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Virtual reproduction of the migration flows generated by AIESEC P.G. Battaglia, F.Gorrieri, D.Scorpiniti

Introduction AIESEC is an international non-‐profit organization that provides services for university students and most of all offer internships in an international playground. The following project focuses on the analysis of the internship system and the migration flows, generated by individuals who want to find an internship abroad. These flows will be based on the combination of both endogenous characteristics of the agents and exogenous variables of the countries in which the AIESEC offices are based.

Our world will be divided into four main blocks, virtually representing agent’s origins. The green square will be representing Europe, the brown one Africa, the blue one America and the yellow one Asia. On each block is settled an AIESEC office and the overall population is distributed around the world by a set of commands on the interface ruled by the user.

Procedure Overview The first step of our program is representing the AIESEC advertising campaign, during which the organization will try to convince people to apply for the internships offered, the rate of success will be set by the user through a slider on the interface. An additional task of the offices is generating available positions. Indeed, each division will provide a number of available places for individuals coming from each block of origin, these values will have to be set by the user.

The second step lies in the feedback influence. Indeed, a percentage of the workers at the end of the internship will provide its own feedback about the country in which it has done the experience and this will modify the preferences on that particular country of the new applying agents. This interaction of individuals who have already left and new potentially applying will condition the decision of each candidate about his or her destination. On the basis of this new modified endogenous variable, the candidates will go to the local AIESEC office and apply for an internship and the reiteration of this process will represent the migration flows.

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Main Procedures After having divided the world into the four blocks, we generate the four AIESEC offices and the global population and then we distribute it all around our world, according to the rates defined on the interface. Below is provided a detailed description of the main procedures of the simulation, in particular of those through which are modified endogenous variable of the agents, in order to make it more clear for the reader to understand the logic of the simulation.

To internship The internship command is composed by the contacting procedure, that is the organization’s advertising campaign, during which AIESEC reaches a certain percentage of the overall population and convinces it to apply for an internship abroad. The individuals applying will be easily recognised as they will become white. Next we focus on the internship selection. Each of the applying candidates will move to his/her local AIESEC office and will randomly choose a destination. Here is an example for the European candidates and this procedure is repeated for each of the four origins:

let Eu ( x * africarate + y * americarate + z * asiarate )

ask europeans [ if (convinced = 1 ) [ move-‐to office 0 set color green ] ]

ask europeans with [convinced = 1] [ set destination random int Eu abroad_e]

to abroad_e

if (destination <= x * africarate ) [move-‐to office 1]

if (destination >= ( x * africarate + y * Americarate ) ) [move-‐to office 2]

if (destination > x * africarate and destination < ( x * africarate + y * Americarate ) ) [move-‐to office 3]

end

Practically all the applying candidates will randomly select an integer number less or equal to “Eu” and according to the belonging interval, the person will move to a specific block. “Eu” is the sum of the intervals pertaining to each destination, multiplied by their growth/decreased rates (initially set at 1), which is given by the feedbacks. Initially each interval will be of the same size but in a second moment they will be modified by the feedback rates, that will influence their dimension and therefore the probability of the future interns of choosing a country rather than another.

To select This is probably the most important and complicated command of the whole code, indeed it includes many procedures and it is the main developer of the system. The task of this command is to allow the potential interns, who have not found an available job position in their first destination choice, to look for other possibilities around the world. Moreover it includes the feedback procedure, which represents the endogenous variable of the process.

The first step is to rearrange the first wave of interns incoming in each block. The individuals who initially have decided to move to a specific destination, go there, check if there are available job positions and if they find an internship opportunity stay there. At the end of this step we count how many free positions in each block for each country of origin are left. Below is related the command for the Europeans in the AfricanBlock, note that the same is repeated for each of the nationalities of the interns, for each of the

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blocks. The happiness variable refers to the satisfaction of the agents, therefore, if the candidate finds an internship at this step the happiness variable will increase by two units, as the intern has found a job in his/her first destination preference.

