Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter

Clay, R.D.; Ward, Jonathan A.; Ternes, Patrícia; Kieu, Le Minh; Malleson, Nicolas

doi:10.1016/j.simpat.2021.102386

Cited by 8 publications

(1 citation statement)

References 44 publications

(91 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This type of variable is extremely common in ABM. Therefore attempts are underway to include categorical variables through innovations such as the Reversible Jump, which may allow multiple filters to try to find the 'correct' categorical parameter values [67], or by resampling only some model variables [37]. As Section 10 will discuss, the ultimate aim of these efforts is to allow for real-time agent-based models of dynamic urban systems, drawing on diverse data sources and making accurate short-term predictions to aid policy making.…”

Section: Real-time Abmmentioning

confidence: 99%

Agent-based modelling for Urban Analytics: State of the art and challenges

Malleson

Birkin

Birks

et al. 2022

AIC

Self Cite

View full text Add to dashboard Cite

Agent-based modelling (ABM) is a facet of wider Multi-Agent Systems (MAS) research that explores the collective behaviour of individual ‘agents’, and the implications that their behaviour and interactions have for wider systemic behaviour. The method has been shown to hold considerable value in exploring and understanding human societies, but is still largely confined to use in academia. This is particularly evident in the field of Urban Analytics; one that is characterised by the use of new forms of data in combination with computational approaches to gain insight into urban processes. In Urban Analytics, ABM is gaining popularity as a valuable method for understanding the low-level interactions that ultimately drive cities, but as yet is rarely used by stakeholders (planners, governments, etc.) to address real policy problems. This paper presents the state-of-the-art in the application of ABM at the interface of MAS and Urban Analytics by a group of ABM researchers who are affiliated with the Urban Analytics programme of the Alan Turing Institute in London (UK). It addresses issues around modelling behaviour, the use of new forms of data, the calibration of models under high uncertainty, real-time modelling, the use of AI techniques, large-scale models, and the implications for modelling policy. The discussion also contextualises current research in wider debates around Data Science, Artificial Intelligence, and MAS more broadly.

show abstract

Section: Real-time Abmmentioning

confidence: 99%

Agent-based modelling for Urban Analytics: State of the art and challenges

Malleson

Birkin

Birks

et al. 2022

AIC

Self Cite

View full text Add to dashboard Cite

show abstract

On learning agent-based models from data

Monti¹,

Pangallo²,

Morales³

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems from micro-level assumptions. However, a significant drawback of ABMs is their inability to estimate agent-specific (or “micro”) variables, which hinders their ability to make accurate predictions using micro-level data. In this paper, we propose a protocol to learn the latent micro-variables of an ABM from data. We begin by translating an ABM into a probabilistic model characterized by a computationally tractable likelihood. Next, we use a gradient-based expectation maximization algorithm to maximize the likelihood of the latent variables. We showcase the efficacy of our protocol on an ABM of the housing market, where agents with different incomes bid higher prices to live in high-income neighborhoods. Our protocol produces accurate estimates of the latent variables while preserving the general behavior of the ABM. Moreover, our estimates substantially improve the out-of-sample forecasting capabilities of the ABM compared to simpler heuristics. Our protocol encourages modelers to articulate assumptions, consider the inferential process, and spot potential identification problems, thus making it a useful alternative to black-box data assimilation methods.

show abstract

Dynamic calibration with approximate Bayesian computation for a microsimulation of disease spread

Asher,

Lomax,

Morrissey

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The global COVID-19 pandemic brought considerable public and policy attention to the field of infectious disease modelling. A major hurdle that modellers must overcome, particularly when models are used to develop policy, is quantifying the uncertainty in a model’s predictions. By including the most recent available data in a model, the quality of its predictions can be improved and uncertainties reduced. This paper adapts an existing, large-scale, individual-based COVID-19 model to explore the benefits of updating the model in pseudo-real time. We use Approximate Bayesian Computation (ABC) to dynamically recalibrate the model’s parameter values as new data emerge. ABC offers advantages over alternative calibration methods by providing information about the uncertainty associated with particular parameter values and the resulting COVID-19 predictions through posterior distributions. Analysing such distributions is crucial in fully understanding a model and its outputs. We find that forecasts of future disease infection rates are improved substantially by incorporating up-to-date observations and that the uncertainty in forecasts drops considerably in later simulation windows (as the model is provided with additional data). This is an important outcome because the uncertainty in model predictions is often overlooked when models are used in policy.

show abstract

Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter

Cited by 8 publications

References 44 publications

Agent-based modelling for Urban Analytics: State of the art and challenges

Agent-based modelling for Urban Analytics: State of the art and challenges

On learning agent-based models from data

Dynamic calibration with approximate Bayesian computation for a microsimulation of disease spread

Contact Info

Product

Resources

About