Specification of MiniDemographicABM.jl: A simplified agent-based demographic model of the UK

• URL: http://arxiv.org/abs/2307.16548v2
• Date: Fri, 20 Oct 2023 11:41:12 GMT
• Title: Specification of MiniDemographicABM.jl: A simplified agent-based demographic model of the UK
• Authors: Atiyah Elsheikh
• Abstract summary: This documentation specifies a non-calibrated demographic agent-based model of the UK. In the presented model, individuals of an initial population are subject to ageing, deaths, births, divorces and marriages. The model serves as a base implementation to be adjusted to realistic large-scale socio-economics, pandemics or immigration studies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This documentation specifies a simplified non-calibrated demographic agent-based model of the UK, a largely simplified version of the Lone Parent Model presented in [Gostolil and Silverman 2020]. In the presented model, individuals of an initial population are subject to ageing, deaths, births, divorces and marriages throughout a simplified map of towns of the UK. The specification employs the formal terminology presented in [Elsheikh 2023a]. The main purpose of the model is to explore and exploit capabilities of the state-of-the-art Agents.jl Julia package [Datseris2022] in the context of demographic modeling applications. Implementation is provided via the Julia package MiniDemographicABM.jl [Elsheikh 2023b]. A specific simulation is progressed with a user-defined simulation fixed step size on a hourly, daily, weekly, monthly basis or even an arbitrary user-defined clock rate. The model can serve for comparative studies if implemented in other agent-based modelling frameworks and programming languages. Moreover, the model serves as a base implementation to be adjusted to realistic large-scale socio-economics, pandemics or immigration studies mainly within a demographic context.

