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When demography met social simulation: a tale of two modelling approaches

When demography met social simulation: a tale of two modelling approaches
When demography met social simulation: a tale of two modelling approaches
In this paper we present an agent-based model of a human population, designed to illustrate the potential synergies between demography and agent-based social simulation. In the modelling process, we take advantage of the perspectives of both disciplines: demography being more focused on matching statistical models to empirical data, and social simulation on explanations of social mechanisms underlying the observed phenomena. This work is based on earlier attempts to introduce agent-based modelling to demography, but extends them into a multi-level and multi-state framework. We illustrate our approach with a proof-of-concept model of partnership formation and changing health status over the life course. In addition to the agent-based component, the model includes empirical elements based on demographic data for the United Kingdom. As such, the model allows analysis of the demographic dynamics at a variety of levels, from the individual, through the household, to the whole population. We bolster this analysis further by using statistical emulation techniques, which allow for in-depth investigation of the interaction of model parameters and of the resulting output uncertainty. We argue that the approach — although not fully predictive per se — has four important advantages. First, the model is capable of studying the linked lives of simulated individuals in a variety of scenarios. Second, the simulations can be readily embedded in the relevant social or physical spaces. Third, the approach allows for overcoming some data-related limitations, augmenting the available statistical information with assumptions on behavioural rules. Fourth, statistical emulators enable exploration of the parameter space of the underlying agent-based models
complexity science, demography, health care, scenario generation
9
Silverman, Eric
641120a2-6584-46a6-a33b-4ea9133463af
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Hilton, Jason
da31e515-1e34-4e9f-846d-633176bb3931
Cao, Viet Dung
2d688630-bdf3-495b-8484-d88908d91f2f
Noble, Jason
440f07ba-dbb8-4d66-b969-36cde4e3b764
Silverman, Eric
641120a2-6584-46a6-a33b-4ea9133463af
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Hilton, Jason
da31e515-1e34-4e9f-846d-633176bb3931
Cao, Viet Dung
2d688630-bdf3-495b-8484-d88908d91f2f
Noble, Jason
440f07ba-dbb8-4d66-b969-36cde4e3b764

Silverman, Eric, Bijak, Jakub, Hilton, Jason, Cao, Viet Dung and Noble, Jason (2013) When demography met social simulation: a tale of two modelling approaches. Journal of Artificial Societies and Social Simulation, 16 (4), 9.

Record type: Article

Abstract

In this paper we present an agent-based model of a human population, designed to illustrate the potential synergies between demography and agent-based social simulation. In the modelling process, we take advantage of the perspectives of both disciplines: demography being more focused on matching statistical models to empirical data, and social simulation on explanations of social mechanisms underlying the observed phenomena. This work is based on earlier attempts to introduce agent-based modelling to demography, but extends them into a multi-level and multi-state framework. We illustrate our approach with a proof-of-concept model of partnership formation and changing health status over the life course. In addition to the agent-based component, the model includes empirical elements based on demographic data for the United Kingdom. As such, the model allows analysis of the demographic dynamics at a variety of levels, from the individual, through the household, to the whole population. We bolster this analysis further by using statistical emulation techniques, which allow for in-depth investigation of the interaction of model parameters and of the resulting output uncertainty. We argue that the approach — although not fully predictive per se — has four important advantages. First, the model is capable of studying the linked lives of simulated individuals in a variety of scenarios. Second, the simulations can be readily embedded in the relevant social or physical spaces. Third, the approach allows for overcoming some data-related limitations, augmenting the available statistical information with assumptions on behavioural rules. Fourth, statistical emulators enable exploration of the parameter space of the underlying agent-based models

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More information

e-pub ahead of print date: 31 October 2013
Keywords: complexity science, demography, health care, scenario generation
Organisations: Social Statistics & Demography, Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 359637
URI: https://eprints.soton.ac.uk/id/eprint/359637
PURE UUID: d89c6b21-90d1-4e12-b84f-be6858f95471
ORCID for Jakub Bijak: ORCID iD orcid.org/0000-0002-2563-5040
ORCID for Jason Hilton: ORCID iD orcid.org/0000-0001-9473-757X

Catalogue record

Date deposited: 07 Nov 2013 14:01
Last modified: 26 Nov 2019 01:41

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