DEPARTMENT OF SOCIOLOGY

1. DEPARTMENT OF SOCIOLOGY Agent-Based Modelling: Social Science Meets Computer Science? Edmund Chattoe-Brown <ecb18@le.ac.uk> http://www2.le.ac.uk/departments/sociology/people/echattoebrown

2. 1. Plan Introducing ABM: The Schelling model. “Introducing” social science: Equations, narratives and programmes. The challenge of interdisciplinarity. Possible connections between SS and CS. Now read on.

3. 2. The Schelling model I am not presenting this to you because I believe it is “true” or that the assumptions are realistic. I am presenting it because it is easy to explain and illustrates key points. If I was going to teach you guitar I would start with strummed “three finger” chords not finger picking and barre chords. Please don’t treat what follows as research! But it more than “mere” teaching.

4. 3. The Schelling assumptions “Agents” live on a square grid (chessboard) so each has a maximum of 8 immediate neighbours. There are two “types” of agents (pink and white) and some grid spaces are vacant. Initially agents/vacancies are distributed randomly. All agents decide what to do in the same very simple way. (This assumption and others can easily be relaxed. It is not an “analytical requirement”.) Each agent has the same preferred proportion (PP) of neighbours of its own kind (PP = 0.5 means you want at least half your neighbours to be your type - but you would accept all of them i. e. PP is minimum.) Vacant grid spaces “don’t count” which is why the PP is a fraction not a number. If an agent is in a position that satisfies it (in terms of PP and neighbourhood composition) then it does nothing otherwise it moves to a vacancy chosen “at random”.

5. 4. Initialisation NetLogo: Free, cross platform. This is from the Models Library with just the colours changed. Search for <netlogo homepage> on google. [Note: Crossed boxes are “not satisfied”.]

6. 5. “Running the process” PP=0.65. Density (proportions of agents versus empty cells) = 0.9. Clusters are obvious. “Buffer zones” maybe less so. This is what those individual level behaviours “add up to”.

7. 6. Thought experiments What happens when PP=0.3? What happens when PP=1?

8. 7. No “majority preference” “Different” clustering (no buffer zones, more “ragged” and “straggly” clusters) but clearly still clusters.

9. 8. Blooming buzzing confusion No clustering despite extreme “xenophobia”: What is going on here?

10. 9. Computational experiments If a social system this basic/homogeneous is non-linear then we really need a theorising tool that can address complexity.

11. 10. Introducing social science 1 • What have we got? (To a good first approximation.) • Statistics (equations): Link individual attributes (which may include “location” or “neighbourhood composition”) via surveys. “Those with more education are happier living in ethnically mixed neighbourhoods”. • Ethnography (narratives): Observe an area in detail and get rich detail about individuals and their interaction. “He gets harassed a whole lot because they call him a honkey lover”.

12. 11. Introducing social science 2 • We now have reason to suspect that clusters do not imply strong “similarity preference” and vice versa. • Each existing method accesses one level quite directly and “infers” the other. Uh-oh? • The so called micro-macro problem (other names too).

13. 12. Schelling with heterogeneous PP This time boxes marked x have PP of 0.75 or greater. (Density: 95%, uniform distribution of PP: 0.00-0.99).

14. 13. Coda How would you pick a site to do ethnography or qualitative interviews? What could you safely conclude from what you discovered? (Compare a “squabble zone” with a “stable neighbourhood” even if it includes boundaries.)

15. 14. Unfortunately... We aren’t very interested in “efficiency” (because this isn’t really our bottleneck in practice). We aren’t very interested in “formality” for its own sake (because we tried that already and the social world won’t wear it).

16. 15. A useful distinction? Descriptive: We are interested in a system for its role in “explaining” real data (though “explain” is not trivial). A good model is one that does this. The social world defines the quality of our model. Instrumental: We are interested in a system to do a practical task. A good model is one that does this faster, more reliably, with less CPU cycles. Human goals define the quality of our model.

17. 16. The challenge How can instrumental methods help with description? The “pure mathematics” view: Pure mathematics is developed in the abstract and sometimes (quaternions) turns out to match aspects of physical reality. (But how often?) Computer programs are not abstract systems and are probably too complicated to represent social reality “by chance”. In any event, if we have information about “how things are” in the social world then why not use it? The “piles of models” problem.

18. 17. ABM methodology This is starting to diffuse as a “technical gizmo”. IMO it is a research method and, without methodology, the results are various sorts of junk. FUQ (Frequently Unanswered Question): What is the point of your ABM and how can its point be evaluated scientifically? Schelling: If I were you, I wouldn’t start from there at all. Calibration: How do individuals make moving decisions? Validation: How “alike” are clusters? What does alike mean? [Verification: Does the program work properly?] Combining data types.

19. 18. The Gilbert and Troitzsch box

20. 19. It works: Hägerstrand (1965!)

21. 20. Strict churches Data is good for you!

22. 21. Opinion dynamics A very odd business! I say again: Data is good for you!

23. 22. Projects 1: Replication A lot of old but important models are effectively lost because they no longer run and the technology has moved on: For example, H (until very recently). Re-implementing a program from the published description and checking/exploring the results is good science. (It also allows for potential efficiency gains in a meaningful context: Hummon.)

