Our approach

Human behaviour is complex and dynamic. Whilst more and more behavioural data is being collected, on its own, data is not enough. Even the largest behavioural data sets, such as loyalty card or credit card data, can only ever provide a partial view of population behaviour and they are limited to showing what happened in the past, not why it happened, nor what might happen in the future or how we might best affect it.

If you want to build a bridge then engineering and materials science will give you the answers you need, all of them definitive and founded in hard evidence. If you want to prevent your citizens from becoming obese, the answers will be spread across a dozen or more disciplines, and the evidence will be of variable quality.

For organisations who are serious about understanding human behaviour, agent-based modelling is the best way to extend and improve that understanding and provide a robust basis for developing and testing strategy.

What are they for?

Population simulations augment strategists’ and planners’ intelligence and allow them to think more deeply about the complex but critical questions of human behaviour:

Insight – why?

By examining and integrating all relevant, interdependent influences on a population into a coherent model, a much deeper level of causal insight is provided than is achievable by other methods

Forecasting – what if?

Being able to create worlds where anything can happen provides a powerful explorative tool for predicting both the range and likelihood of possible outcomes

Planning – how?

Exploring what might happen allows us to evaluate how different strategies might perform in a myriad of scenarios allowing you to identity the optimum approach, the likely outcome and the inherent risks.

What they are

Our simulations reflect the real populations we are seeking to understand. Each simulation has the following three components:

A population of agents: an agent is a representation of a real decision making unit within the real population. In most cases it is a person, but it could be a household or a company or other collective. Each agent will have a set of attributes such as age, gender and income, distributed to make the simulated population representative of the real population.

A set of rules of behaviour: each agent is provided with a set of rules that governs the choices it makes in the domain of interest, which could be where they shop, what brand of soft drink they buy, or whether they quit smoking or not. In our software we codify the rules that real people use to determine their behaviour. They are never wholly rational or irrational: our aim is to make them realistic.

An environment: the population exists within a virtual world that provides the set of external factors that influence behaviour. If the agent rules are based on the location of stores, the prevalence of brand advertising in media, or the weather, then our simulation will incorporate environmental components to reflect them. The environment also provides the means for agents to influence each other.

Benefits of our approach

Our models have a number of distinct features compared to other approaches.

Inclusive & iterative

Population simulations provide a single place in which all the existing qualitative and quantitative data about the population can be knitted together. The use of an explicit representation of an individual and their decision making process within the simulation makes it easier to fill the inevitable gaps in knowledge using expert opinion or informed assumptions. The models can be continually improved with new and updated data and insights so that the simulation always represents the best and latest understanding of population behaviour.

Forward looking

Whilst based on historical data population, simulations are explicitly forward looking through the focus on identifying the rules that govern individual behaviour. This means that our forecasting capability is not constrained by historical data. The simulated population will attempt to make sense of a new situation even if they have never seen it before whether that is a new product, the impact of a long-term change in attitudes, or a market shock like a product recall.

Micro, macro & meso

The simulations reconcile our understanding of what drives individual behaviour with the dynamics of the population as a whole, connecting the micro and macro through the social interactions of individuals – the meso. This allows us to disaggregate the origins of population behaviour and so identify the segments or decision points on which we should focus our efforts in order to change that behaviour.