Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race

Warnke, Tom; Reinhardt, Oliver; Klabunde, Anna; Willekens, Frans; Uhrmacher, Adelinde M.

doi:10.1080/00324728.2017.1380960

Cited by 25 publications

(15 citation statements)

References 40 publications

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“…We have implemented the model in the general purpose language Julia (Bezanson et al 2017) as well as in the Modeling Language for Linked Lives (ML3), a domain-specific language for agent-based modeling (Warnke et al 2017).…”

Section: Methodsmentioning

confidence: 99%

“…ML3 models are interpreted by a Java-based simulator (Reinhardt and Uhrmacher 2017) using a variation of Gillespie's Stochastic Simulation Algorithm (Gillespie 1976). The language has been successfully applied to models of human migration and decision processes (Warnke et al 2017;Reinhardt et al 2018). However, the collection and adaptation of knowledge, a central part of this model, was not part of previous models.…”

Section: Methodsmentioning

confidence: 99%

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Developing Agent-Based Migration Models in Pairs

Reinhardt

Uhrmacher

Hinsch

et al. 2019

2019 Winter Simulation Conference (WSC)

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Developing a realistic agent-based model of human migration requires particular care. Committing too early to a specific model architecture, design, or language environment can later become costly in terms of the revisions required. To examine specifically the impact of differences in implementation, we have developed two instances of the same model in parallel. One model is realized in the programming language Julia, the underlying execution semantics is of a discrete stepwise stochastic process. The other is realized in an external domain-specific language ML3, based on a continuous-time Markov chain (CTMC) semantics. By developing models in pairs in different approaches, important properties of the target model can be more effectively revealed. In addition, the realization within a programming language and an external domain-specific modeling language respectively, helped identifying crucial features and trade-offs for the future implementation of the model and the design of the domain-specific modeling language.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Developing Agent-Based Migration Models in Pairs

Reinhardt

Uhrmacher

Hinsch

et al. 2019

2019 Winter Simulation Conference (WSC)

Self Cite

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“…Therefore, a causal model is necessarily a multilevel model (Billari 2015;Courgeau et al 2017). Warnke et al (2017) proposed a new computer language that facilitates the multilevel causal modelling of demographic phenomena. They applied the language in implementing the computer model of international migration developed by Klabunde et al (2017).…”

Section: Causal Forecasting: Focus On Mechanismsmentioning

confidence: 99%

Towards causal forecasting of international migration

Willekens¹

2019

Populationyearbook

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International migration is difficult to predict because of uncertainties. The identification of sources of uncertainty and the measurement and modelling of uncertainties are necessary, but they are not sufficient. Uncertainties should be reduced by accounting for the heterogeneity of migrants, the reasons why some people leave their country while most stay, and the causal mechanisms that lead to those choices. International migration takes place within a context of globalisation, technological change, growing interest in migration governance, and the emergence of a migration industry. Young people are more likely than older people to respond to these contextual factors, as they are better informed, have greater self-efficacy, and are more likely to have a social network abroad than previous generations. My aim in this paper is to present ideas for the causal forecasting of migration. Wolfgang Lutz's demographic theory of socioeconomic change is a good point of departure. The cohort-replacement mechanism, which is central to Lutz's theory, is extended to account for cohort heterogeneity, life-cycle transitions, and learning. I close the paper by concluding that the time has come to explore the causal mechanisms underlying migration, and to make optimal use of that knowledge to improve migration forecasts. *

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“…The model of Klabunde, Zinn, Willekens, & Leuchter (2017) was innovative in focusing on the decision to immigrate. The authors took a life-course approach, including events such as childbirth and marriage as well as the influence of agents' social networks in the country of origin vs. immigration destination (for projects with similar goals, see also Warnke, Reinhardt, Klabunde, Willekens, & Uhrmacher, 2017). One of this team's projects was described using the ODD+D framework (Klabunde, Willekens, Zinn, & Leuchter, 2015).…”

Section: The Rationale For Using Odd: Novel Domainmentioning

confidence: 99%

Human Simulation: Perspectives, Insights, and Applications

Diallo¹,

Wildman²,

Shults³

et al. 2019

New Approaches to the Scientific Study of Religion

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No agent-based model exists of language learning following immigration to a new country. Language learning has features that make it a good fit to ABMs, such as diffusion/adoption effects: people learn language via social interaction and are influenced by other social actors about how and when to invest in learning. Language learning involves positive and negative feedback loops, such that poor progress early in learning can spiral into negativity and avoidance, while early success can accelerate learning. Most importantly, the question of why language learning is difficult for adults is controversial. Should implementers program into models the equations that match the robust age effects observed in data, or should these patterns emerge from multiple factors and actors? To address this, the large literature on foreign language acquisition was reviewed as part of the background of making modeling decisions. Decisions were sufficiently challenging that it was decided to begin with a narrative description, using the Overview, Design Concepts and Details protocol (ODD). The ODD protocol provided an organizing framework in which many details were worked out. These included identifying outcome variables (frequency of use and fluency in the two languages), basic entities (representing individuals, families, neighborhood, global environment), defining rules for initiating and continuing conversation, and rules for agents to move to new locations. Considerable narrative space was used to discuss the rationale for simplifications, as well as decisions that were left for future extensions. Given the complexity of the domain, the narrative description was a necessary foundation to smooth the way for a working simulation.

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Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race

Cited by 25 publications

References 40 publications

Developing Agent-Based Migration Models in Pairs

Developing Agent-Based Migration Models in Pairs

Towards causal forecasting of international migration

Human Simulation: Perspectives, Insights, and Applications

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