Synthetic Population Generation

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18 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Sep 29, 2026

MetaPersona: Task-Grounded Synthetic Populations from Empirical Social Science

Personas used to seed LLM social simulations face a cold-start problem: existing methods lack a principled basis for deciding which attributes to include and how to assign their values. As a result, synthetic populations may misrepresent the demographic composition, latent attributes, and dependency structure that shape downstream behavior. We introduce MetaPersona-DB, a dataset of 11,000+ empirical human-subjects studies annotated with task-relevant variables, reported relationships, and aggregate-level population statistics. Building on this resource, we propose MetaPersona, a framework that retrieves task-relevant evidence, constructs literature-derived persona dependency graphs, and samples synthetic populations from empirical priors linking demographics, latent attributes, and outcomes. Across three downstream case studies, three baselines, and three frontier models, results vary by task and model: MetaPersona performs strongly on misinformation belief and AI-tool sentiment, while results on income redistribution are mixed. It also reduces persona-construction cost to under $0.5 per task using GPT-5.2. Finally, we present MetaPersona-Studio, a prototype interactive interface for empirically grounded persona generation.

Jinyi Ye, Yuangang Li, Chenxiao Yu +7

University of Southern California · University of California, Irvine · 2077AI +1

Personality Modeling in Language Models Synthetic Data Generation Computational Social Science Synthetic Population Generation Large Language Model-Based Social Simulation

Sep 24, 2026

Artificial Societies Benchmark: A Validation Framework for Synthetic Research

A synthetic survey can reproduce the average answer while misrepresenting how people differ, how their answers relate to one another, or how they respond to changes in conditions. We introduce the Artificial Societies Benchmark to help researchers assess whether synthetic populations support their intended analyses. The framework combines eleven tests across internal, construct, and external validity, drawing on twenty human sources and comparing nine language models. It connects each research use to the evidence it requires and tests how results change with the information we supply about respondents. Importantly, strong performance in one domain does not establish fidelity in the others. Models often answer too consistently, compress response scales, and alter relationships between traits whilst richer profiles improve prediction for some models and worsen it for others. The resulting scorecard helps researchers identify which aspects of a synthetic population can support their analysis and where researchers need further human evidence.

Edoardo Chidichimo, Min Jun Jung, Felix P. S. Wallis +1

Artificial Societies University of Oxford, UK

Benchmark Design Computational Social Science Psychometric Validation Large Language Model-Based Social Simulation Synthetic Data Evaluation Synthetic Population Generation

Sep 23, 2026

KITE: Scaling Jev Population Experiments with Sparse Flagship Calibration

KITE queries a typed behavioral kernel once per unique state, then executes populations of any size from the table with event-keyed randomness and common random numbers. An expensive flagship model is reserved for sparse paired anchors that estimate intervention effects. Measured human-model discrepancy is propagated as shared error into every conclusion. Population-experiment cost thus scales with unique states and anchors, while uncertainty is governed by evidence about people rather than Monte Carlo noise. On Epstein experiments with 9,070 participants, anchors covering 1.7% of states reduced effect error by 41% (absolute MAE reduction 0.0125). On 37 held-out SocSci210 experiments, 0.5-1.5% anchor coverage raised captured decision gain from 0.27 to 0.39. The kernel passed content-fidelity criteria in all 15 new countries of a 16-country study. Shared discrepancy yielded retrospective coverage of 93% and 96% at nominal 80% and 90%, versus 29% and 36% from human sampling uncertainty alone. A million agents executed 20 tabulated steps in 0.9 seconds on a laptop. This architecture offers a route to screening candidate interventions before human trials, multi-country content audits, and uncertainty-aware policy comparison at the cost of a few thousand kernel calls with sparse flagship anchors. Property-specific evidence records connect each use to its validation scope, correction provenance, and uncertainty, making these applications auditable.

