"A 30min Introduction to Agent-Based Modelling" for GORS

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Introduction to Agent-Based Modelling by Bruce Edmonds …

Introduction to Agent-Based Modelling by Bruce Edmonds
Centre for Policy Modelling, Manchester Metropolitan University
For a workshop at the "Government Operational Research Service"

Many aspects of modern society are highly complex, in the sense that they are not easy to understand without taking into account the detailed interactions between the social actors that comprise it. For such cases mathematical and system dynamics models are insufficient to be useful for understanding what is going on or forecasting what might occur. Statistical models are useful when the detail of the interactions can be treated as noise, and such models can make useful projections into the future, but it is not clear when the assumptions behind such projections will fail (and hence the projections being wrong - qualitatively as well as the error rate).

Agent-based modelling (ABM) is a relatively new technique that has the potential to play a part in this. In this technique social actors (people, departments, firms, households etc.) are individually represented as separate entities within the simulation and the interactions between the actors are represented as separate interactions within the simulation. The entities in the simulation that correspond to the social actors are called "agents". Sets of "rules" determine the micro-level behaviour of each agent. Each agent may have different characteristics and, indeed, different rules. One then "runs" the simulation where (effectively in parallel) all the agents obey their rules and a complex web of interactions between them result. This "mess" of detail can then be abstracted/graphed/measured in various ways (very similar to the techniques we use to understand what is happening in society itself) to give us macro-level statistics and visualisations which can be compared with existing aggregate statistics.

Two examples of such ABM are presented: (1) a very simple one that illustrates how the detailed interactions of individuals can affect the global outcomes, and (2) a more complex descriptive model that illustrates some of the social complexity that such models can represent.

Unsurprisingly the increased expressive power of ABM comes with downsides, including: (a) such models require a lot of data in order to be able to validate them and (b) the models are so complex that it can be difficult to understand the model itself. As a result of these difficulties, ABMs should not be considered to give probabilistic forecasts, but rather possibilistic - that is, they can produce some of the possibilities that are inherent in the system, but not (reliably) the probabilities of each (nor indeed will they be able to produce all of the real possibilities). In the context of policy making this is particularly relevant to risk-analyses of policies, where one wants to know some of the possible ways a policy might go wrong. This will allow one to design and implement "early warning" systems ...