let europeans_in_africa count europeans with [convinced = 1 and destination <= x * africarate ]

if (europeans_in_africa > eaf_places) [

ask n-‐of eaf_places europeans with [convinced = 1 and destination <= x * africarate ] [

move-‐to one-‐of AfricanBlock set pastdestination 2 set happinesseurope happinesseurope + 2

set stage 1 set free_eaf_places 0 ] ]

if (europeans_in_africa <= eaf_places ) [

ask n-‐of europeans_in_africa europeans with [convinced = 1 and destination <= x * africarate ] [

move-‐to one-‐of AfricanBlock set pastdestination 2 set stage 1 set happinesseurope happinesseurope + 2

set free_eaf_places eaf_places -‐ europeans_in_africa ]]

In a second moment we ask to those interns who have not found a job in their first destination choice to return to their belonging block and look for another position somewhere else. This latter procedure represents another interesting feature of the code. Indeed what we do is to compute how many potential interns have come back, not finding an internship in their first destination choice, and how many free available positions are left all around the world for the individuals coming from each block. Later we use an algorithm to embrace all the situations possible. Here are reported the code lines for the Africans in two cases: there are places only in one country and there are places in two countries. As always the procedure will be repeated for each of the four nationalities, moreover we report only the cases in which there are places in one or two specific blocks, obviously the code is repeated for each of the possible combinations.

set africans_back count africans with [convinced = 1 and stage != 1]

let available_placesAF (free_aas_places + free_aeu_places + free_ausa_places)

ask africans with [convinced = 1 and stage != 1] [

if (free_aeu_places != 0 and free_aas_places != 0 and free_ausa_places = 0 ) [ set destination random int ( a * europerate + c * asiarate ) retry-‐abroadA1]

if (free_aeu_places = 0 and free_aas_places = 0 and free_ausa_places != 0 ) [move-‐to one-‐of AmericanBlock set pastdestination 3 set stage 1 set free_ausa_places free_ausa_places -‐ 1 set happinessafrica happinessafrica + 1]

if (free_aeu_places = 0 and free_aas_places = 0 and free_ausa_places = 0) [ask africans with [convinced = 1 and stage != 1][set happinessafrica happinessafrica -‐ 1 die]]

to retry-‐abroadA1

if (destination <= a * europerate and africans_back < free_aeu_places ) [move-‐to one-‐of EuropeanBlock set pastdestination 1 set stage 1 set free_aeu_places free_aeu_places -‐ 1 set happinessafrica happinessafrica + 1 ]

if (destination <= a * europerate and africans_back >= free_aeu_places ) [move-‐to one-‐of EuropeanBlock set pastdestination 1 set stage 1 set free_aeu_places 0 set happinessafrica happinessafrica + 1]

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if (destination > a * europerate and africans_back < free_aas_places) [move-‐to one-‐of AsianBlock set pastdestination 4 set stage 1 set free_aas_places free_aas_places -‐ 1 set happinessafrica happinessafrica + 1 ]

if (destination > a * europerate and africans_back >= free_aas_places ) [move-‐to one-‐of AsianBlock set pastdestination 4 set stage 1 set free_aas_places 0 set happinessafrica happinessafrica + 1]

end

A chance is that there are free places for the people coming from a specific block in only one country, in that case a number of interns equal to the free available positions moves to this new destination. Another possibility is that there are free places in two different blocks, in that case the interns set a new destination choice and go to the new destination chosen. The variable happiness is increased only of 1 unit, as the interns do find an job abroad, but not in the block they would have liked.

Proceeding in the select command, we find the endogenous part of the code. Here, through a slider on the interface, the user selects the percentage of past interns who comes back to its origin block and provides a feedback about its past experience. All the individuals are endowed with a variable “quality”, and for those who come back to provide a feedback, we randomly modify it in a range of 40 around 100. Successively we sum the quality of all the feedback-‐giving people and we compute the quality rate for each block. This rate influences the probability that the new potential interns apply for a block rather than another. Below is presented the example of the quality of the European block.

let europeansfeedback round ( feedback-‐rate * count europeans)

to feedback

ask turtles with [ pastdestination != 0 and stage = 1 ][

ifelse random-‐float 1 > 0.5 [set quality quality + random -‐40]

