computer simulation and economics

Computer Simulationand Economics

Edmund ChattoeDepartment of Sociology

University of Oxfordedmund.chattoe@sociology.ox.ac.uk

http://www.sociology.ox.ac.uk/people/chattoe.html

Plan of the Talk

• The ideology of simulation• The “agent based” perspective• A case study: pricing under oligopoly• What next?

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

The “Agent Based” Approach IAGENT 1

c1=a1y1

AGENT 2c2=a2y2

CSOC, Y

ECONOMISTC=aY

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.)

The “Agent Based” Approach IIAGENT 1MODEL

AGENT 2MODEL

INSTITUTIONRULES

“SOCIAL”SCIENTIST

REGULARITY

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?”

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

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

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

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?

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!

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

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

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)

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

A Typical GP Price String+

3/

OP1

+ 2

OP2

The Operators• Crossover: Take two trees, identify “legal” cut

points and swop “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

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

Monopoly Learning

Dosi et al. Replication

Cost Plus Pricing

Price Following

Much Lower Unit Costs

Speculative Market: Fixed Unit Cost

Co-ordination Through Salience

Stable But Uncoordinated Market

Expectation Formation Terminals

Tacit Collusion?

Sustainable Market Shares

Three Firms with Expectations

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)

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

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

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

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

The Scope of Models

Labour Markets

Demography

Education

??

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

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

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