Hengyu Li

The University of Tokyo

Causal Effect Estimation Uncertainty Quantification Synthetic Population Generation Large Language Model-Based Social Simulation Uncertainty Calibration

Sep 14, 2026

The average-farmer illusion in language-model simulations of agricultural decisions

Language-model agents are increasingly used as synthetic people in surveys and social simulations, yet their apparent realism is often judged from population averages or distributional similarity. We tested what such evidence actually establishes by comparing Claude, Codex and Kimi under four prespecified prompt designs with matched farmer decisions from China and four African countries. Some configurations reproduced observed means and adoption rates. However, their person-level predictions were weak; their decisions clustered around typical values and policy-relevant extremes were largely missing. Most strikingly, a simple generator fitted only to the observed marginal dis- tribution, and given no information about any farmer, achieved greater distributional similarity than every language-model configuration. Prompt additions produced conditional gains rather than uni- versal improvement: results varied with model, outcome, population and validation target. We call this the average-farmer illusion: a synthetic population can look realistic while failing to repro- duce who does what or how behaviour varies. We provide a claim-matched validation framework and reusable modular prompts that turn prompt construction into an auditable experimental process. Population-level resemblance should therefore be treated as the start of validation, not as evidence of individual simulation.

Zhanliang Zhu, Ziwei Li, Yuchen Liu +5

1State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China · 2Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China · 3Meta Platforms Inc. +2

Synthetic Data Generation Synthetic Population Generation LLM Agents Large Language Model-Based Social Simulation

Aug 16, 2026

Feasible and Novel Synthetic Population Generation with Tabular and Sequential Travel Attributes

Synthetic populations are critical inputs for activity-based travel demand models, yet generating realistic populations from limited survey data remains challenging. Small samples miss valid attribute combinations, known as sampling zeros, and generative models may also produce infeasible structural zeros. Moreover, realistic synthetic populations must capture both static socio-demographic attributes and sequential travel behaviour, such as trip chains. This paper proposes a regularized two-stage generative framework to address these challenges, where regularization refers to additional loss terms that guide the generator toward broader valid coverage and fewer infeasible samples. In Stage 1, a Wasserstein GAN with gradient penalty is augmented with three regularization terms, IGP, LDR, and CLAP, to improve feasibility, diversity, and novelty in tabular population synthesis. In Stage 2, Transformer and LSTM-Attention models generate sequential travel attributes, including departure time, trip purpose, and travel mode, conditioned on the synthesized tabular profiles. We also introduce novelty and count-aware metrics to evaluate whether valid unseen combinations are recovered and generated in realistic proportions. Results show that regularized models outperform the vanilla WGAN-GP across feasibility, diversity, and novelty. Regularization increases feasibility by 2.1 to 3.7 percentage points and novelty by 6.6 to 10.0 percentage points, improving sampling-zero recovery without sacrificing feasibility. The F1 score improves by 6.3 to 8.6 percentage points. For sequential attributes, LSTM-Attention best matches the trip-length distribution, while Transformer achieves higher overall sequential F1, 90.6% versus 89.1%. Cross-stage validation confirms strong consistency between generated mobility status and generated trip chains.

Farbod Abbasi, Zachary Patterson, Bilal Farooq

Concordia University, Montreal, Quebec, Canada · Toronto Metropolitan University, Toronto, Ontario, Canada

Synthetic Population Generation Synthetic Data Generation Synthetic Tabular Data Generation Generative Adversarial Networks Conditional Generative Modeling

Jul 17, 2026

Distribution-First Population Simulation: Collapse, Calibration, and Recall in Non-WEIRD LLM Persona Modeling

