Examining the Economic Effect of Various Forms of

Pharmaceutical Intellectual Property

By Alex McGuigan

Purpose/Goal

• Creating an economic simulation that can effectively replicate scenarios regarding intellectual property

• Finding the economic viability of our current pharmaceutical laws

• Looking for other solutions to our intellectual property situation

Scope

• Measuring pharmaceutical IP with economic data from 1980-2000

• Different IP laws– American IP: 20 year patent, heavy FDA

regulation, high legal, heavy healthcare reg• Test reforms to IP laws

– Other countries’ IP– No IP laws

Background• US IP laws are

highly contentious– Companies charge

high prices for drugs• Can take advantage

of inelastic demand• Price the poor in

Third World out of the market

– Third World nations ignore US IP laws

– Companies focus on new, high-tech drugs

• Much debate exists about the issue– Companies claim that

cost of creating drugs leads to prices

– Third World thinks they should be accessible to the poor

– Mostly rhetoric– Research is from a

business or sociological perspective

• Low economic input, almost no economic research

Research• Agent-Based

Modeling– Usually used for

ecological or sociological purposes

– Rarely from an economic perspective

• Sugarscape had a trade component

– More sociological than economic

• Axiomatic in nature

• Economic– Rarely from a

simulation perspective

– Usually uses real world data or axioms

– Research through analyzing existing data

– Agent based modeling can be very useful

Agents

• Vision– Sight– Evaluation of ideas– Profit estimations

• Metabolism– Efficiency – Management– No consumption of

sugar in location

• Sugar– Wealth– Capital

• Work to gain as much sugar as possible

Scapes (Locations)• Locations are ideas

– Locations produce a product if occupied

– Different IP laws for each

• No agents allowed• Agents from one

corporation• Any agents• Different lengths

– Locations have inherent supply/demand

– Shaped to measure different kinds of product markets and innovation

• Pyramid shape of ideas

• Linear shape

– Products will be clustered in nearby locations

– Inherent cost to occupy

– Initial cost to occupy

Corporations• Legal teams

– Conflicts between agents from different companies

• Decentralized control of agents– Control through

providing funding– Control through

firing

• Agents work for them– Hire agents from a

job market for sugar– Agents send portion

of profits back– Provide capital for

agents

• Work to gain most sugar possible– Incentive to

monopolize

Products

• Each product has a supply/demand market– Supply and demand

both have inherent values

– Supply also determined by output of product’s locations

–Prices and wages alter every turn• Based on supply

and demand–IP laws apply to

them to an extent

Lawsuit• Two agents attempt to occupy same

square, square has one company IP laws

• Legal teams match – legal score v. legal score

• Advantage to corporation that owned idea longer, advantage to holding similar ideas

Markets

• Inherent values– Real world data

• Use real world data to measure market ups and downs

–Limited economic estimation by agents and corporations• Imperfect• Based on vision

and estimation

Design

• Adapted MASON version of Sugarscape

• Test by using real-world data

• No randomness inside actual mechanics, account for market irrationality through vision and through using data from periods of irrationality

• Simplicity is the goal

Development

• Adapted MASON version was outdated– Also very large, much of

it extraneous– Hard to learn the ins and

outs of the program

• Implementing groups and markets required additional threading

• Optimization necessary, large cuts made

• Finding economic solutions was difficult– Trouble with mixed CS

and economic concepts

• Real world data presented challenges– Difficult to find a way to

influence agents while remaining decentralized and predetermined

– Hard to find and isolate proper economic data

Testing – Many trials of each

• Randomized– Randomized stats– Randomized

allocation product effectiveness

– Use real world data for agent placement, corporation roster

• Planned– Products will be

roughly placed together

– Products and ideas will be placed in specific layouts

– Use real world data to influence starting sizes, stats, layouts, etc.

Results

• Was not able to gain results for my specific instance

• Results became an economic model that can be applied to many situations, especially intellectual property

• Adapting to other types of intellectual property is merely a matter of changing IP laws in products and locations and using different real world data

Conclusion

• Agent Based Modeling can become a valuable tool for economics, if used in an innovative way

• Applying real world data is extremely difficult, continue working in spare time

• Enjoyed this project and plan on expanding it in the future to other areas of intellectual property and economics