A.J.Heppenstall@leeds.ac.uk

Slides available at:

https://urban-analytics.github.io/dust/presentations.html

Josie McCulloch, Patricia Ternes, Robert Clay,

Jiaqi Ge, Jonathan Ward, Minh Kieu

Why is ABM and calibration difficult?

Why should we bother?

Framework from UQ

Adding time element

Towards live simulations

Systems are driven by individuals

(cars, people, ants, trees, whatever)

Individual-level modelling

Rather than controlling from the top, try to represent the individuals

Autonomous, interacting agents

Represent individuals or groups

Situated in a virtual environment

Finding the optima not a problem, finding the global is…

Model discrepancy – error between optimised error and an observation

Uncertainties – between model and real system

Computational cost: potentially lots of parameters = lots of runs

**And added delights with ABM...**

Nature of the system

Variable dimensionality

Distance metric based on pattern measurement

Emulation of model structure with many different rules

Parameter uncertainty:

Which parameters? Which values? Equifinality.

Observation uncertainty

Imprecise, noisy data, natural variability

Model uncertainty / model discrepancy:

Model is a simplification (relies on assumptions and imperfections)

Ensemble variance:

Stochastic variance

History Matching (HM)

HM is a procedure used to reduce the size of the parameter space (Craig et al. 1997)

HM discards areas found to be implausible, leaving a (usually) much smaller non-implausible region of inputs

Approximate Bayesian Computation (ABC)

ABC estimates a posterior distribution that quantifies the probability of specific parameter values given the observed data

Here we are looking at two parameters:

Maximum possible metabolism of an agent

Maximum possible vision of an agent

Outcome measure is **size of population** that the model can sustain (66).

Framework followed

Define the parameter space to be explored

Quantify all uncertainties in the model and observation

Run HM on the parameter space

Run ABC, using the HM results as a prior

Sugarscape - small parameter space. Measure the implausiblity of each parameter pair (metabolism, vision)

10 waves performed – use plausible space from wave 1 as input to wave 2 and so forth

Results show that the parameters with the highest probability of matching the observation (i.e. sustaining a population of 66 agents) are where {metabolism, vision} are {4, 7}.

Microsimulation of disease spread

Includes shopping, schooling, working

Calibrated on hospital admissions data

ABC Parameter posteriors

ABC results uncertainty

*People* are the drivers of processes in cities

We need to understand mobility patterns:

Pollution – who is being exposed? Where are the hotspots?

Economy – can we attract more people to our city centres?

Disease - which times / places have large numbers of interactions

Lots of uncertainties

Inputs (measurement noise)

Parameter values

Model structure

Natural stochasticity

... and that's assuming that the system hasn't changed fundamentally

Used in meteorology and hydrology to update running models with new data.

Try to improve estimates of the true system state by combining:

Noisy, real-world observations

Model estimates of the system state

Simulating a city in real-time is too hard!! (for now). Let's start with a crowd

Can we reduce the uncertainty in an agent-based crowd simulation in real time?

Pedestrian trace data (Grand Central Terminal, New York City)

B. Zhou, X. Wang and X. Tang. (2012) Understanding Collective Crowd Behaviors: Learning a Mixture Model of Dynamic Pedestrian-Agents. In *Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)* 2012

More particles!

Will need high performance computing, but we have lots of that

Better PF algorithms (we used the most basic)

Alternative data assimilation methods

More advanced particle filtering is available

Unscented / Ensemble Kalman Filters

Unscented Kalman Filter

Some behaviours are more predictable than others

No obvious 'solution' to this

But shows how important data assimilation could be

Ensemble Kalman Filter

Ward, Jonathan A., Andrew J. Evans, and Nicolas S. Malleson. (2016) Dynamic Calibration of Agent-Based Models Using Data Assimilation. *Royal Society Open Science* 3(4). DOI: 10.1098/rsos.150703.

Unscented Kalman Filter

Clay, Robert, Le-Minh Kieu, Jonathan A. Ward, Alison Heppenstall, and Nick Malleson (2020) Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter’. In *Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection* 12092:68–79. *Lecture Notes in Computer Science*. DOI:10.1007/978-3-030-49778-1_6.

Quantum Field Theory - Creation and Annihilation Operators

Tang, Daniel. (2019) Data Assimilation in Agent-Based Models Using Creation and Annihilation Operators. *ArXiv:1910.09442 [Cs]*. arxiv.org/abs/1910.09442.

Data Bias

Need to be very careful: biased data -> biased models

The digital divide

Tracking People

Advantage with these methods is we *don't need to track people*

Models work with counts of flows

Although may need work on mapping from the data to the model domain (e.g. Lueck at al. (2019))

Potential for large-scale, agent-based models to transform policy making

Pollution, economy, disease spread, ...

But need to better understand the uncertainty in our models

What do we need?

Uncertainty quantification for ABMs

Efficient data assimilation methods for ABMs

High-resolution data (the identifiability problem)

Better models **?**

*Swarup, S., and H. S. Mortveit (2020) Live Simulations. In *Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems*, 1721–25.

A.J.Heppenstall@leeds.ac.uk

Slides available at:

https://urban-analytics.github.io/dust/presentations.html