24. 23. Projects 2: Synthesis “Piles of models” leads to lots of models with two or three “not implausible” features (networks, learning, type recognition) that don’t cohere. Useful to have well designed “test beds” that allow existing dimensions to be synthesised in well known research areas (like the Repeated Prisoner’s Dilemma or opinion dynamics).

25. 24. Projects 3: Library development There are whole classes of models which, sometimes surprisingly, aren’t simply (and effectively) implemented for “community use”. Coverage in NL Models Library is quite odd: No organisations. For example, there is a lot of discussion of “rule based” agents (production systems?) but no “go to” example in the NetLogo Models Library. My DPhil was based on Genetic Programming representing firms interacting via price in a market. It would make a very good Models Library contribution allowing “reuse” of GP as well.

26. 25. Projects 4: Making efficiency matter Many ABM are “implicitly” limited by processing power. Although the cognitive capacities of agents do not need to be limited, they often are in practice. Where would “big models” really add value? Speed is definitely a bottleneck in exploring large parameter spaces (sensitivity analysis).

27. 26. Projects 5: Proving formality matters Some researchers have claimed that ABM should follow better computer science procedure. However, this really needs a demonstration not an assertion. What is “wrong” with existing programming languages (the floating point issue) and what actually goes wrong with “hacked” code? (Possible link to replication?) Tools for “breaking” code (like two way translation).

28. 27. Projects 6: Standard social science Some of you may already have social science interests (or there may already be some descriptive social science in CS that I don’t know about) and the “normal rules” of ABM development then apply.

29. 28. Projects 7: Other languages What I have said may equally apply to other ABM languages (FLAME, RePast, SWARM, MASON) but I’m not well enough qualified to advise you. Don’t back a dying horse! In passing, though, there may well be important “speed up” advantages in particular areas (detailed cellular ABM of cancers for example).

30. 29. Summing up ABM is an empirical research method with a methodology not just a technical gizmo. The descriptive/instrumental issue has the potential to make interdisciplinarity harder in some combinations. There is still plenty to be done with a bit of extra thought.

31. 30. Questions? Comments? Criticisms? Research in certain areas? Omissions?

32. 31. Now read on 1 <https://leicester.academia.edu/EdmundChattoeBrown>. [Including Genetic Programming DPhil thesis.] Chattoe, E. (2006) 'Using Simulation to Develop and Test Functionalist Explanations: A Case Study of Dynamic Church Membership', British Journal of Sociology, 57(3), pp. 379-397. [Discussed example.] Chattoe-Brown, E. (2013) ‘Why Sociology Should Use Agent Based Modelling’, Sociological Research Online, 18(3), <http://www.socresonline.org.uk/18/3/3.html>. [Amplifies many arguments here and deals with critiques of ABM.] Chattoe-Brown, E. (2014) 'Using Agent Based Modelling to Integrate Data on Attitude Change', Sociological Research Online, 19(1), <http://www.socresonline.org.uk/19/1/16.html>. [Discussed example.] Hägerstrand, T. (1965) ‘A Monte Carlo Approach to Diffusion’, Archives Européennes de Sociologie, 6(1), pp. 43-67. [Very early validated and calibrated model.] Gilbert, N. (2007) Agent-Based Models (London: Sage). [A very good short introduction based on an interesting “example” ABM.]

33. 32. Now read on 2 JASSS (Journal of Artificial Societies and Social Simulation), <http://jasss.soc.surrey.ac.uk/JASSS.html>. [Free online journal with lots of examples.] Netlogo Users Group (check also with NL web site) <https://groups.yahoo.com/neo/groups/netlogo-users/conversations/topics/4581?var=1>. simsoc (email discussion group for the social simulation community): <https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=SIMSOC>. ESSA (European Social Simulation Association): <http://www.essa.eu.org>. [Annual conferences, Summer School, ESSA @ Work.] Kravari, K. and Bassiliades, N. (2015) ‘A Survey of Agent Platforms’, Journal of Artificial Societies and Social Simulation, 18(1), <http://jasss.soc.surrey.ac.uk/18/1/11.html> [Will date rapidly but gives more information about the range of languages.]

34. 33. Now read on 3 Edmonds, B. and Hales, D. (2003) ‘Replication, Replication and Replication: Some Hard Lessons from Model Alignment’, Journal of Artificial Societies and Social Simulation, 6(4), <http://jasss.soc.surrey.ac.uk/6/4/11.html> [A good place to start with replication issues.] Polhill, J. G., Izquierdo, L. R. and Gotts, N. M. (2005) ‘The Ghost in the Model (and Other Effects of Floating Point Arithmetic)’, Journal of Artificial Societies and Social Simulation, 8(1), <http://jasss.soc.surrey.ac.uk/8/1/5.html>. [The only “technical programming issue” that has surfaced significantly in ABM to my knowledge.] Galán, J. M., Izquierdo, L. R., Izquierdo, S. S., Santos, J. I., del Olmo, R., López-Paredes, A. and Edmonds, B. (2009) ‘Errors and Artefacts in Agent-Based Modelling’, Journal of Artificial Societies and Social Simulation, 12(1), <http://jasss.soc.surrey.ac.uk/12/1/1.html>. [Kind of what it says on the tin.]