[set quality quality + random 40] ]

ask turtles with [ pastdestination != 0 ] [ set label quality set label-‐color white ]

set qualityinEurope sum [quality] of asians with [ pastdestination = 1 ] + sum [quality] of americans with [ pastdestination = 1 ] + sum [quality] of africans with [ pastdestination = 1 ]

let pastpersoninEurope count asians with [ pastdestination = 1 ] + count americans with [ pastdestination = 1 ] + count africans with [ pastdestination = 1 ]

ifelse pastpersoninEurope != 0 [

let Europebase pastpersoninEurope * 100

set Europerate qualityinEurope / Europebase ] [ set Europerate 1 ]

The last command of the program is called “El Farol”, this name comes from the study of the El Farol minority game, that has inspired this work. The “new” agents collect the information provided by “old” agents, and according to that improve the probability of moving to a specific block rather than another. Practically the agents can learn from the experience of overcrowding happened in the past.

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The command is structured to check if the percentage of people who have applied for a specific block is too high. As an example, we start from the case of population set at 400 individuals, an applying rate at level 1 and with a given set of available positions, which has as final result 100 interns in each country. Since we are able to modify the set of available positions and create more interesting results, we can study what happens if an higher percentage of people is willing to do the internship in a particular country. The “El Farol” command checks exactly this situation, moreover if in a specific block there has been an overcrowding phenomenon in the last period, it decreases the rate of choice linked to that destination.

As in the El Farol we wanted to re-‐create the issue of local information, focused on the fact that in the reality not all the agents have the same quantity of information. To achieve this result we set up a variable and we activate the command only if there is not a loss of information greater than the 70% of the total. Here is reported the code of the command:

if ( internspastdestination1 != 0 and internspastdestination2 != 0 and internspastdestination3 != 0 and internspastdestination4 != 0 and lostinformation < 0.7 )

[ if (internspastdestination1 > 0.30 * ( internspastdestination1 + internspastdestination2 + internspastdestination3 + internspastdestination4 ) [set europerate europerate -‐ 0.05]

if (internspastdestination2 > 0.30 * ( internspastdestination1 + internspastdestination2 + internspastdestination3 + internspastdestination4 ) )

[set africarate africarate -‐ 0.05]


[set americarate americarate -‐ 0.05]


[set asiarate asiarate -‐ 0.05]]

; 3 country with places

if ( internspastdestination1 = 0 or internspastdestination2 = 0 or internspastdestination3 = 0 or internspastdestination4 = 0 and lostinformation < 0.7 )

[ if (internspastdestination1 > 0.40 * ( internspastdestination1 + internspastdestination2 + internspastdestination3 + internspastdestination4 ) )

[set europerate europerate -‐ 0.05 ]


[set africarate africarate -‐ 0.05 ]


[set americarate americarate -‐ 0.05 ]


[set asiarate asiarate -‐ 0.05 ]]

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The Graphs

The Graphs

Past destination: with this graph we monitor each country and we show the number of people that has done the internship in there. For each block we create a variable called “Internpastdestination”, which assumes as value the sum of agents, who has been doing the internship in that specific block. Below is reported an example from the code:

set Internspastdestination1 count turtles with [ pastdestination = 1]

Quality rate: this figure exhibits the sum of all the values of the variable quality of those people that, after having done the internship, are selected to give a feedback to the new generation. This sum is divided by the number feedback-‐giving individuals multiplied by their initial quality value (100). As it has been explained before, the value obtained will influence the probability of choosing a destination block rather than another for the future applying agents.

Happiness: It shows the happiness of the agents, in terms of satisfaction of their desires. Each agent willing to leave gets 2 points if he/she finds an available position in the first destination choice. Otherwise if he is able to find an available position, but somewhere else, he/she only gets 1 point. Finally if the candidate was willing to leave, but he/she has not found an available job anywhere, than the happiness value decreases of one unit. The sum of the happiness of individuals from each of the four block, divided by the number of individuals that wanted to leave, provides us the graph happiness on the interface.