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  • 1. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 1 An Introduction to Agent-Based Modelling Bruce Edmonds Centre for Policy Modelling Manchester Metropolitan University
  • 2. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 2 Society is Complex! • This may not be a surprise to many of you.. • …but in the sense of complexity science it means that significant global outcomes can be caused by the interactions of networks of individuals • In other words, the outcomes are not modellable if you do not model interactions between individuals or model only the interaction of global variables… • …and if you try to model it in these ways, you will often be caught out be surprises • Agent-based simulation allows the exploration of such surprises but it is still a maturing field
  • 3. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 3Social influence and the domestic demand for water, Aberdeen 2002, http://cfpm.org/~bruce slide-3 Equation-based or statistical modelling Real World Equation-based Model Actual Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes
  • 4. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 4 Individual- or Agent-based simulation Real World Individual-based Model Actual Outcomes Model Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes Agent-
  • 5. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 5 What happens in ABSS • Entities in simulation are decided on • Behavioural Rules for each agent specified (e.g. sets of rules like: if this has happened then do this) • Repeatedly evaluated in parallel to see what happens • Outcomes are inspected, graphed, pictured, measured and interpreted in different ways Simulation Representations of OutcomesSpecification (incl. rules)
  • 6. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 6 Example 1: Schelling’s Segregation Model Schelling, Thomas C. 1971. Dynamic Models of Segregation. Journal of Mathematical Sociology 1:143-186. Rule: each iteration, each dot looks at its 8 neighbours and if less than 30% are the same colour as itself, it moves to a random empty square Segregation can result from wanting only a few neighbours of a like colour
  • 7. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 7 Characteristics of agent-based modelling • Computational description of process • Not usually analytically tractable • More specific… • … but assumptions are less ‘brave’ • Detail of unfolding processes accessible – more criticisable (including by non-experts) – but can be more convincing that is warrented • Used to explore inherent possibilities • Validatable by a variety of data kinds… – but needs LOTS of data to do this • Often very complex themselves
  • 8. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 8 Micro-Macro Relationships Micro/ Individual data Qualitative, behavioural, social psychological data Theory, narrative accounts Social, economic surveys; Census Macro/ Social data Simulation
  • 9. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 9 Choosing Simulation Techniques • Every simulation technique has pros and cons • The hardest decision is when to use which approach (or how to combine approaches) • Analytic approaches rely on their formulation being simple enough to be solvable (or, in practice, they use simulation anyway) • Statistical approaches rely (in different and subtle ways) on the representation of noise as random – they will miss surprises in their projections • Agent-based approaches are complex, require lots of data and do not give probability forecasts • Simplicity is no guarantee of truth or generality
  • 10. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 10 Meaning from intermediate abstraction (often implicit) target system mental model formal model (Meaning)
  • 11. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 11 In Vitro vs In Vivo • In biology there is a well established distinction between what happens in the test tube (in vitro) and what happens in the cell (in vivo) • In vitro is an artificially constrained situation where some of the complex interactions can be worked out… • ..but that does not mean that what happens in vitro will occur in vivo, since processes not present in vitro can overwhelm or simply change those worked out in vitro • One can (weakly) detect clues to what factors might be influencing others in vivo but the processes are too complex to be distinguished without in vitro experiments
  • 12. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 12 Some modelling trade-offs simplicity generality Lack of error (accuracy of results) realism (design reflects observations)
  • 13. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 13 Example 2: A model of social influence and water demand • Part of a 2004 study for EA/DEFRA, lead by the Stockholm Environment Institute (Oxford branch) • Investigate the possible impact of social influence between households on patterns of water consumption • Design and detailed behaviour from simulation validated against expert and stakeholder opinion at each stage • Some of the inputs are real data • Characteristics of resulting aggregate time series validated against similar real data
  • 14. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 14 Simulation structure •Activity •Frequency •Volume Households Policy Agent •Temperature •Rainfall •Daylight Ground Aggregate Demand •Activity •Frequency •Volume Households Policy Agent •Temperature •Rainfall • Ground Aggregate Demand
  • 15. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 15 Some of the household influence structure
  • 16. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 16 Example results: relative aggregate domestic demand for water (1973 = 100)Aggregate dem and series scaled so 1973=100 0 20 40 60 80 100 120 140 160 180 200 J- 73 J- 74 J- 75 J- 76 J- 77 J- 78 J- 79 J- 80 J- 81 J- 82 J- 83 J- 84 J- 85 J- 86 J- 87 J- 88 J- 89 J- 90 J- 91 J- 92 J- 93 J- 94 J- 95 J- 96 J- 97 Sim ulation Date RelativeDemand
  • 17. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 17 Conclusions from Example 2 • The use of a concrete descriptive simulation model allowed the detailed criticism and, hence, improvement of the model • The inclusion of social influence resulted in aggregate water demand patterns with many of the characteristics of observed demand patterns • The model established how: – processes of mutual social influence could result in differing patterns of consumption that were self- reinforcing – shocks can shift these patterns, but not always in the obvious directions – the importance of introduction of new technologies
  • 18. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 18 Example 3: Developing Work • Institute for Social Change & Theoretical Physics Group, University of Manchester • Centre for Policy Modelling, Manchester Metropolitan University
  • 19. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 19 the SCID Modelling Approach Data-Integration Simulation Model Micro-Evidence Macro-Data Abstract Simulation Model 1 Abstract Simulation Model 2 SNA Model Analytic Model
  • 20. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 20 Overall Structure of Model Underlying data about population composition Demographics of people in households Social network formation and maintenance (homophily) Influence via social networks • Political discussions Voting Behaviour Input Output
  • 21. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 21 Discuss-politics-with person-23 blue expert=false neighbour-network year=10 month=3 Lots-family-discussions year=10 month=2 Etc. Memory Level-of-Political-Interest Age Ethnicity Class Activities AHousehold An Agent’s Memory of Events Etc. Changing personal networks over which social influence occurs Composed of households of individuals initialised from detailed survey data Each agent has a rich variety of individual (heterogeneous) characteristics Including a (fallible) memory of events and influences
  • 22. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 22 Example Output: why do people vote (if they do) Intervention: voter mobilisation Effect: on civic duty norms Effect: on habit- based behaviour
  • 23. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 23 Simulated Social Network at 1950 Established immigrants: Irish, WWII Polish etc. Majority: longstanding ethnicities Newer immigrants
  • 24. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 24 Simulated Social Network at 2010
  • 25. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 25 Possibilistic vs Probibilistic • The idea is to map out some of the possible social processes that may happen • Including ones one would not have thought of or ones that have already happened • The global coupling of context-dependent behaviours in society make projecting probabilities problematic • Increases understanding of why processes (such as the spread of a new racket) might happen and the conditions that foster them • Complementary to statistical models
  • 26. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 26 Role of ABM in Policy Assessment • ABMs are good for analysing risk – how a more standard model/prediction might go/be wrong • That is, testing the assumptions behind simpler models (statistical, discrete event, system dynamic, etc.)… • …so exploring the possible deviations from their forecasts • In other words, showing some of the possible surprises that could occur (but not all of them) • To inform a risk analysis that goes with a forecast • Can be used for designing early-warning indicators of newly emergent trends
  • 27. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 27 ABSS Advantges • ABSS allows the production and examination of possible complex outcomes that might emerge • It does not need such strong assumptions (that analytic approaches require) to obtain results • It allows the indefinite experimentation and examination of outcomes (in vitro) • It aids the integration and use of a wider set of evidence, e.g. very open to stakeholder critique • It suggests hypotheses about the complex interactions in observed (in vivo) social phenomena • So allowing those ‘driving’ policy to be prepared, e.g. by implementing ‘early warning systems’ • Can be complementary to other techniques
  • 28. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 28 ABSS Disadvantages • It does not magically tell you what will happen • Are relatively time-consuming to construct • It can look more convincing that is warranted • Understanding of the model itself is weaker • It needs truck loads of data for its validation • It gives possibilities rather than probabilities • Fewer good practitioners around • Not such a mature field
  • 29. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 29 To Learn More • Simulation for the Social Scientist, 2nd Edition. Nigel Gilbert and Klaus Troitzsch (2005) Open University Press. http://cress.soc.surrey.ac.uk/s4ss/ • Simulating Social Comlexity – a handbook. Edmonds & Meyer (eds.) (2013), Springer. • Journal of Artificial Societies and Social Simulation, http://jasss.soc.surrey.ac.uk • European Social Simulation Association, http://essa.eu.org • NetLogo, a relatively accessible system for doing ABM http://ccl.northwestern.edu/netlogo • OpenABM.org, an open archive of ABMs, including code and documentation
  • 30. An Introduction to Agent-Based Modelling, GORS, London, 24th Sept. 2013. slide 30 Thanks! Bruce Edmonds http://bruce.edmonds.name Centre for Policy Modelling http://cfpm.org I will make these slides available at: http://www.slideshare.net/BruceEdmonds