Supporting JRF through Agent Based Modelling David Buxton, decisionLab

decisionLab Making decisions easier Privately owned analytics consultancy based in South London 10 permanent staff Model Building specialists Experienced Peer Reviewers Agile development methods, openness and collaboration Simulation, Optimisation, Dependency Modelling, Econometrics, Statistical Research

Our experience Successfully solved real-world problems If I increase the price of product X, what will happen to sales (switching)? How can I ensure my investment planning takes account of asset criticality as well as asset health? How can I reduce trips to the supermarket? If I have a limited budget how can I balance the expenditure of interventions across multiple asset types to minimise overall risk? What’s the best way to train a fighter pilot? What’s the quickest way to evacuate a casualty from a battlefield? I know I need more water supply, but what’s the least cost way I can match the supply with the demand for water? If I train everyone remotely, will they still be as effective? And will it save me money? Why is helicopter availability so low?

Business Simulation In a nutshell.. Building a computer model which mimics a real or proposed system in order to understand more about performance or to learn what will happen if something within the system changes. The system can be a helicopter, a person, a hospital, a factory, a supply chain, a training system,.. Benefits Gives a ‘helicopter’ view All stakeholders can engage with to understand the root cause of any problems Visually explains the complexity of interactions and feedbacks Rapid prototyping Models are quick to build meaning that insight can be gained very quickly More potential designs & solutions can be tried out than using pilot studies Risk Management It much more cost effective to understand how your system may perform in unusual situations than in the real world! By testing & learning through using the simulation, more robust, more optimal solutions can be identified Understand complexity Initial models can easily be extended to incorporate complexity meaning that systems can be thoroughly examined and robustly tested

Agent Based Modelling Understanding causal relationship and factors Human systems are emergent “How and Why decisions are made” May include a behavioural economics perspective Simple decision rules which are close to reality Understand why certain strategies / intervention work Not an econometric approach Constructivist approach

The agents decision is to switch (or not switch) bank accounts This is an informed decision, and the outcome of a learning journey Do I know about the possibility of switching? What alternative products are available? What push factors are affecting me? Poor bank service What pull factors are affecting me? Innovations from other banks Currently very much WiP Objective is to run a number of scenarios to establish the most effective intervention Could BACS encourage or banking innovation? What about marketing? Would a TV campaign highlighting the CASS service work? Project example: How can rates of Account Switching be improved?

Funds research into root causes of societal problems and how they can be resolved Not just poverty! Current research themes are – Poverty: examining the root causes of poverty, inequality and disadvantage and identifying solutions Place: contributing to the building and development of strong, sustainable and inclusive communities Ageing Society: how people and communities can have control of their own lives. Politically independent JRF: Background Charitable research organisation

Pro-bone project objective: Advising on the appropriate Agent Based Modelling approach Peer review of competitive tenders JRF already involved in funding research to develop anti-poverty strategies for the UK High emphasis that strategies should be supported by Modelling to enable JRF to challenge current policy with more confidence that currently possible Previous tried and failed with System Dynamics Aware of Agent Based Modelling, had launched a competition and received three very different responses But low detailed knowledge of Agent Based Modelling within the JRF team So how to compare? Which one is best at meeting the objectives of JRF

Pro-bone project results: Advising on the appropriate Agent Based Modelling approach Ran a series of half day workshops to increase awareness of ABM and its various forms System Dynamics Vs Agent Based Modelling Micro Simulation Vs Behavioural Modelling Reviewed the three tenders and gave formal feedback and participated in workshops to discuss merits of each Advised to take develop the model current in use by UK government for long term economic forecasting Better to be challenging / developing policy with a model which has credibility?!!

Follow on consultancy project To understand the ‘bigger’ picture using Dependency modelling Dependency Modelling A modelling approach based on Bayesian probability to capture the causal relationship between each of the variables Nutrition OK Sanitation OK Home OK

The process Capture all the influences, quantify the relationships, model the risk Workshop 1: JRF core team (4 people): Frame the problem and an initial brain storming of causal factors Workshop 2: Extended group of stakeholders (circa 30 people): Validate the initial map, and collect a broad Desk research: Using previous literature to establish a core model Workshop 3: JRF core team Validate the enlarged map and ensure it represents a complete picture without bias Workshop 4: JRF core team Quantification of the map to show the direction and strength of the relationships Risk model building: Risk solutions Building the risk model Workshop 5: JRF core team Final presentation

The Risk Model What are the top influencing factors in the risk that a current child we be in Poverty by the age of 25?

How is it being used in JRF? A policy assessment tool SMEs map policies onto how they will impact the ‘influence factors’ Recalculate the risk Does it increase or decrease the Give the ability to compare between policies – albeit, at the macro level Policy: Freeze teacher pay Acts to reduce the probability of ‘Good’ teaching by 2%

Questions, comments, discussion David Buxton, decisionLab