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Experiments

Test 1: Abundance and scarcity of feedbacks The first experiment focuses on how much does the endogenous procedure of giving feedbacks affect the outcomes of the program. We set the population equally distributed around the world, the population size (100) and the number of available places will be 240 and fairly spread and the applying rate will be set at 50%. To test the influence of the feedback procedure, we will initially set the feedback rate at 0, then 50 % and finally at 100% and looking at the graphs we will analyse how do the past destinations change and also how does the quality rate evolve in each of the three situations.

The first case, where the feedback rate is set equal to 0, that means that no past intern is coming home to provide a statement about the past experience is presented below.

The trend of the past destinations is quite chaotic with huge and frequent variations. Moreover we can notice that, even though they are random, because of the El Farol procedure, when a specific country becomes to much popular and gets problems of overcrowding, successively it will fall down because the quality rate referred to it will decrease. The graph of quality rate is also quite static. It moves around the value 1 as the code is structured in such a way that the variable is influenced mostly by those who are coming back and providing a feedback, in this case none. The variations are given to the El Farol procedure, that, as we have noticed before, reduces the quality rate for those blocks that have been the most chosen destinations in the last period.

The second test is done setting the feedback rate at 0.5, this means that half of the interns who have been abroad will come back and share their opinion with the new applying individuals.

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In this case we see that still the destinations are moving frantically and the variations have a bigger amplitude, as they are reflecting the trend of the quality rates. This means that the feedback procedure influences strongly the choice of the destination place. The quality rate trend is really interesting. We can see that it is lancing randomly around 1 in a range of 0.45. The trend is completely random and reflects reality as it is strongly accidental whether to appreciate more or less an experience of working abroad, depending on many different variables. Also in this case the El Farol procedure is applied, reducing for the interns the probability to apply for a destination, that last period has been overpopulated.

Finally we analyse the case in which all the past interns are coming back to provide their feedback.

In this final case the variations of the quality rates are slightly less fluctuating than in the previous event. Interesting is also the evolution of the rates, that look calmer and less chaotic than in the precedent case. The variations indeed are softer and less agitated than before, but in affecting the past destinations they have a strong influence. This influence is clearly represented by the increased frequence and amplitude of the deviations in the past destinations graph.

To summarize we can deduce that the choice of the interns is more random, when the probability of choosing a block rather than another is the same for each destination. The more the feedbacks influence the rates, the more the fluctuations of the quality rates will become closer to the mean (1), the more random will be the destination choice.

Test 2: Abundance and scarcity of available internship positions We set up the program with a population of 500 individuals equally distributed around the four blocks. The applying rate is fixed at 0.5, therefore there will be 250 people applying for an internship abroad and the feedback rate will be set at 0.5. Initially we set the total amount of free available internship positions at 240, fairly spread around the four blocks, practically we choose 20 as input for available positions for each

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of the four blocks for people coming from all around the world. Then we reduce this number to 4, for a total amount of 48 available positions and then we set it a 40 for a total amount of 480 available positions. This simulation will enable us to see what happens in cases of extreme abundance or scarcity of opportunities.

This first case simulates a situation in which the agents applying for an internship are almost the same number as the available position. What we get is a graph in which the agents are really “happy”, indeed the majority of them has been able to perform an internship, willing to do that.

The second eventuality is that there are 250 potential interns applying, but only 48 available position. This is a situation of extreme scarcity of job offers. Obviously what we can deduce from the figures is that the happiness of the agents is decreasing, indeed many individuals willing to move, will not be hired. Predicted is also the outcome of the past destinations graph. As there are a few free places and many interns applying, clearly the positions will all be takes and the representing line will adjust at the maximum level of available positions possible. On the other hand is quite interesting to have a look at the quality rates. The lines are moving frantically and widely. The reason for this comes from the fact that as the interns providing the feedbacks are just a few, the rates do not tend to a smooth function, but on the other hand, they have big and twitchy variations.

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Finally we come to the last case, that of abundance of available positions ( 480 versus 240 applying candidates). What we get is a chaotic figure for the past destinations and a smoother graph for quality rates variations. Moreover we can notice that the happiness figure has a positive trend, as all the candidates will be able to find an available position, and furthermore to find it in their first destination choice. The slope of the line is indeed steeper than in the first case, as the interns will be happier, given the fact that they will probably find a work in their first preference, and less probably be pleased by a second choice.

