Value-Enriched Population Synthesis: Integrating a Motivational Layer

TL;DR

This paper introduces a novel population synthesis framework that incorporates a motivational layer with values, ideologies, and priorities into agent profiles, enhancing the realism of social simulations.

Contribution

It presents a new method combining microdata and macrodata within Bayesian networks to generate synthetic populations with embedded value systems.

Findings

Enhanced agent profiles with motivational attributes

Preservation of socio-demographic distributions in synthetic populations

Potential for more nuanced social simulation decision-making

Abstract

In recent years, computational improvements have allowed for more nuanced, data-driven and geographically explicit agent-based simulations. So far, simulations have struggled to adequately represent the attributes that motivate the actions of the agents. In fact, existing population synthesis frameworks generate agent profiles limited to socio-demographic attributes. In this paper, we introduce a novel value-enriched population synthesis framework that integrates a motivational layer with the traditional individual and household socio-demographic layers. Our research highlights the significance of extending the profile of agents in synthetic populations by incorporating data on values, ideologies, opinions and vital priorities, which motivate the agents' behaviour. This motivational layer can help us develop a more nuanced decision-making mechanism for the agents in social simulation settings. Our methodology integrates microdata and macrodata within different Bayesian network structures. This contribution allows to generate synthetic populations with integrated value systems that preserve the inherent socio-demographic distributions of the real population in any specific region.