Agent-Based Simulation of Decision-Making Under Uncertainty to Study Financial Precarity

Financial insecurity in the U.S. is on the rise, accelerated by the growth of the gig economy and the associated income instability, increasing inequality, and the effects of algorithmic decision-making. Such insecurity has been studied within the framework of precarity – a concept that captures people’s latent uncertainty and precariousness. To alleviate precarity, we must study it. Precarity manifests over time as a sequence of events for an individual. Therefore, we must study individual trajectories, rather than the trajectory of aggregate properties of populations or snapshot analysis of an automated decision process. Doing so requires an agent behavior model that can simulate a number of related phenomena simultaneously: how individual consumption reacts to uncertainty in one’s financial status, how predictive tools impact income, and how utility-maximizing individuals behave in the long term. In this paper, we develop an agent-based simulation framework with realistic elements to examine the dynamics of precarity. Our model combines different threads of inquiry in economics and incorporates models of consumption, ruin, and investment. Our results illustrate how precarity, if ignored by policy-makers, can exacerbate the ill-effects of automated decision-making. Our framework also allows us to experiment with different strategies to mitigate precarity and evaluate their effectiveness.