Generating Realistic Synthetic Population Datasets

TL;DR

This paper introduces a maximum entropy-based method for generating realistic synthetic population datasets, enabling better modeling of societal phenomena while respecting privacy constraints.

Contribution

It presents a novel maximum entropy framework and an efficient inference algorithm for creating synthetic datasets from categorical data, validated on real and simulated data.

Findings

Effective estimation of data distributions demonstrated

Feasibility shown through epidemic simulation application

Outperforms existing methods in accuracy and efficiency

Abstract

Modern studies of societal phenomena rely on the availability of large datasets capturing attributes and activities of synthetic, city-level, populations. For instance, in epidemiology, synthetic population datasets are necessary to study disease propagation and intervention measures before implementation. In social science, synthetic population datasets are needed to understand how policy decisions might affect preferences and behaviors of individuals. In public health, synthetic population datasets are necessary to capture diagnostic and procedural characteristics of patient records without violating confidentialities of individuals. To generate such datasets over a large set of categorical variables, we propose the use of the maximum entropy principle to formalize a generative model such that in a statistically well-founded way we can optimally utilize given prior information about the data, and are unbiased otherwise. An efficient inference algorithm is designed to estimate the maximum entropy model, and we demonstrate how our approach is adept at estimating underlying data distributions. We evaluate this approach against both simulated data and on US census datasets, and demonstrate its feasibility using an epidemic simulation application.