A presentation at the Open University, Milton Keynes, 2003. The paper presents three different examples of simulation: An agent-based model of adaptive behaviour in oligopoly, a learning model of consumption and lifestyle and a preliminary attempt to model social mobility processes.

1. Computer Simulation
and Economics
Edmund Chattoe
Department of Sociology
University of Oxford
edmund.chattoe@sociology.ox.ac.uk
http://www.sociology.ox.ac.uk/people/chattoe.html

2. Plan of the Talk
• The ideology of simulation.
• The “agent based” perspective.
• A case study: Pricing under oligopoly.
• What next?

3. The Ideology of Simulation
• Agents are “fundamentally” heterogeneous.
• Agents are “really” cognitive but bounded.
• Agents are socially situated.
• Agents adapt in a complex environment of
other agents including organisations.
• There are systematic micro foundations to
macroscopic regularity.
• Environments are profoundly dynamic.

4. The “Agent Based” Approach I
AGENT 1
c1=a1y1
AGENT 2
c2=a2y2
CSO
C, Y
ECONOMIST
C=aY

5. ABA: Implications I
• Aggregability and absence of feedback:
A special case?
• Where do agent models come from?
• What is the “story of a?” (Functional,
adaptive, other).
• How do we find out about agent models?
(Problem of macro to macro inference.)

6. The “Agent Based” Approach II
AGENT 1
MODEL
AGENT 2
MODEL
INSTITUTION
RULES
“SOCIAL”
SCIENTIST
REGULARITY

7. ABA: Implications II
• How do we model if we relax
aggregability and absence of feedback?
• What do we do about organisations?
• Does “good science” needs to link levels to
avoid mere “data mining” ?
• Are we modelling the “right stuff”?
• What do we do about equilibrium?
• Is economic theory “just another model?”

8. Common Concerns
• What is the status of cognitivism?
• Isn’t this all ad hoc? Yes, but ...
• This is all very well but we can’t model it
• We have good “social” processual reasons
to assume regularity in agent models
• But, if we are wrong, we must all pack up
and go home or become novelists

9. Simulation: Provisional Definition
• Computational (rather than verbal or
mathematical) representation of a social
process.
• Descriptive rather than instrumental use.
• An “explicit” representation?
• Fundamental problem is not programming
but adequate data.

10. Example: “Social” Market I
• Economics aggregates to get D, S curves
and then solves for market clearing.
• S and D curves don’t exist in the minds of
buyers or (probably) sellers.
• What exists are inventories, shopping trips,
haggling, gossip …
• When shoppers run out they go shopping.
• While shopping they search and gossip.

11. Example: “Social” Market II
• Shops produce on past sales but sell out.
• Shops may adapt prices through gossip or
direct “observation”.
• Shops and customers may match “bids”
and “asks” to reach agreement.
• S and D curves can be “produced” from
such a simulated market but so can trade
networks: Effective falsification?

12. Example: “Social” Market III
• Firms raise production level if they sell out or
lower it if they have unsold inventory.
• Firms lower price if too many customers walk
away or if they hear/observe too many lower
prices in other shops.
• Firms raise opening bid if nobody walks away.
• Consumers use 1 unit per period and go shopping
if they run out.
• Consumers pass/receive one message per period.
• And so on … this is a programme!

13. Example 1: Oligopoly Pricing
• How do firms set prices in a complex
environment?
• Simplify by making it a game or assuming
lots of common knowledge
• Third approach is adaptive but this is “too
difficult” for simple adaptation
• Possible solution is evolutionary learning:
Firms adapt by trial and error and are
selected.

14. The Appeal of Evolution
• Driven by heterogeneity.
• Open ended: actors don’t need to know
objective function (if there is one).
• Works on minimally effective strategies
using relative success.
• Analogous to situation of firms?
• Observed to produce stable self-organised
heterogeneity in ecosystems.

15. A Brief History
• Marshall and the representative firm.
• Alchian:
– Outcomes not intentions.
– Genotype is firm practices.
– Phenotype is firm behaviour.
• Nelson and Winter:
– Fixed decision rules.
• Dosi et al. (1999)

16. The Dosi et al. Model
• Candidate prices are small set of GP strings.
• Firms set price probabilistically based on
accumulated profits of candidates.
• Demand determined by “market” price and
allocated by current market share.
• Market share updated via set and market price.
• Profits are accrued to firms.
• Firms with losses/minimal market share replaced.
• New candidates may be generated.