Synthetic-population tools increasingly run every individual as an independent large language model (LLM) agent. Using real survey microdata, we show that this paradigm has a basic failure mode, and we set a distribution-first corrective against it, all measured with a deterministic, construct-validated verifier on non-WEIRD (Turkey-first) data. First, N independent LLM agents grounded on 2,414 real World Values Survey respondents fail to reproduce the population's response distribution: they pile onto a modal default (four scenarios x five seeds: concentration 0.36->0.69, entropy 1.46->0.77, 85% collapse, TVD=0.44), and the collapse is a predictable function of scenario structure (r=0.55 with a single-answer structure). Second, Verbalized Sampling (VS) fixes the field's chronic under-dispersion without training in three model families (fidelity +7 to +10; significant on Qwen, p=0.002, d=6.2), yet the same move universally overshoots into over-dispersion (SD-ratio 0.4-0.56 -> 1.26-1.37), a structural property of VS. Third, survey fidelity transfers only weakly to agentic behavior: in a single-model, single-domain booking task, a persona is dominated by a cheapest-default (~80%) that income modulates but does not override (comfort choice 0%->7%->32% across income bands). Fourth, a placebo-controlled memorization attack and an election backtest show VS keeps aggregate strength while subgroup and individual claims are contaminated by recall and underdetermination. We close with the corrective: model the distribution once (VS) and assign it to grounded characters at O(1) cost, with a budget-aware router whose honest AUC is 0.805, not the tautological 1.0 of a code-derived oracle. The central contribution needs no realism claim: it measures the internal inconsistency of the independent-agent route and the conditions under which the distribution-first route calibrates.

Gurkan Ozkan

Istanbul Technical University

Human Behavior Simulation Large Language Model-Based Social Simulation Synthetic Data Generation for Language Models Persona-Conditioned Generation Synthetic Population Generation

Jul 13, 2026

Trustworthy synthetic data for campaign decision support: strategy simulation fidelity and the PolicySynth framework

Decision support systems (DSS) increasingly run retention what-if analysis on synthetic customer populations, because privacy constraints preclude unrestricted use of real data. Such a system is trustworthy only if the synthetic data lead managers to the same decisions as the real data would; yet prevailing criteria certify distributional similarity, not decision alignment, so a synthetic population can match every marginal distribution while still steering a marketing team toward the wrong campaigns. We close this decision-alignment gap with three contributions: strategy simulation fidelity (SSF), a criterion measuring how often the synthetic population yields the same go/no-go campaign decision as the real population; PolicySynth, a DSS framework whose generator is conditioned on the production churn scorer to align decision-relevant structure; and a three-axis reporting standard of decision alignment, membership-inference resistance, and novel-record rate as the minimum deployment quality gate. On a telecommunications churn corpus and a banking acquisition corpus, PolicySynth attains a mean SSF of 0.923 and 0.960, with seed-to-seed variance roughly ten times tighter than CTGAN on telecommunications and 2.5 times on banking. This stability is the deployable property: go/no-go recommendations shift by at most 1.2 percentage points between monthly retraining cycles, against 11.5 for CTGAN, a reversed recommendation on one campaign in nine. A bootstrap baseline matches PolicySynth on SSF yet copies real records verbatim and fails membership inference, evidence that no single axis suffices. PolicySynth reliably supports directional go/no-go screening; its ROI estimates diverge from real outcomes by 70 to 78% and require the volume correction we document.

Tung Dang, The Hung Phung, Son Lam Nguyen +1

aGraduate School of Science, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan · bFaculty of Human Resource Management, Trade Union University, 169 Tay Son, Kim Lien, Hanoi, Vietnam · cInstitute for Business Management Development, Academy Of Finance, 58 Le Van Hien Street, Dong Ngac Ward, Hanoi, Vietnam +1

AI-Assisted Decision Making Synthetic Data Generation Synthetic Data Evaluation Synthetic Population Generation

Jul 1, 2026

Calibrating the Instrument: Controllability of an LLM-Driven Synthetic Population

Generative Synthetic Populations (GSP) -- the convergence of population synthesis, agent-based modelling, and LLM agents -- are attracting growing interest for urban simulation and institutional communication research. Before any GSP instrument is used on a real population, a more basic question must be answered: does it respond to stimuli of known valence in an ordered, replicable, group-structured way? We call this controllability. We ask not whether a synthetic population tracks humans, but whether it tracks itself: whether the latent structure we impose on it is recovered in its own responses. This internal-validity question is logically prior to any claim about external validity, just as characterising an instrument's response function must precede using it to test a theory. We report SIVE (Synthetic Instrument Validation Experiment): a fictional municipality (Montelago) with 120 synthetic personas of known latent structure, exposed to seven conditions spanning strongly positive to strongly negative institutional communications about a water network. Seven pre-registered criteria, evaluated across a temperature sweep, jointly assess fidelity, stability, noise floor, specificity, sensitivity, and ordering. All seven pass at every temperature. A central finding turns a calibration failure into a diagnostic success: a message designed as "weakly positive" was identified by the instrument as functionally negative, traced to unresolved problems, uncertainty, and institutional passivity in its text; a redesigned version restored the expected ordering and interacts with agents' latent trust in unanticipated ways. A noise sub-experiment shows the instrument's intrinsic noise is roughly half the cross-agent estimate and stable across temperatures. Individual trajectories reveal coherent micro-dynamics that summary statistics obscure. Full data are available via an interactive explorer.