Test 3: Rate of Applying In this test we analyse the influence of the applying rate on the results. The population is set at 500 equally distributed in each block, the feedback rate is set a 0.5 and 20 available places are given to each block to the candidates coming from each country, for a total amount of 480 positions.

We focus at first on the case where only the 20% of the population is willing to take part to an internship abroad. The outcomes are really similar to those of the abundance of available positions, with a positive

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trend for the happiness figure, a frantically moving past destinations graph and a quality rate graph characterized by not so wide variations.

In the second case the applying rate is set at 0.5, that means that half of the people in the world wants to participate to an AIESEC project. The past destinations graph is stirring around a constant value, that is the maximum number of available positions. Indeed the applying interns are 250 and the available positions 240, therefore the positions are all taken, apart from some small variations in the trend given by some random influences. The quality rate is smoother than in the previous case, because of the fact that there are more people providing their feedback. Finally the happiness figure maintains a positive trend, although with a smaller slope, given by the reduced possibility of the interns to be completely satisfied.

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If the applying rate is set at an higher level, here 0.8, the past destinations figure sets statically on a constant value, equal to the number of available destinations. The variations present in the previous case are here reduced to zero, therefore the influence of the random variables of the programs is reduced. This case should present similar characteristic to that of scarcity of available positions, but this does not happen. Apart from the first graph that is practically the same, both the others have different characteristics: a positive trend for the happiness, even with a slower pace and smaller variances for the quality rates.

Finally we have a look at the extreme case in which the applying rate is set equal to 1, that implies that all the people in the world want to take part to an AIESEC program. The past destinations graph is statically set at the maximum level of available places. The quality rates are moving in a really similar way to the previous case and also the happiness graph is following a positive trend, but with an even smaller slope than in the previous case.

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Test 4: Unequal distribution of the population In this experiment we test what happens if we distribute the population in the world in an unequal way. We do it for three chance: most of the population on one block, few people on a block and all the population spread in only two blocks. The total amount of people is set at 500, the feedback rate at 0.5 and the applying rate at 0.5. There are 20 available internship positions in each block for interns coming from each of the four blocks, for a total amount of 480 jobs.

First we test what happens if the majority of the population is living in one of the four blocks. For doing this we modify the distribution percentage on the interface and we set the Asian-‐rate at 0.6, the American-‐rate at 0.2 and both the African-‐rate and the European-‐rate at 0.1. What we get is a graph in which the past destinations graph presents huge variations, but it is clear that there are less people moving to America and mostly to Asia. This is because as there are less people living there than in the other two blocks, there will be less people coming from abroad to fill the available working positions in Asia. The second figure instead, shows the trend of the happiness that is strongly positive for all of the unpopulated blocks, as it will be easy for them to satisfy their destination and working desires. On the other hand the Asians are unhappier and the reason for that can be found in the overcrowding of their origin country.

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A second chance to be analysed is the eventuality of having a block under-‐crowded. This is simulated fixing the distribution rates at 0.32 for each of the blocks but Europe, that is set is at 0.04, this means that only the 4% of the overall population will be born in the European block. The outcome is that obviously Europe is the most popular destination, because all the places in there will be filled by interns coming from abroad, while being so few the Europeans will not be able to compensate the offers from abroad. In the same way has to be read the happiness graph. The Europeans do not have to “fight” to find an available place, they are so few that it is easy for them to find a proposal that fits their desires. On the other hand for the other countries of origin there is more competition.

Finally we look at what happens in the unlikely event that only the northern side of the world is populated. This is done through setting the distribution rate of the American and European blocks at 0.5 and 0 for the other two blocks. What we get is that the Asian and African blocks are preferred destination, that comes from the fact that, as there are no Asians or African to go abroad, the people moving to America will only be the 20 Europeans and viceversa, while the interns moving to Asia and Africa will be the sum of the two groups of interns coming from the populated blocs. Moreover the representing lines for Africa and Asia will be overlapping and without variations, as well as those of America and Europe. This situation implies overlapping lines also in the happiness graph. Indeed the happiness of Europeans and Americans is the same and higher than the one of the other two blocks.