17. A Typical GP Price String
+
3/
OP1
+ 2
OP2

18. The Operators
• Crossover: Take two trees, identify “legal” cut
points and swap “tales”.
• Mutation: Take one tree and identify “legal” cut
point for new randomly generated tree.
• Other possibilities.
• Some completely new trees.
• IF NOT AND OR > < = + - % *.
• OMP, OMD, OP x OUC, CUC, OS, integer.
• http://users.ox.ac.uk/~econec/thesis.html

19. Some Results
• Main Dosi et al. result is evolution of price
following and “cost plus” pricing.
• This appears to be sensitive to assumptions made
about large variable unit costs.
• Profit maximisation doesn’t drive out other goals.
• Market share maximisation leads to monopoly.
• Fixed unit cost markets are speculative and can
co-ordinate using “salient” prices.
• Naïve expectations allow co-ordination and tacit
collusion.

20. Monopoly Learning

21. Dosi et al. Replication

22. Cost Plus Pricing

23. Price Following

24. Much Lower Unit Costs

25. Speculative Market: Fixed Unit Cost

26. Co-ordination Through Salience

27. Stable But Uncoordinated Market

28. Expectation Formation Terminals

29. Tacit Collusion?

30. Sustainable Market Shares

31. Three Firms with Expectations

32. What Next?
• Better data collection: Ethnographic,
experimental, participatory.
• More effective sensitivity analysis.
• Much more “joined up” research mediated
by simulation.
• More “middle range” theory provoked by
new approach (dynamic decision).
• More infrastructure (JASSS, CRESS, S3).

33. Example 2: Lifestyle Emergence
• Based on qualitative data about money
management among pensioners.
• Importance of “practices” and “lifestyles”.
• Almost no explicit calculation: An
excellent corrective to economics.
• Abstraction but inductive abstraction.
• Linking sequence/narrative data to
individual choice.

34. Lifestyle Emergence Simulation
• Activity plans (444411122111) and budget
plans (1111000110111).
• Distinguish plan and realisation.
• Adaptive rule for individual comparison of
(largely unobservable) budget plans.
• Adaptive rule for social comparison of
observable (communicated) activity plans.
• Improved wellbeing and emergent
lifestyles.

36. Example 3: Social Mobility
• Paradigmatic statistical (GLR) sociology
linking highly theorised concepts.
• Dilemma with micro/macro link:
– Micro theory must be “anti social” (RCT) to
guarantee transparent aggregation.
– Plausible micro theories have uncertain macro
consequences (Schelling example).
• Simulation as a tool for integration.

37. MOBSIM: Work in Progress
• Microsimulation: agents, attributes and
updating processes (environment).
• Families, schools and jobs/classes.
• Families: demographics and social
practices.
• Schools: “epoints”.
• Jobs: Hiring by epoints, random firing.
• No social networks or “economics” yet.

38. The Scope of Models
Labour Markets
Demography
Education
?
?

39. Distribution of Education Points By Class
0
0.5
1
1.5
2
2.5
0 200 400 600 800 1000 1200
Education Points
Series1

40. Implications of MOBSIM
• Thought provoking surprises:
Identification of lacunae.
• Integration of diverse research.
• Potential falsification using within
generation (labour market surveys),
qualitative biographical and sequence
data.
• Exploring micro/macro relations: Another
possible mode of falsification.

41. The Future?
• Methods: Adapting methods to simulation
– Dynamic process data.
– Ethnographic decision elicitation.
– A sociological protocol for “experimentation”.
• Data: Neglected approaches to sociality
– Adaptive models: Innovation diffusion (drugs).
– Dynamic social networks and endogeneity.
– Time planning and lifestyles as sequences.
– Selectionism: Evolving social practices.

42. Conclusions
• A genuinely novel method of representing
social processes.
• Inspires new developments in methodology
(the agent based approach) and the possible
return of falsifiability.
• Suggests new kinds of theories and
represents existing debates (micro/macro).
• Uses and generates data in novel ways:
Synthetic.