Using Hybrid Agent-Based Systems to Model Spatially-Influenced Retail Markets

One emerging area of agent-based modelling is retail markets; however, there are problems with modelling such systems. The vast size of such markets makes individual-level modelling, for example of customers, difficult and this is particularly true where the markets are spatially complex. There is an emerging recognition that the power of agent-based systems is enhanced when integrated with other AI-based and conventional approaches. The resulting hybrid models are powerful tools that combine the flexibility of the agent-based methodology with the strengths of more traditional modelling. Such combinations allow us to consider agent-based modelling of such large-scale and complex retail markets. In particular, this paper examines the application of a hybrid agent-based model to a retail petrol market. An agent model was constructed and experiments were conducted to determine whether the trends and patterns of the retail petrol market could be replicated. Consumer behaviour was incorporated by the inclusion of a spatial interaction (SI) model and a network component. The model is shown to reproduce the spatial patterns seen in the real market, as well as well known behaviours of the market such as the "rocket and feathers" effect. In addition the model was successful at predicting the long term profitability of individual retailers. The results show that agent-based modelling has the ability to improve on existing approaches to modelling retail markets.Agents, Spatial Interaction Model, Retail Markets, Networks

Investigating the Behaviour of Twitter Users to Construct an Individual-Level Model of Metropolitan Dynamics.

In this paper, consideration is given to the use of new forms of social network data as a means to enrich our understanding of complex structures and activity patterns in urban areas. Specifically, a sample of Twitter messages (‘tweets’) in the city of Leeds is assembled from publicly available sources, and spatial and temporal patterns in these data are demonstrated, with special reference to the geodemographic profiles of service users. It is argued that classical space-time models of individual behaviour provide one possible framework for the interpretation of patterns, and the process of attempting to classify activities is begun with reference to the geographical distribution, timing and, importantly, the content of messages. Some initial analysis is undertaken to examine emerging networks of interconnection between users and individual users’ spatio-temporal behaviour. In the discussion, it is suggested that the integration of this form of social data analysis with existing microscale representations and multi-agent models of city structure and dynamics will provide fertile ground for future research

Microscopic simulations of complex metropolitan dynamics

In this paper we will introduce an individual-based model of a British city. The approach draws its inspiration from both microsimulation and agent-based modelling. Microsimulation is used to reconstruct the entire population of a city region at both the household and individual scale. We illustrate this for Leeds, a city with three-quarters of a million inhabitants grouped into more than 300 thousand households. The resulting population is profiled by demographic and social attributes which are richly specified. In order to incorporate dynamic individual behaviour, we argue that agent-based simulations are more appropriate, and rules will be presented which allow the identi�cation of �ve essential behaviours which we term domestic living, education, work, recreation and shopping. Through this modelling process we seek not just to understand residential patterns within the city, but the dynamic ebb and flow of the population in everyday metropolitan life. The novel feature of our research is that we will use up-to-date social network data to calibrate our agent behaviours. Although social network data are likely to be somewhat skewed and unreliable, they are abundant and continually refreshed and also provide temporally-accurate daily behavioural information that are often absent from traditional sources (such as censuses). We will make an attempt to evaluate the robustness and (potential) value of this approach

Simple, multiple and multiway correspondence analysis applied to spatial census-based population microsimulation studies using R

As a bivariate and multivariate multidimensional exploratory method, simple and multiple correspondence analyses have been used successfully in social science for survey or questionnaire results descriptions. Nonetheless, the complexity of social interactions including health status indicators, with also the need to take into account the spatial and temporal realm of the survey, may incline to look at variable associations in a multiway approach instead of a two-way matrix analysis. This means for example, that interaction of order three between the spatial configuration (say the Output Areas of an urban zone), the set of categorical variables (say selected from a census survey) and the evolution (say every 5 years over a 30 years period) would be considered in order to differentiate spatio-temporal associations across categorical variables. For census-based spatial simulation models such as microsimulations, exhibiting this kind of properties is useful as forecasts moves of population characteristics to be considered for healthcare policy scenario analysis. In this paper it is shown how to run this type of analysis within R using a package dedicated to multiway analysis (the R package PTAk), that is, working on multi-entry array data using an algorithm extending classical multidimensional analysis. A didactic approach from two-way analyses to multiway ones, of the same dataset generated from a population spatial simulation model allows a critical demonstration of the potential of the different t methods. Particular attention is also given to the different choices of spatial units and the scale variation effect within a nested administrative zoning system that can be analysed by a correspondence analysis with respect to a model (extending the approach using the independence model) and which can be done for a simple, multiple of multiway correspondence analysis

Moses: Planning for the Next Generation

The study of population changes has always been at the centre of public policy and planning. People’s movements, interactions and behaviors will inevitably have an important impact on the society and environment that they are living in. At the same time, such changes will also lead to an evolution of the population itself over time. Advances in technologies and new tools often bring new visions to such studies. To facilitate strategic decision making and to plan developments for a more sustainable future, it is vital to study and understand the changes in our population. This paper introduces Moses, an individual based model that simulates the UK population through discrete demographic processes at a fine spatial scale for 30 years from 2001 to 2031. The modeling method is grounded in a dynamic spatial MicroSimulation Model (MSM), but also introduced Agent Based Model (ABM) insights to strengthen the modeling of movements, interactions and behaviors of distinctively different sub-populations. The MSM can not only produce projections of baseline population with rich information on individuals to facilitate various studies, it can be also useful in providing an assessment of multiple scenarios for different planning applications. In this paper, we will demonstrate three spatial planning applications in the areas of residential land use planning, public health planning and public transport planning. Whilst the demonstrations are deliberately made simple, the contribution of intelligent agents in the modeling of interaction, behavior and the impact of personal histories on demographic changes is clearly shown. Within this framework, it enables researchers to effectively model the heterogeneous decision making units on a large scale, as well as provide the flexibility to introduce different modeling techniques to strengthen various aspects of the model

Scoping and business models report

The aim of this work was to scope the community for needs, raise awareness and encourage adoption of new social simulation models and tools developed in the National e-Infrastructure for Social Simulation (NeISS) and gather findings, establish contacts, and build capacity for future activities

Synthetic Population Catalyst : a micro-simulated population of England with circadian activities

Funding: This work was supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/W006022/1, particularly the “Ecosystem of Digital Twin” and “Shocks and Resilience” themes within that grant & The Alan Turing Institute.The Synthetic Population Catalyst (SPC) is an open-source tool for the simulation of populations. Building on previous efforts, synthetic populations can be created for any area in England, from a small geographical unit to the entire country, and linked to geolocalised daily activities. In contrast to most transport models, the output is focussed on the population itself and the way people socially interact together, rather than on a precise modelling of the volume of transport trips from one area to another. SPC is therefore particularly well suited, for example, to study the spread of a pandemic within a population. Other applications include identifying segregation patterns and potential causes of inequality of opportunity amongst individuals. It is fast, thanks to its Rust codebase. The outputs for each lieutenancy area in England are directly available without having to run the code.PostprintPeer reviewe

Evidence from big data in obesity research: international case studies

Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of 'big data' presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). 'Additional computing power' introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered

Developing an e-infrastructure for social science

We outline the aims and progress to date of the National Centre for e-Social Science e-Infrastructure project. We examine the challenges faced by the project, namely in ensuring outputs are appropriate to social scientists, managing the transition from research projects to service and embedding software and data within a wider infrastructural framework. We also provide pointers to related work where issues which have ramifications for this and similar initiatives are being addressed