Model design is one of the most important stages of the development of any agent-based model.  Get the design wrong and you might find yourself tied up in knots, battling against the structure of the project you defined months before.  Get it right and you’ll leave yourself a extensible platform to develop into, allowing yourself time to concentrate on refining your model structure.

During the development of an agent-based model during my PhD, I found that while many authors write about the application and implementation of ABM, each model is approached on a seemingly ad-hoc basis, with little reference to a unifying framework or starting point.  So I decided to develop my own.

The framework is divided into four sections, intended to be approached in the order in which they are presented.  At each section, further subsections demand questions of the modeller about how they might approach each facet of the design.  Only once the modeller has developed a strong understanding of each element (including deciding whether the factors are, in fact, relevant) of the design should they proceed with the development of the ABM.  The framework is intended to be generic, applicable across disciplines and language neutral (although elements naturally align with the structure of some of the ABM tools).

The framework is outlined below.  I’d be very interested to receive any feedback, positive or negative, or (even better) news of anyone applying this framework in the development of their agent-based model.

The Observer

The Observer refers everything that exists outside of the simulation environment. These are the constructs within which the simulation exists, including the theoretical constructs, in addition to the noted role of the modeller, and the environment within which the simulation will be developed. It is important that all aspects of design are considered prior to continuing with model development, and should ensure the modeller is taking the right steps in choosing to develop an ABM in lieu of other approaches.

In outlining the Observer, one should consider each of the following elements, answering each associated question, noting particularly where inherent uncertainty exists.

  • Mission Statement: What is the fundamental process one is seeking to model? Why is ABM being used to simulate this process? Do alternative models exist that ABM will demonstrate a significant advantage over?
  • Measuring Accuracy: How is the process under investigation ordinarily measured? Over what distribution and variance do these measurements lie? What are the accuracy of these existing measurements? In view of existing measures of the process, what outputs will be generated during the simulation process? How will the simulation outputs be related directly to real-world measures? What statistical tests will be carried out to measure model accuracy?
  • Software: Which software package will be used for the construction of the simulation? Can the model be developed using generic terms or is a modelling framework required? If so, which specific features are sought from a modelling framework? What are the modeller’s existing programming skill set? How feasible is an extension of this skill set within the time frame allowed for the development of this model?
  • Visualisation: Who is the audience for this model? Is the real-time visualisation of results important? What purpose does the visualisation hold? How much increased computation will the visualisation require (particularly where one is considering 3D animation)?
  • Bias: From what perspective is the model being developed? What is the background of the modeller? What are the natural assumptions being built into the model? How can the influence of modeller bias be removed from the approach, to the greatest extent possible?

The World

The World refers to the modelled environment within which the simulation will take place. The World does not only encompass the physical constraints of the model, but also the global rules that define the behaviour of all objects within it. This definition should only be made in relation to the specifications made during the Observer definitions, and not with respect to Agent design.

Once more, a number of design aspects must be considered at this stage, only within the context of those design decisions made during the Observer specification. Each design aspects again presents a number of questions that must be answered.

  • External Systems: Are there any interacting systems that interact with the process in question, but will not be modelled explicitly? What are the nature of these interactions? How will these external systems be encapsulated and represented within the model?
  • Space: Are spatial interactions important within the process being examined? If so, within what kind of space do these interactions occur – geographic, continuous, gridded, topological? Which data sets are required to aid in the definition of the space?
  • Time: Over what time period will the process be analysed? How long will one time step represent? Will a time step correspond to a real-world description of time?
  • Physical Rules: What physical rules shaping the actions of all within the World require explicit definition?

The Interactions

The specification of the Interactions does not refer to defining of agents themselves, but rather how Agent-to-Agent interaction occurs. These definitions refer to the physical and collective rule sets that organise interactions. It is the manifestation of these interactions within the simulation, that may form the representation of the process under examination, thus any definitions must be made within the contexts described during the Observer and World definitions.

The elements of Interactions design are as follows, with the relevant design questions outlined. References to higher level definitions are made where necessary consideration is required.

  • Physical Interactions: Do the agents physically interact across the space defined within the World? Under what circumstances do these interactions occur? What is the impact of these physical interactions? Is there a product of the interaction? How do such interactions impact upon the agents themselves or upon other external systems? Are there any specific social constraints governing these interactions? What are the temporal considerations of these interactions in relation to the time definitions made within the World? How are these interactions recorded?
  • Communication: Is there communication between agents? How do these communications occur? Through which medium? Are there any additional communications between agents and external systems? Are there any particular social rules governing communication? What are the temporal considerations of these interactions in relation to the time definitions made within the World? How are these interactions logged?
  • Resource Exchange: Do agents exchange resources in any way? How do these interaction occur? What is lost and what is gained by through each interaction? How do these interactions manifest themselves over space and time? How are these interactions recorded?

The Agent

The design of the Agent is all about capturing the characteristics, actions, and decisions that influence their interactions with other agents and the wider environment. It is ultimately these behaviours that shape how the overall process is modelled, and how it evolves over space and time. It is therefore vital that, only once each level within the design hierarchy is completely specified – once all higher level entities have been fully understood, broken down and incorporated within the model – should the modeller start explicitly considering the Agent design process. For only within the context of the prior specifications, outlining completely the environment and conditions in which the Agent exists, can an Agent be fully described.

Once one has defined this environment, the definition of the Interactions involved in the simulation should proceed naturally. During this final process, the following design considerations should be examined in detail.