Mirko Degli Esposti

Department of Physics and Astronomy, University of Bologna

Human Behavior Simulation Synthetic Data Generation LLM Agent Evaluation Synthetic Population Generation

Jun 30, 2026

A Filtered Mixture-of-Generators for Fully Synthetic Survival Training

Survival analysis models time-to-event data, but in clinical settings training data are costly and scarce: events accrue over years of follow-up, cohorts are small, and privacy regulations restrict sharing across institutions. Tabular generative models promise augmentation and privacy-preserving cohort sharing, yet are themselves data-hungry -- on the small cohorts typical of survival analysis, a single generator rarely characterizes the population well enough for downstream models trained on its output to match real-data performance. FoGS (Filtered Mixture-of-Generators for Survival analysis) reframes synthetic-data construction as sample selection rather than generation. A candidate pool is drawn from four architecturally distinct tabular generators, and each sample is scored by an ensemble of seven survival models trained on real data, using proper scoring rules as a per-sample plausibility proxy. A two-level pipeline optimizes, in its outer loop, a selection policy -- generator quotas, scorer weights, a random complement, and stratified balancing on event time and censoring -- against held-out downstream performance, while an inner loop tunes the downstream model (XGBoost-Cox). On 16 public datasets under train-on-synthetic, test-on-real (C-index and IBS,

0

--

100

scale), FoGS yields mean improvements of

+2.17

in C-index and

+0.67

in IBS, improving both metrics on 9 of 16 datasets and at least one on 13 (one-sided Wilcoxon

p=0.039

and

p=0.035

). It matches or exceeds real-data training on most cohorts, with no significant change in nearest-neighbour privacy margin relative to unfiltered sampling. Sample filtering over a heterogeneous generator pool is thus a viable substitute for real-data training in privacy-restricted clinical settings.

Niccolò Maria Rizzi, Eugenio Lomurno, Alberto Archetti +1

Politecnico di Milano, Milan, Italy

Survival Analysis Synthetic Data Generation Synthetic Data Evaluation Synthetic Data Selection Synthetic Population Generation

Jun 26, 2026

GenWorld: Empirically Grounded Urban Simulation Infrastructure for Scalable LLM-Agent Studies

LLM-agent simulation faces a joint grounding and scaling problem: agents should act in environments that reflect real urban constraints, yet direct online LLM calls for city-scale populations are computationally prohibitive. We present GenWorld, an empirically grounded urban simulation infrastructure that combines a building-level synthetic city, a structured agent-environment interface, and offline compilation of LLM-derived decision signals into lookup policies for scalable rollout. In a reference instantiation for Higashihiroshima, Japan, GenWorld grounds 196,608 synthetic residents in census and geospatial data, validates demographic consistency against census tabulations, and uses YJMob100K mobile-phone data as a commuting-distance diagnostic. We demonstrate the infrastructure through three reproducible cases: a full-city weekday rollout, a weekday-weekend behavioral contrast, and a warning-response perturbation with auditable replanning traces. These cases support GenWorld as a reproducible platform for grounded and scalable LLM-agent studies, while calibrated forecasting for traffic, evacuation, or policy outcomes remains future work.

