Model Validation – Migrant Characteristics

From the results outlined above it is evident that, in analysing the outputs of the AMARC models, it is important to consider the modelled migrant flows in terms of both the total flow and the origins/destinations of migrants. As such it is reasonable to assume that consideration of the modelled flows at the next level of analysis, that of migrant characteristics, will provide further insight into the simulation potential of the ABM. Evidence that the migrants modelled by the AMARC2 model between 1970 and 1999 correspond approximately with the sum

0 500 1000 1500 2000 2500

AMARC2 EMIUB2

migrants

migration probability characteristics of each agent class in Chapter 5 will provide final proof of the valid nature of the model and the appropriateness of the migration outcomes simulated. The average numbers of agents from all origin zones with each combination of age, gender and marital status attributes modelled by five runs of AMARC2 as migrating between 1970 and 1999 are displayed in Table 6.3 and Figure 6.20.

15-20 Married Female 15-20 Married Male 15-20 Single Female 15-20 Single Male

1,107 114 289 1,084

21-35 Married Female 21-35 Married Male 21-35 Single Female 21-35 Single Male

1,178 1,470 1 209

35+ Married Female 35+ Married Male 35+ Single Female 35+ Single Male

397 610 1 0

Table 6.3: Average numbers of migrants with defined attributes modelled by AMARC2 as originating in all zones between 1970 and 1999.

Figure 6.19: Average numbers of migrants with defined attributes modelled by AMARC2 as originating in all zones between 1970 and 1999.

The data displayed in Table 6.3 and Figure 6.20 reveal that the greatest proportion of migrants modelled by AMARC2 are 21-35 year old married males (1,470 between 1970 and 1999). The next most prolific migrants are modelled as being 21-35 married females. Analysis of the sum behavioural attitude probabilities presented in Figure 5.4 of Chapter 5 revealed that 15-20 year old married females had a high likelihood of migrating. This was attributed to marriage

0 200 400 600 800 1000 1200 1400 1600

migrants

migration, despite the removal of specific reference to such migration from the EMIUB data. In accordance with this unexpected finding, Table 6.3 and Figure 6.20 display 15-20 year old married females as the third most prolific migrants from all zones over the 30 year validation period. Only single male agents over the age of 35 are modelled by AMARC2 as not migrating at all. However, five averaged runs of the model only show two single women aged 21 and over as having migrated over the 30 year validation period. As noted in Chapter 5 however, statistics relating to numbers of migrants must be considered in terms of the population. Figure 6.21 displays the 1970-1999 averaged annual percentage population structure of all agents in the AMARC2 model.

Figure 6.20: Averaged annual percentage AMARC2 population distribution of individuals from all origin locations between 1970 and 1999.

When Figures 6.20 and 6.21 are considered in parallel it is evident that zero or very few migrants could be anticipated to be 21-35 year old single females, over 35 year old single females or over 35 year old single males. A very small population of married male agents aged between 15 and 20 is also evident. The two largest populations of agents are 21-35 year old married males and females of the same age and marital status. Figure 6.21 shows that, of these two agent groups, there are slightly more 21-35 year old married females than males. However, despite this, and as a result of the behavioural attitude values generated by interrogation of the EMIUB data, more males within this age and marital status category migrate than females.

Although it is somewhat surprising that only 209 21-35 year old single male agents are shown to migrate between 1970 and 1999 in Figure 6.20, such a statistic is put into perspective when

0 5 10 15 20 25

percentage of total population

considered in parallel with Figure 6.21. On average, only 2% of the total annual model population fall within the 21-35 year old single male category. Despite this, such migrants make up more than 3% of modelled migrants, indicating that the propensity of such modelled agents towards migration is strong despite the small population of such individuals recorded by the EMIUB2 data.

It can be concluded from Figures 6.20 and 6.21 that the AMARC2 model is able to effectively apply the SMARC theoretical model in the construction of an agent based model of the migration decision in Burkina Faso. The fact that the migrants modelled by the AMARC2 model are found to be effectively distributed across the agent classes reflects both the success of the model in simulating the migration decision and the ability of the model to replicate the appropriate demographic phenomena controlling population growth within the simulated population.

Strong correlations between observed and modelled data for the total modelled flow of migrants and the origins of those migrants, combined with the evidence presented above that confirms the appropriate implementation of the conceptual basis used in the development of the ABM, affirms the value of the AMARC2 model and its ability to simulate migration flows in Burkina Faso as a result of changing rainfall conditions. It is therefore concluded that the AMARC2 model is externally valid in its simulation of both demographic processes and migration decision-making in Burkina Faso and can be legitimately used further by this research in order to investigate the role of change in rainfall variability in the migration decision.