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Using Agent-Based Modelling in Studying Labour–Education Market System

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Agent-Based Modelling of Social Networks in Labour–Education Market System

Part of the book series: SpringerBriefs in Complexity ((BRIEFSCOMPLEXITY))

Abstract

We’re now ready for a discussion on how exactly one could build an agent-based model of labour–education market system (LEMS). This discussion will necessarily be quite abstract, because particular mechanisms built into the model (agents, their behaviour, interactions, other structures) depend heavily on the purpose of the model. I’ll focus on general approaches and mechanisms that you may find useful when building agent-based models of LEMS.

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Notes

  1. 1.

    See [126] for a more elaborate review of agent-based models of the education market.

  2. 2.

    Assuming that the agent-based model is implemented in some object-oriented language .

  3. 3.

    The two decisions may depend on distinct sets of factors, but this is not a problem from the mathematical point of view.

  4. 4.

    Strictly speaking, in case of private education institutions, the quantity side still plays a role here, as they may be reluctant to turn the output filter on to full extent if that would mean such large drop-out rate that would make them miss their financial targets.

  5. 5.

    There’s no mistake here—it is possible to introduce job heterogeneity without introducing firms. This might, for instance, reflect occupations or industries.

  6. 6.

    These two variants are equivalent; the difference is only in the side where random choice is implemented. In the first-applied-first-accepted approach, the programmer randomises the sequence of persons in the queue of sending applications. In the random choice approach, persons send applications in any order (e.g., in the order they are stored in the array of labour market participants), and randomness is shifted to the process of choosing an element from the list of applications received by the firm.

  7. 7.

    Besides leaving the reader somewhat disappointed by hiding the details of your model, you make it harder to replicate it. There is a strand of research on agent activation , which studies in which cases seemingly minor differences in the implementation details of the mechanism of agent behaviour cause substantial differences in model outputs (see, e.g., [15, 69, 70] if you’re interested). Thus, a replica of the model may produce results that are inconsistent with the original model also if the replica’s author incorrectly inferred the actual—but unspecified—activation scheme used in the original model.

  8. 8.

    In [105] and [240], the authors actually consider reservation wage to be constant and reflect the absolute minimum wage under which the person will never be ready to work. Instead, they impose a random decrease of the “satisficing” wage—the one the person would like to receive—with unemployment duration.

  9. 9.

    This setting does allow to model overall increase and decrease in labour demand without the need to specify which employees firms lay off in case of downsizing. A downside is that job search takes place every period, which might be too frequent for your purposes.

  10. 10.

    Amadeus is a product of Bureau Van Dijk company and contains internationally comparable financial data on around 21 million European companies. For a global version, consider Orbis, a product from the same provider with data on more than 160 million companies.

  11. 11.

    For instance, the YANG external network generator is used in [173, 270], but network generating packages in Java or other general-purpose programming languages can also be used—an attractive option if the model is also created in that language (or in a framework that uses that language).

  12. 12.

    A connected component is a subgraph of an undirected graph where each pair of vertices is connected by a path and there are no other vertices in the graph that are connected to any vertex of the connected component but aren’t in its vertex set. Thus, the connected components of a graph are isolated from each other—there is no edge between a vertex in one component and a vertex in any other component.

  13. 13.

    Inbreeding homophily of type i is defined as \(\mathrm{IH}_{i} \equiv (H_{i} - w_{i})/(1 - w_{i})\), where H i is the share of ties of type-i individuals with other type-i individuals and w i is the share of type-i individuals in the population [77]. It measures the preference to creating ties with same-type individuals relative to the maximum bias, which is (1 − w i ). The inbreeding homophily index is then \(\mathrm{IH} \equiv \sum _{i}w_{i} \times \mathrm{IH}_{i}\) [80].

  14. 14.

    Discussing learning and adaptation algorithms is beyond the scope of this book. You can find a short introduction to this field specifically for economic modelling in, e.g., [48]. For more details, see books on artificial intelligence.

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  1. Faculty of Economics and Management, University of Latvia, Riga, Latvia

    Alexander Tarvid

  2. Riga Business School, Riga Technical University, Riga, Latvia

    Alexander Tarvid

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Tarvid, A. (2016). Using Agent-Based Modelling in Studying Labour–Education Market System. In: Agent-Based Modelling of Social Networks in Labour–Education Market System. SpringerBriefs in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-26539-1_3

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