Gen Li, Jieyuan Lan, Pengcheng Xu +3

Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima, Japan · Jiangxi Polytechnic University, Jiangxi, China · College of Urban Development and Modern Transportation, Xi’an University of Architecture and Technology, Xi’an, China +1

Human Behavior Simulation Synthetic Data Generation Synthetic Population Generation Agent-Based Modeling Large Language Model-Based Social Simulation

Jun 24, 2026

ProfileFoundry: A Synthetic Person-Object Substrate for Privacy, Memory, and Tool-Use Evaluation in LLM Agent

Foundation-model research increasingly needs data about people: user state, personal histories, relationships, contact-like fields, documents, and longitudinal updates. Real user data is difficult to share, perturb, audit, or redistribute responsibly, while independently generated fake fields rarely preserve the cross-field and temporal consistency needed for controlled evaluation. We present PROFILEFOUNDRY, a deterministic generator and fixed reference release of 100,000 adult synthetic Person Objects across eight locales. Each object combines a typed current snapshot, household, family, and employer links, snapshot-aligned events, normalized relational views, and generation provenance. The release contains 709,228 events, 40,338 households, 52,491 employers, and 518,564 directed relationship edges. We report evidence in separate categories: selected population-marginal comparisons, per-object invariant checks, release-wide referential and temporal closure, and coincidence/provenance screens. PROFILEFOUNDRY is not a population-fidelity model, a rendered-text corpus, or a formal privacy mechanism. Instead, it is a responsible synthetic source layer for constructing downstream foundation-model evaluations involving memory, privacy, document understanding, record linkage, and agent state while keeping the synthetic person behind each artifact inspectable

Sriram Selvam, Anneswa Ghosh

Synthetic Data Generation Synthetic Benchmark Generation User Modeling Synthetic Data Evaluation Synthetic Data Generation for LLM Agents Synthetic Population Generation

Jun 2, 2026

Conditional Latent Diffusion Model with Fourier-based Motion Modelling for Virtual Population Synthesis

In-silico trials of medical devices require the generation of virtual populations of anatomies. In cardiovascular applications, virtual anatomy is typically represented as a 3D+t mesh sampled from a generative model. However, most existing mesh generators focus on static anatomy, while sequence models often lack explicit periodicity. To this end, we propose 4D F-MeshLDM, a conditional generative framework comprising a convolutional mesh VAE to encode meshes, a structural latent space that parameterises motion using a truncated Fourier series, and a diffusion prior that learns the latent distribution over Fourier coefficient tokens. By conditioning the diffusion process on clinical covariates via affine modulation, we enable controllable synthesis. Sampling tokens and performing inverse Fourier synthesis yield cycle-consistent latent trajectories, which can be decoded into 3D+t cardiac mesh sequences. Experiments on 5,000 UK Biobank subjects demonstrate that 4D F-MeshLDM outperforms state-of-the-art baselines in anatomical fidelity and achieves near-zero cycle closure error. Furthermore, the generated cohorts accurately preserve clinical functional indices, highlighting the potential of our framework for reliable in-silico cardiac trials.

Shaokun Lan, Haoran Dou, Jinghan Huang +5

Centre for Computational Imaging and Modelling in Medicine (CIMIM), University of Manchester, Manchester, UK

3D Mesh Generation Synthetic Population Generation Conditional Diffusion Models

Apr 23, 2026

Using Zero-Shot LLM-Generated Survey Data for Geographically Explicit Population Synthesis

There is a growing interest in utilizing synthetic populations for a diverse range of applications. At the same time, we are witnessing a tremendous growth in artificial intelligence in all walks of life. This paper evaluates whether zero-shot large language model (LLM)-generated health survey data can serve as inputs to a conventional iterative proportional fitting (IPF) workflow for geographically explicit population synthesis. Using the 2023 Behavioral Risk Factor Surveillance System (BRFSS), we generate synthetic survey records for the U.S. states of Colorado and Mississippi with GPT-4.1 and Gemini-2.5-Pro. We use the generated data in an IPF-based synthesis pipeline and evaluate the resulting census tract-level synthetic populations against external benchmarks. Results show both LLMs capture several major state-level contrasts, indicating zero-shot generation produces geographically differentiated survey data. However, performance is strongly variable-dependent. Downstream effects in population synthesis are mixed, as IPF sometimes amplifies or reduces errors in the generated data. Spatial validation shows that LLM-based populations reproduce census tract-level patterns reasonably well, especially for variables that were more aligned with the ground truth data. Overall, the LLM-generated survey data shows promise as supplementary input, but not yet as a replacement for real survey data.

