country under the condition that they
are not staying permanently ● Hungary has become one of the most affected European countries by
mitigation crisis ● As of late 2015, Hungary has not approved the quota system for refugees
Looking at the Numbers ● Estimated 200 Syrians are successfully entering the
country every week ● Estimated 250,000 of Hungarians located in the
southern region of the country ● Hungarians receive 150 USD a week from the
government through public services such as education, law enforcement, health and welfare programs
● Government has allocated 50 USD a week for one refugee, which is drawing from their overall welfare budget of resources for the local people
Goal of the model
To see if different types of mitigation would be able to stabilize two populations that had been
forced to interact due to unforeseen circumstances
Predator-Prey Equations
Original Equations
Non-Dimensionalized
● K = Carrying Capacity ● a = rate of loss of welfare
(resources) per time, per refugee for Hungary
● b = rate of loss of welfare the refugees will encounter as they interact with the local people
● c = rate of flow of refugees/ or the rate of resources given to the refugees over time
● X= Syrian population in Hungary
● Y = Hungarians
Mitigation Factors ● The only variable that we are able to
manipulate is the amount of welfare that is available the refugees
● By increasing Ɣ, there will be more resources to give to the refugees without impacting the resources given to the locals.
Higher rate of interaction without enlarged/separate resource pool will increase conflict between the two populations
Scenario where X (Syrians) is greater than Y (Hungarians)
- Setting X at a high value can represent a rapid influx of refugees in the model
- Setting Y at a lower value can represent the local population being outnumbered
- Actual populations would rarely reflect X as greater than Y, but because the populations in the model are also reflective of welfare and not just number of people, these values can work
Scenario where X (Syrians) is less than Y (Hungarians)
- Setting X the value lower than the Y values signals a lower influx of refugees
- A higher Y value signifies a largely stable and consistent local population- This scenario is more likely when simply looking at population numbers,
but similar to the previous scenario, welfare involvement causes complexity and both scenarios can accurately describe changing conditions
Testing Stability/Jacobian When α = .67 and = .25
● (0,0)○ Trace = 1.25 >0 ○ Determinant = .25 > 0 ○ Unstable Node
● (1,0)○ Trace = -1.42 < 0 ○ Determinant = 1.42 > 0 ○ Stable Spiral
● ( /α , α- /α)○ Trace = -.37○ Determinant = -.1554 < 0 ○ Saddle Point
When α = .67 and = .5● (0,0)
○ Trace = 1.5 > 0○ Determinant = .5 > 0 ○ Unstable Node
● (1,0)○ Trace = -1.17 < 0 ○ Determinant = .17 > 0 ○ Stable Node
● ( /α , α- /α)○ Trace = -.7258○ Determinant = -.
128866○ Unstable Node
MATLAB (PHASE PORTRAIT)c
I.C [.5 .7] I.C [.5 .9] I.C [.9 .3]
MATLAB (PHASE PORTRAIT) α= .67 Ɣ= .5
I.C [.5 .7] I.C [.5 .9] I.C [.9 .3]
Interpretation ● When changing initial conditions and keeping the
amount of welfare provided consistent (not changing the values of alpha and gamma), interaction between alpha and gamma changes.
● In application, no specified or set amount of welfare will equate to the same trends. Outcomes are reliant on initial values.
● To manipulate the model, a trend can be observed when setting alpha and gamma at current amount of welfare being provided, and then changed once the initial conditions are perturbed. Other trends can then be run and observed with respect to certain goals.
Results/ Discussion ● Over the long run, the situation is dependent on the initial population.
There is no stable point of coexistence point for the time-span necessary for this model.
● Because the refugees are coming at such a rapid rate, there are no conditions that could integrate them into a country that would not have an impact on the welfare of the local people or a situation that would not lead to conflict.
● When applying this to other real world applications, based off of the initial conditions of both of the populations when you change alpha and gamma (which are the welfare factors for both populations), it will show what levels of welfare or outside intervention is necessary for both populations to coexist.
References For Refugees in Hungary. (2015). “Information for People Arriving in Budapest or Other Parts of Hungary.” Refugees in Hungary. Retrieved
from: http://refugeesinhungary.eu/en/brief-information/
Frayer, Lauren. (2015). “ Risking Arrest, Thousands of Hungarians offer to help Refugees.” National Public Radio. Retrieved from:
http://www.npr.org/sections/parallels/2015/09/29/444447532/risking-arrest-thousands-of-hungarians-offer-help-to-refugees
Myre, Greg. (2015) “The Migrant Crisis: By the Numbers.” National Public Radio. Retrieved from:
http://www.npr.org/sections/parallels/2015/09/08/438539779/the-migrant-crisis-by-the-numbers
The New York Times. (2015) “Which Countries Are Under the Most Strain in the European Mitigation Crisis?” The NY Times. Retrieved from:
http://www.nytimes.com/interactive/2015/08/28/world/europe/countries-under-strain-from-european-migration-crisis.html?_r=2