Modeling Non-deterministic Human Behaviors in Discrete Food Choices

TL;DR

This paper introduces a non-deterministic model that predicts human food preferences based on demographics, generating synthetic data aligned with behavioral science for use in machine learning applications.

Contribution

The paper presents a novel non-deterministic simulator for human food choices that leverages demographic data, behavioral studies, and NHANES dataset to produce realistic synthetic data.

Findings

Successfully generates synthetic data matching original distribution

Aligns with established behavioral science insights

Applicable to various machine learning tasks involving human behavior

Abstract

We establish a non-deterministic model that predicts a user's food preferences from their demographic information. Our simulator is based on NHANES dataset and domain expert knowledge in the form of established behavioral studies. Our model can be used to generate an arbitrary amount of synthetic datapoints that are similar in distribution to the original dataset and align with behavioral science expectations. Such a simulator can be used in a variety of machine learning tasks and especially in applications requiring human behavior prediction.