Taylor Anderson, Sara Von Hoene, Orhan Yagizer Cinar +4

Dept. of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA · Dept. of Computer Science, George Mason University, Fairfax, VA, USA · College of Public Health, George Mason University, Fairfax, VA, USA +2

Synthetic Data Generation Synthetic Population Generation Synthetic Data Evaluation

Apr 19, 2026

Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics

We introduce Prior-Fitted Functional Flows, a generative foundation model for pharmacokinetics that enables zero-shot population synthesis and individual forecasting without manual parameter tuning. We learn functional vector fields, explicitly conditioned on the sparse, irregular data of an entire study population. This enables the generation of coherent virtual cohorts as well as forecasting of partially observed patient trajectories with calibrated uncertainty. We construct a new open-access literature corpus to inform our priors, and demonstrate state-of-the-art predictive accuracy on extensive real-world datasets.

César Ojeda, Niklas Hartung, Wilhelm Huisinga +6

University of Potsdam, Germany · Technical University Berlin, Germany · Freie Universität Berlin, Germany +2

Probabilistic Time Series Forecasting Healthcare Synthetic Data Generation Zero-Shot Time Series Forecasting Continuous Normalizing Flows Prior-Data Fitted Networks+2

Apr 8, 2026

Validated Synthetic Patient Generation for Small Longitudinal Cohorts: Coagulation Dynamics Across Pregnancy

Small longitudinal cohorts, common in maternal health, rare diseases, and early-phase trials, limit computational modeling because enrollment is slow and the data are too sparse to train reliable models. We present multiplicity-weighted Stochastic Attention (SA), a generative framework based on modern Hopfield networks. Stochastic attention stores real patient profiles as memory patterns in a continuous energy landscape. The resulting distribution is a finite mixture with one component centered on each stored profile. We generated the reported cohort with Langevin dynamics; direct sampling from the same distribution reproduced its fidelity and membership-inference results. Multiplicity weights amplify selected subgroups at inference time without retraining. We applied the method to longitudinal coagulation data from 23 patients, with 72 features measured before pregnancy and during the first and third trimesters. The cohort included three patients with polycystic ovary syndrome and five who developed preeclampsia. Across statistical, structural, and mechanistic tests, including a separately specified coagulation model that was blind to the generator but calibrated on the same cohort, the synthetic profiles closely matched the real profiles under the measures applied at this sample size. A model calibrated on synthetic profiles predicted held-out real-patient TGA measurements as well as one calibrated on real profiles. These results provide proof of concept for using SA in the tested modeling tasks, including mechanistic calibration and hypothesis generation, with very small longitudinal cohorts.

Jeffrey D. Varner, Maria Cristina Bravo, Carole McBride +2

Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14850 · Department of Biochemistry, The Robert Larner, M.D. College of Medicine, University of Vermont Medical Center, Burlington, VT 05401 · Department of Obstetrics, Gynecology, and Reproductive Sciences, The Robert Larner, M.D. College of Medicine, University of Vermont Medical Center, Burlington, VT 05401

Synthetic Data Generation Synthetic Tabular Data Generation Synthetic Population Generation Synthetic Data Evaluation Hopfield Networks

Mar 28, 2026

Scalable Maximum Entropy Population Synthesis via Persistent Contrastive Divergence

Maximum entropy (MaxEnt) modelling provides a principled framework for generating synthetic populations from aggregate census data, without access to individual-level microdata. The bottleneck of exact-enumeration approaches is expectation computation by explicit summation over the full tuple space

\cX

, which becomes infeasible for more than

K \approx 20

categorical attributes; sampling-based alternatives exist but rely on Metropolis-type schemes that require proposal tuning and rejection steps. We propose GibbsPCDSolver, a stochastic replacement for this computation based on Persistent Contrastive Divergence (PCD): a persistent pool of

N

synthetic individuals is updated by Gibbs sweeps at each gradient step, providing a stochastic approximation of the model expectations without ever materialising

\cX

. We validate the approach on controlled benchmarks and on Syn-ISTAT, a

K{=}15

Italian demographic benchmark with analytically exact marginal targets derived from ISTAT-inspired conditional probability tables. Scaling experiments across