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Test 5: Unequal distribution of the internship positions A similar test to the one previously done is that of setting the available internship positions in an unequal geoghraphical way. The population is set at 500, the applying rate and the feedback rate at 0.5 and the distribution rates for the populations on each block are all set at 0.25. We initially try to see what happens if only country wants incoming interns from abroad. For doing this we set the available places in Africa, for interns coming from each part of the world at 20, for a total amount of 60 places, and we set 0 in all the other countries. What we obtain is a graph in which all the lines referred to past destinations other than Africa are equal to 0. The graph for Africa is steadily set at the number of available positions, both because this number of job offered is small if compared to the candidates and also because all the interns of the world are trying to go there, therefore the positions are all taken. The quality rate of each block is also not moving, as there is no interns who has been there and therefore none is coming back to provide feedbacks. Only the African quality rate is changing, but we see that the trend is that of the reducing the variations, therefore we expect it to be assimilated to a the constant value of the other lines in the long period. Finally the happiness graph shows clearly that the Africans are the unhappiest in the world. Indeed they can not take part to any internship abroad and therefore they are strongly unsatisfied.

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In the following example we analyse the case in which one country does not want any incoming intern from abroad. We do this setting 30 available positions on each block for candidates coming from each of the other blocks, but in Africa, where there are no available job positions. The outcome is a situation in which the past destination figure is quite chaotic for each of the blocks, but for Africa, were the graph is constant at value 0. That is because none can go there. The quality rate graph shows small variations for each of the country in which is possible to find an internship and a constant value for Africa. The happiness is clearly higher for Africans than for others to be satisfied, as the others miss the chance to move to Africa.

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Test 6: Racism

This final test focuses on the contingent presence of racism, therefore we simulate what can happen if no block wants interns coming from a specific block, for example Europe. We set up a population of 500 individuals equally distributed in the 4 blocks. The applying rate is set at 0.5 as well as the feedback rate. Each of the input boxes is set at 20, but the ones referred to available positions for Europeans, that are set equal to 0, the total amount of available places will be 180, of which 0 destined to Europeans.

What we can infer from these charts is that obviously the value around which the Europe line sets in the past destination graph is bigger than for the other blocks. Indeed Europe is accepting incoming interns from all over the world, while the other three blocks are refusing European interns and offering the same number of positions to incoming workers of other origins. The happiness of Europeans obviously follows a negative trend, as they have no chance to leave for an internship abroad and therefore to satisfy their desires. If we leave the same conditions, but we change the number of incoming interns from nationalities different from Europe, therefore we leave the inputs to go in Europe al set at 20, while the inputs of other blocks equal to 0 for Europeans and to 30 for the other nationalities, for a total amount of 240 available positions. This option makes the model more realistic, as ideally if a block needs 60 interns, but does not want Europeans, will hire more people from other nationalities. What we get is:

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These are very chaotic graphs, in which the past destinations are doing huge deviations, but with the feeling that the Europe line is still overlooking the others. The reason might be the wider acceptance of the block that makes it easier in a probabilistic way to choose to go there or the influence of the feedback rate on the decision of the future interns. The quality rate graph is more regular and with smaller variations that in the previous case. To conclude the happiness graph keeps the same feature, with a negative trend for the happiness of Europeans and a positive one for the happiness of the other interns. Moreover the slope of the graph for the other three remaining blocks is steeper, as being the number of available internships increased, it is easier for them to find a internship abroad and therefore their happiness increases.

References

1. Emergence of Cooperation and Organization in an Evolutionary Game. CHALLET, D., ZHANG, Y.C., 1997

2. Gossip, Sexual Recombination and the El Farol Bar: modelling the emergence of heterogeneity, Journal of Artificial Societies and Social Simulation. EDMONDS, B., 1999

3. Seeing the light at El Farol. Complexity Vol. 1 CASTI, J.L., 1996.

4. Shedding light on El Farol. Physica A: Statistical Mechanics and its Applications , Volume 332, Pages 469–482 CHALLET, D., MARSILI, M., OTTINO, G., 2004.

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