Related papers

• Affordances Enable Partial World Modeling with LLMs [68.52975612311575]
  We show that agents achieving task-agnostic, language-conditioned intents possess predictive partial-world models informed by affordances.<n>In the multi-task setting, we introduce distribution-robust affordances and show that partial models can be extracted to significantly improve search efficiency.
  arXiv  Detail & Related papers  (2026-02-11T00:25:25Z)
• Intention-Conditioned Flow Occupancy Models [69.79049994662591]
  Large-scale pre-training has fundamentally changed how machine learning research is done today.<n>Applying this same framework to reinforcement learning is appealing because it offers compelling avenues for addressing core challenges in RL.<n>Recent advances in generative AI have provided new tools for modeling highly complex distributions.
  arXiv  Detail & Related papers  (2025-06-10T15:27:46Z)
• Demographic User Modeling for Social Robotics with Multimodal Pre-trained Models [4.2185937778110825]
  We introduce two datasets specifically curated to represent demographic characteristics from user facial images. We evaluate the performance of a prominent contrastive multimodal pre-trained model, CLIP, on these datasets. To address this, we propose adopting a masked image modeling strategy to improve generalization and better capture subtle demographic attributes.
  arXiv  Detail & Related papers  (2025-02-15T02:38:58Z)
• Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations [49.908708778200115]
  We are the first to specialize large language models (LLMs) for simulating survey response distributions. As a testbed, we use country-level results from two global cultural surveys. We devise a fine-tuning method based on first-token probabilities to minimize divergence between predicted and actual response distributions.
  arXiv  Detail & Related papers  (2025-02-10T21:59:27Z)
• A Hitchhiker's Guide to Scaling Law Estimation [56.06982415792523]
  Scaling laws predict the loss of a target machine learning model by extrapolating from easier-to-train models with fewer parameters or smaller training sets. We estimate more than 1000 scaling laws, then derive a set of best practices for estimating scaling laws in new model families.
  arXiv  Detail & Related papers  (2024-10-15T17:59:10Z)
• GenSim: A General Social Simulation Platform with Large Language Model based Agents [111.00666003559324]
  We propose a novel large language model (LLMs)-based simulation platform called textitGenSim.<n>Our platform supports one hundred thousand agents to better simulate large-scale populations in real-world contexts.<n>To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform.
  arXiv  Detail & Related papers  (2024-10-06T05:02:23Z)
• Causal Estimation of Memorisation Profiles [58.20086589761273]
  Understanding memorisation in language models has practical and societal implications. Memorisation is the causal effect of training with an instance on the model's ability to predict that instance. This paper proposes a new, principled, and efficient method to estimate memorisation based on the difference-in-differences design from econometrics.
  arXiv  Detail & Related papers  (2024-06-06T17:59:09Z)
• Promising and worth-to-try future directions for advancing state-of-the-art surrogates methods of agent-based models in social and health computational sciences [0.0]
  Execution and runtime performance of model-based analysis tools for realistic large-scale ABMs can be excessively long. The main aim of this ad-hoc brief report is to highlight some of surrogate models that were adequate and computationally less demanding for nonlinear dynamical models.
  arXiv  Detail & Related papers  (2024-03-07T11:30:56Z)
• The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute [66.84421705029624]
  We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
  arXiv  Detail & Related papers  (2023-09-20T10:31:17Z)
• Formal specification terminology for demographic agent-based models of fixed-step single-clocked simulations [0.0]
  This document presents adequate formal terminology for the mathematical specification of a subset of Agent Based Models (ABMs) The proposed terminology further improves the model understanding and can act as a stand-alone protocol for the specification.
  arXiv  Detail & Related papers  (2023-08-24T20:57:07Z)
• Scaling Laws Do Not Scale [54.72120385955072]
  Recent work has argued that as the size of a dataset increases, the performance of a model trained on that dataset will increase. We argue that this scaling law relationship depends on metrics used to measure performance that may not correspond with how different groups of people perceive the quality of models' output. Different communities may also have values in tension with each other, leading to difficult, potentially irreconcilable choices about metrics used for model evaluations.
  arXiv  Detail & Related papers  (2023-07-05T15:32:21Z)
• Questioning the Survey Responses of Large Language Models [18.61486375469644]
  We critically examine language models' survey responses on the basis of the well-established American Community Survey by the U.S. Census Bureau. We find that models' responses are governed by ordering and labeling biases, leading to variations across models that do not persist after adjusting for systematic biases. Our findings suggest caution in treating models' survey responses as equivalent to those of human populations.
  arXiv  Detail & Related papers  (2023-06-13T17:48:27Z)
• A deep learning framework to generate realistic population and mobility data [5.180648702293017]
  Census and Household Travel Survey datasets are regularly collected from households and individuals. These datasets often represent a limited sample of the population due to privacy concerns or are given aggregated. We propose a framework to generate a synthetic population that includes both socioeconomic features (e.g., age, sex, industry) and trip chains (i.e., activity locations)
  arXiv  Detail & Related papers  (2022-11-14T14:05:09Z)
• A Robust Bias Mitigation Procedure Based on the Stereotype Content Model [0.0]
  We adapt existing work to demonstrate that the Stereotype Content model holds for contextualised word embeddings. We find the SCM terms are better able to capture bias than demographic terms related to pleasantness. We present this work as a prototype of a debiasing procedure that aims to remove the need for a priori knowledge of the specifics of bias in the model.
  arXiv  Detail & Related papers  (2022-10-26T08:13:58Z)
• Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning [92.89846887298852]
  Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data. Give access to a set of expert models and their predictions alongside some limited information about the dataset used to train them.
  arXiv  Detail & Related papers  (2022-10-11T10:20:31Z)
• Mismatched No More: Joint Model-Policy Optimization for Model-Based RL [172.37829823752364]
  We propose a single objective for jointly training the model and the policy, such that updates to either component increases a lower bound on expected return. Our objective is a global lower bound on expected return, and this bound becomes tight under certain assumptions. The resulting algorithm (MnM) is conceptually similar to a GAN.
  arXiv  Detail & Related papers  (2021-10-06T13:43:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.