Markov Models of Social Change (Part 1)

Abstraction: Cross-impact balance analysis with stochastic succession rules for social scenarios

Key points:

• Cross-impact balance (CIB) analysis uses expert panels to score how descriptor states (e.g., agricultural policy, temperature) influence each other, building an impact matrix
• Classical CIB finds self-consistent "sink" scenarios and loops via deterministic succession rules; all paths lead to either a cycle or a stable fixed point
• Stochastic succession (dice rule) picks a random descriptor to update each step, eliminating most cycles by allowing divergence at forks
• The Boltzmann succession law assigns transition probabilities proportional to exp(impact score / temperature), allowing rare transitions against expert judgment
• Long-run stationary distributions over fully stochastic models reveal relative importance of scenarios and help identify levers for intervention
• Method by Wolfgang Weimer-Jehle; software: ScenarioWizard from ZIRIUS (University of Stuttgart)

Connections: Alastair Jamieson Lane · Santa Fe Institute · Markov Models · Cross Impact Balance Analysis · Stochastic Processes

Source: http://johncarlosbaez.wordpress.com/2014/02/24/markov-models-of-social-change-part-1/