  • Characteristics: What are the characteristics of the agents? Can agents be assigned to different profiles? What are the core traits that are required for specification of an agent’s actions? How do these properties vary across the population of agents?
  • Decisions: What decisions are made by the agent (or type of agent) during the course of the simulation? What information sources are used during the formation of this decision? Through which type of mechanism are these decisions formed? How long do decisions take to make? Are decisions formed in consultation with other agents over a framework of communication?
  • Actions: What actions does an agent, or type of agent, conduct during the simulation? How are these actions influenced by the agent’s characteristics? Are these actions directly relevant in simulating the process one wishes to examine? How are these manifested across the simulation space? How often do these actions take place relative to the temporal evolution of the simulation? Under what wider constraints (e.g. physical, moral framework) are these actions shaped?
London 2012: Using Fear to Tame Transportation Demand

One of the biggest advantages, I feel, about studying urban transport phenomena in London is the simple ability to be able look out of the window and see what is actually going on.  This week, the Olympics and its (supposed) transportation chaos, came to London.

What has struck me early on, mainly since the introduction of the Games Lanes last week, is a big reduction in the number of vehicles on the road.  There have been reports of certain inevitable problems in various parts of the capital, but my experience has been a general reduction in demand on most roads (see a couple of photos I took below).  This sentiment has been shared by a number of my colleagues.  There has been no word yet from Transport for London as to whether the data is backing this up.

London 2012: Using Fear to Tame Transportation Demand

Second, the big public transport problems predicted at certain stations and at certain times, have no yet come to fruition.  Warnings were issued widely this morning about potential overcrowding at a number of stations, yet early reports suggest that this is far from the reality – the Guardian highlight a number of citizen reports of empty Tube seats and quiet stations this morning.

London 2012: Using Fear to Tame Transportation Demand

Typical fear-inducing GetAheadOfTheGames literature (copyright Transport for London 2012)

It appears that the strategy has worked.  In fact, one might even suggest that it has worked better than expected.  I would say that this is partly down to the impact of irrationality, specifically the impact of fear.  Individuals, scared of potentially having to wait considerable amounts of time at stations only to cram into packed Tube trains, or fearful of long queues on the roads, have changed their habitual plans en masse.

Social Phenomena

The effect has gone to demonstrate, at least to me, the impact that small changes in the behaviour of many individuals can have on the nature of the city.  As individuals, we make a choice, we carry out that action, and we are mostly unaware of the impact that decision has on shaping broader phenomena.  Yet, in observing the patterns these many individuals make, we can begin to see how individual and social attitudes impact on shaping transportation flows.

This relationship, specifically the impact that fear has had in the context of the Olympics, appears to have caught some analysts on the hop.  INRIX, a big transport data provider, predicted earlier in the year the ‘perfect traffic storm‘ in traffic demand during the first few days of the Games (reported in more detail here).  This patently failed to happen.  The models INRIX employed in making these predictions clearly failed to make consideration for the impact that fear would play in reducing traffic demand.  This approach is far from uncommon where transport demand modelling is concerned.

The Games have a long way to run yet, and we may well see a counter movement occur in time as people begin to realise that transportation isn’t as bad as first expected.  But I think the impact that fear has held on shaping, at least, the first few days of transportation flows makes for interesting viewing.

Amanda Erickson put up a nice, simply visualisation of what life might be like in a future of driverless, automated cars. Check it out.

Two things sprang to mind while watching this – first, how terrifying this might be for a passenger in one of these cars, and second, haven’t I seen this sort of thing somewhere else before?

Well, yes, I showed the following video in a lecture last month as demonstration of self-organisation.  To me, the patterns look similar – at the higher level you see chaos, but when you observe the actions of individual’s there is usually a rational stream of thought behind the actions they are taking – normally to get to their exit road.  Judge for yourself.

I think the stark similarity seen between these two videos raise interesting questions about what we consider as progress in the urban realm.  Bare with me as I attempt to explain.

The driverless or automated car is often seen as the natural future of private transportation*, with one of its main benefits being the apparent offer of optimal organisation of traffic flows (e.g. no congestion).  And indeed when look at the first video, everything works and works well, perhaps even optimally.  But then you look at the second video, and you essentially have the same thing, created solely through the activity of individuals.

It is strange therefore that a fully optimised technical system is generally deemed necessary and superior.  When people are left to their own devices, to ‘sort it out between them’, people invariably do.  Traffic in Hanoi is not just the only example of this type of self-organisation – the Internet itself is a creation of human ingenuity.  Following Monderman’s ideas on Shared Space, perhaps all of these traffic regulations, signage and restrictions actually reduce our need to think about what we are doing.  They reduce and remove our ability or will to self-organise, and to the deficit of us all.

So why don’t ‘natural’ answers to technical problems receive a better press?  I suspect it is an issue of trust in the citizen.  That threat that one person may mess up, and mess it up for the rest of us.  Instead of facing the risk and accepting it as part of the solution, we surround ourselves with unnecessary and invasive mechanisms that carry out the task for us.  They may cost a lot of money and not be any better than our current solution, but they feel like progress.  It feels like things are getting better.  So, yes, perhaps automated cars are indeed a thing of the future.

As ever, very interested to hear your thoughts on this.

* I’ve personally never been so sure – mainly because of the safety element, and that fact that many people actually enjoy the process of driving…