MoSeS: Modelling and Simulation for e-Social Science

MoSeS (Modelling and Simulation for e-Social Science) is a research node of the National Centre for e-Social Science. MoSeS uses e-Science techniques to execute an events-driven model that simulates discrete demographic processes; this allows us to project the UK population 25 years into the future. This paper describes the architecture, simulation methodology and latest results obtained by MoSeS.

• Publication year

  2009

• Venue

  Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

• Publication date

  2009-07-13

• Fields of study

  Mathematics, Computer Science, Sociology, Medicine

• Identifiers
  DOI 10.1098/rsta.2009.0041PMID 19487212
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Accessibilità e interoperabilità: il ruolo guida del World Wide Web Consortium (W3C)

  2007cited by this paper
• SimBritain: a spatial microsimulation approach to population dynamics

  2005cited by this paper
• Geography Matters: Simulating the Local Impacts of National Social Policies

  2004cited by this paper
• Tracing very long-term kinship networks using SOCSIM

  2004cited by this paper
• Microsimulation methods for population projection.

  1998cited by this paper
• The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records

  1998cited by this paper
• The Geography of Europe's Futures

  1992cited by this paper
• Microanalytic simulation models to support social and financial policy

  1986cited by this paper
• Requiem for large-scale models

  1973cited by this paper

• synthACS: Spatial Microsimulation Modeling with Synthetic American Community Survey Data

  2022cites this paper
• Fuel performance optimization of U3Si2-SiC design during normal, power ramp and RIA conditions

  2019cites this paper
• Integrating a multi-layer deformation model in FRAPTRAN for accident Tolerant fuel analysis

  2019cites this paper
• Comparison of Iterative Proportional Fitting and Simulated Annealing as synthetic population generation techniques: Importance of the rounding method

  2017cites this paper
• Impacts of compact growth and electric vehicles on future air quality and urban exposures may be mixed.

  2017cites this paper
• Modelling the impacts of demographic ageing on the demand for health care services

  2015cites this paper
• On Geocomputational Determinants of Entropic Variations for Urban Dynamics Studies

  2015cites this paper
• Software for multistate analysis

  2014cites this paper
• An agent-based model of residential mobility: Assessing the impacts of urban regeneration policy in the EASEL district

  2014cites this paper
• Simple, multiple and multiway correspondence analysis applied to spatial census-based population microsimulation studies using R

  2013cites this paper
• Towards victim-oriented crime modelling in a social science e-infrastructure

  2011cites this paper
• Calibration of a spatial simulation model with volunteered geographical information

  2011cites this paper
• social simulation Elements of a computational infrastructure for

  2010cites this paper
• Enabling Quantitative Data Analysis Through e-Infrastructure

  2009cites this paper
• Author ' s personal copy A spatial microsimulation model with student agents

  2008cites this paper
• A spatial microsimulation model with student agents

  2008cites this paper
• Experience of e-Social Science: a case of Andy Turner and MoSeS

  2008cites this paper
• An Architecture for Social Simulation Models to Support Spatial Planning

  2007cites this paper
• Critical Accountability: Dilemmas for Interventionist Studies of e-Science

  2007cites this paper
• 2 . 1 Assumptions about Public Policy Scenarios

  year unknowncites this paper