K \in \{12, 20, 30, 40, 50\}

confirm that GibbsPCDSolver maintains

\MRE \in [0.010, 0.018]

while

|\cX|

grows eighteen orders of magnitude, with runtime scaling as

O(K)

rather than

O(|\cX|)

. On Syn-ISTAT, GibbsPCDSolver reaches

\MRE{=}0.03

on training constraints and -- crucially -- produces populations with effective sample size

\Neff = N

versus

\Neff \approx 0.012\,N

for generalised raking, an

86.8{\times}

diversity advantage that is essential for agent-based urban simulations.

Mirko Degli Esposti

Department of Physics and Astronomy, City Science Lab University of Bologna, Bologna, Italy

Synthetic Population Generation Synthetic Data Generation

Mar 23, 2026

Maximum Entropy Relaxation of Multi-Way Cardinality Constraints for Synthetic Population Generation

Generating synthetic populations from aggregate statistics is a core component of microsimulation, agent-based modeling, policy analysis, and privacy-preserving data release. Beyond classical census marginals, many applications require matching heterogeneous unary, binary, and ternary constraints derived from surveys, expert knowledge, or automatically extracted descriptions. Constructing populations that satisfy such multi-way constraints simultaneously poses a significant computational challenge. We consider populations where each individual is described by categorical attributes and the target is a collection of global frequency constraints over attribute combinations. Exact formulations scale poorly as the number and arity of constraints increase, especially when the constraints are numerous and overlapping. Grounded in methods from statistical physics, we propose a maximum-entropy relaxation of this problem. Multi-way cardinality constraints are matched in expectation rather than exactly, yielding an exponential-family distribution over complete population assignments and a convex optimization problem over Lagrange multipliers. We evaluate the approach on NPORS-derived scaling benchmarks with 4 to 40 attributes and compare it primarily against generalized raking. The results show that MaxEnt becomes increasingly advantageous as the number of attributes and ternary interactions grows, while raking remains competitive on smaller, lower-arity instances.

François Pachet, Jean-Daniel Zucker

ImagineAllThePeople, Paris, France · IRD, UMMISCO, Sorbonne Universit´e, France

Synthetic Population Generation Synthetic Data Generation Constrained Generative Modeling Computational Social Science

Feb 17, 2026

Enhancing Diversity and Feasibility: Joint Population Synthesis from Multi-source Data Using Generative Models

Generating realistic synthetic populations is essential for agent-based models (ABM) in transportation and urban planning. Current methods face two major limitations. First, many rely on a single dataset or follow a sequential data fusion and generation process, which means they fail to capture the complex interplay between features. Second, these approaches struggle with sampling zeros (valid but unobserved attribute combinations) and structural zeros (infeasible combinations due to logical constraints), which reduce the diversity and feasibility of the generated data. This study proposes a novel method to simultaneously integrate and synthesize multi-source datasets using a Wasserstein Generative Adversarial Network (WGAN) with gradient penalty. This joint learning method improves both the diversity and feasibility of synthetic data by defining a regularization term (inverse gradient penalty) for the generator loss function. For the evaluation, we implement a unified evaluation metric for similarity, and place special emphasis on measuring diversity and feasibility through recall, precision, and the F1 score. Results show that the proposed joint approach outperforms the sequential baseline, with recall increasing by 7% and precision by 15%. Additionally, the regularization term further improves diversity and feasibility, reflected in a 10% increase in recall and 1% in precision. We assess similarity distributions using a five-metric score. The joint approach performs better overall, and reaches a score of 88.1 compared to 84.6 for the sequential method. Since synthetic populations serve as a key input for ABM, this multi-source generative approach has the potential to significantly enhance the accuracy and reliability of ABM.

Farbod Abbasi, Zachary Patterson, Bilal Farooq

Concordia University, Montreal, QC, Canada · Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada · Toronto Metropolitan University, Toronto, ON, Canada

Synthetic Population Generation Data Fusion Synthetic Data Generation Synthetic Tabular Data Generation Constrained Generative Modeling Generative Adversarial Networks