What Problem Does It Solve?: It is hard to find optimal solutions to complex problems and policy disputes. Various parties dig in on particular issues. The parties don’t effectively explore the whole solution space. It is hard for anyone to understand the complex tradeoffs created by interdependency between particular choices.

How Does It Do That?: GenEvPol allows entry of relatively uncontroversial data regarding all the potential elements of a solution, the effects likely to be created by selection of particular elements, the likely impact of those effects on particular parties, and the mutual incompatibility of various alternatives. From this data, the system evolves an optimal starting point for future discussions – finding combinations of decisions or policies that maximize the welfar of all the affected parties in aggregate.
Why Is It Different? : GenEvPol treats alternative decisions and policy choices as GENES. Combinations of such genes (organisms) represent solutions to a dispute or selection of a particular set of policies. GenEvPol starts with a randomly selected population of organisms. Using relatively uncontroversial data regarding the effects of particular choices, GenEvPol scores the organisms and allows the most fit to mate and mutate. The result is the evolution of an optimal set of decisions – a good, neutrally-produced, starting place for further discussion among the parties.

Who Will Use It? : Anyone seeking optimal solutions to complex problems. Legislators. Lawyers counselling clients. Peace negotiators. Business people seeking an optimal multi-party deal.

Other Potential Uses : Anything can be treated as having a set of genes. Its possible that GenEvPol could be used to evolve inventions of all types.

More Detailed Description : GenEvPol

Takes advantage of the fact that, while the resolution of a multi-issue dispute may be controversial, the likely effects of particular possible elements of any solution may not be (because all parties agree that certain decisions will produce particular impacts).
Treats particular decisions or policies as genes that can, conceptually, combine to form complex organisms.
Uses a scoring mechanism and mating and mutation to explore the entire solution space and find the most promising combination of decisions.
Can be run separately by various parties – and may convince all of them that disagreements about particular impacts don’t lead to different optimal solutions.
Avoids the tendency of sequential bargaining to lock in to suboptimal solutions.
Explains the influence of each scoring element in evolving the optimal solution.
Rapidly converges on optimal solutions.
Lead Designer : David R. Johnson

Sponsors: GenEvPol was developed by Graphical Groupware

For More Information: email:;(202) 674-0187