Competitive Innovation and the

Emergence of Technological Epochs

Jeremy Pesner's Excellent Lecture SeriesPart 1

Overview Simulates evolution of economic goods (innovation) Model takes cues from:

Brian Arthur (recombination of existing technologies) Eric Beinhocker (utility of technologies defined on a

fitness landscape)

Mirrors some interesting aspects of technological evolution:

Creative destruction Punctuated disequilibria (innovation waves) Human involvement (lack of technology agency)

Technological Evolution vs Biological Evolution

Technologies “inherit” properties from previous technologies, but don't reproduce

Humans drive technological evolution “Lamarckian” characteristics – Technologies change

their form over time, can “reproduce” in new context Ex: Laser was initially a measurement tool Has “reproduced” as metal cutting tool and disc-

reading mechanism

“Unnatural” selection – What people want, not what they need

Setup

Some number of agents & some number of total goods with randomly distributed fitnesses

Agents can hold and use goods with defined fitnesses. Goods have finite lifetimes

Agents invent a new good by combining 2 existing ones – new fitness value f = [0, g1 + g2]. Some probability that it is marketable

Agents evaluate their own fitness of good = [0, f]. If new good's fitness is greater than least useful good agent owns, will adopt it

Default Parameters Used In Paper (Not in Model)

Size of population (A) – 10000 Max number of goods of each agent (G) – 20 Initial number of goods (N) – 30 Probability of marketability (p) - .0001 Length of time model is run (T) – 1000 Agents are networked in small world network with p

= .01 Can talk to their neighbors about their goods

Goods Don't Last

The fitness landscape at work

Creative Destruction

Technology use is emergent

Adoption of Goods

Which goods are most used? How quickly are goods (un)adopted?

At first blush this figure looks somewhat like a colorful city at night, reflected across icy still water.

Rate of Overall Adoption

Most goods stay with an agent for a long time

Exponential Total (Average) Fitness Growth

Implication: Newer technologies offer increasing amounts of fitness

Deviation From Average

How can most of the goods be below the average?

New classes of technology

Technology fitness reliably declines (relative to average fitness)

Absolute fitness increasesexponentially with every new

technology class

One Good's Evolution

Complex genesis from a wide variety of technologies

Conclusions These trends persist regardless of distinct types

of technology Guns vs butter

Can enable quantification of qualitative ideas Creative destruction – ratio of goods displaced

to total goods Difficult to measure outside of model

Model has technological epochs (innovation waves)

Increasing avg fitness = more capabilities & better standard of living

Adaptive Agent Modeling in a Policy Context

Jeremy Pesner's Excellent Lecture SeriesPart 2

A New Look at Policy Tim Gulden's (CSS prof) dissertation at UMD

People on committee: Thomas Schelling, some guy from Brookings

In 2004, ABMs not as well-known/numerous Had mostly been used to demonstrate social science

concepts But can we actually derive policy implications? Gulden says yes, and proves it (mostly) Looks at 3 different, unconnected cases. ABMs used

differently in each

ABMs: An Evolution in Models Static models: state of a system at single point in time

Price of a house today

Comparative static models: State of a system at multiple points in time

Most economic equations. Tend to make many assumptions

Systems dynamics models Trace evolution of system over time through

differential equations and software

And now, ABMs

Case 1: Ricardo Theory of Comparative Advantage

Theory: All nations have ideal equilibrium of goods they can produce & trade

Even if others nations can produce goods themselves

Gomory & Baumol: Multiple equilibria for trading arrangements

Countries produce what they do because of histories, polices

Industries may complement each other, providing further advantage to a country

A nation can produce too many goods – there should be a certain distribution

Let's see what a model can say about this...

Details of Model* Two nations, developed & developing, with agents

living in each Different employment distributions & production

functions

Barter wine & cloth, with variable exchange rate Agents get paid a certain amount, can adjust Model is robust – Compared to Paul Samuelson under

traditional economic assumptions If technology enables more efficient production for one

country, can lead to extreme disadvantage for other

*I am far from an expert on the economics of international trade

Details of Model, Part II Model has startup costs, increasing returns Agents may (with small prob.) choose to leave their

nation and work in the other When developing nation gets efficiency boost...

...nothing happens Cannot suddenly shift into high-tech industry

When nation closes borders, develops industry When borders reopen, can trade on par with

developed nation

Policy Implications Ricardo Theory: Trade, trade, trade!

Can always trade no matter production arrangements Should always trade, even if bananas

Samuelson: Significant technological advantage can harm other nations

ABM: Ehhhhhh.... Closing borders not an unreasonable idea

economically Technological efficiency is meaningless without

training/infrastructure

Case 2: Zipf Distribution of Cities Within Nation

Zipf distribution: Size of city is proportional to ordinal rank

10th largest city has roughly a tenth the population of largest city

Has defied empirical explanation

Significant exceptions: United States (New York half of what Zipf says it

should be) France (Paris way higher than Zipf says it should be) Russia (nothing matches the Zipf model)

Persuasive Graphs

Jar and Beans Model Jars... with beans in them

Jars face off with each other and “wager” half the beans of the smaller jar

Larger jars at more of an advantage Floor assumption for smallest jars – will not lose last

beans

Zipf distribution among jars is produced No matter initial configuration Cities “churn” and switch places often Doesn't really represent city migration

So Let's Make Some Changes Size of the bet is now a parameter of the model Some growth in smaller jars to offset design of model Some beans will never leave their jars A few other changes to reflect political situations (i.e.

Russia's limits on city migration) Cities still grow and change places unrealistically Policy implications: In countries with fewer cities and

more people, people will concentrate in “megacities” Management of middle-tier cities, broad ideas for

urban policy

Jar & Beans Graph

Case 3: Guatemalan Civil Violence 1977-1986

Different than the previous two – this is more micro Also far, far more specific Data collected by AAAS and CIIDH Analyzed, compared to existing ABM of civil

disobedience Epstein, Steinbruner & Parker (2001) at Brookings Citizens & Cops: Citizens have parameters which

can incite them to violence Cops will arrest one random citizen within their view

per turn Can be broken into red and blue groups

Guatemalan Data Intensity and frequency of violence weakly correlated Killings most prevalent in area with high percentages

of Mayan and Ladino ethnic groups A large spike in monthly killings in 1982

A punctuated disequilibrium

Killings in both conflicts and genocide are Zipf distributed

Cause not clear, likely not the same as Zipf distribution from before

Comparison of Data to Model Brookings – Elimination of leaders = effective

repression technique Logic for weak correlation between frequency and

intensity of violence

Violence spikes in model Consistent with punctuated disequilibrium from data

Model does not produce Zipf-distributions of violence, but does produce heavy-tailed distribution

When red and blue are adversarial, there is “ethnic cleansing”

Conclusions Demonstrates insights that ABMs can generate into

policy matters In first two, enables an understanding between inputs

and outputs In third, suggests that models are in fact useful for

the subject

ABMs allow for history, bounded rationality, etc. A mix of a quantitative and qualitative perspective

Actual policy derivations not given much attention Three cases and ABM applicability very different &

disconnected